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With the increasing trend towards informatization and intelligence in modern warfare, high-intensity and continuous reconnaissance activities are becoming increasingly common in battlefield environments via airborne, vehicle, UAV, satellite and other platforms. Visible and infrared images are preferred due to their high resolution, strong contrast, rich texture details and color features, and strong information expression ability. However, the quality of imaging is easily affected by environmental factors, making it crucial to quickly and accurately filter useful information from massive image data. To this end, super-resolution image preprocessing can improve the detection performance of UAV, and reduce false detection and missed detection of targets. Additionally, super-resolution reconstruction results in high-quality images that can be used to expand UAV datasets and enhance the UAV characteristics, thereby enabling the enhancement of small targets. In response to the challenge of "low-slow small" UAV targets at long distances, we propose a multi-scale fusion super-resolution reconstruction (MFSRCNN) algorithm based on the fast super-resolution reconstruction (FSRCNN) algorithm and multi-scale fusion. Our experiments confirm the feasibility of the algorithm in reconstructing detailed information of the UAV target. On average, the MFSRCNN reconstruction time is 0.028 s, with the average confidence before and after reconstruction being 80.73% and 86.59%, respectively, resulting in an average increase of 6.72%. [ABSTRACT FROM AUTHOR]

The Graphic Interchange Format (GIF) is a bitmap picture format that has a series of perpetually repeating images or silent movies that may be viewed without the user having to click and start them. GIFs are frequently used to visually represent emotions that are expressed through body language such as gestures, movements, and facial expressions. Computing may be used to recognise thoughts and other emotions like desire, interest, sentiments, etc. by using emotional expressions or movements as face markers or properties in GIFs. The ability to predict emotions in GIFs may make it easier to express oneself on social media and convey a person's attitude or personality. Emotion detection in GIFs may be utilised for a range of purposes, e.g., developing a recommendation system, detecting inappropriate content, sentiment identification from GIF-induced sentiment as perceived by person and creating a GIF tag generating system. This study discusses the prior contributions made towards emotion identification in GIFs and describes a method for detecting seven different emotion classes (Happy, Anger, Sad, Surprise, Disgust, Fear, and Neutral) in GIFs by combining an activity recognition network with face emotional expression. The suggested deep neural network, RNN, LSTM approach produced an F1-score of 0.89 and an accuracy of 88 percent. [ABSTRACT FROM AUTHOR]

Most hotel recommendation systems currently rely on text-based information or meta-data. We develop a deep network recommendation model with three modalities – picture, review, and scoring.We propose a unifified deep neural network including an embedding layer, pooling layer, and fully connected layer. Comparing with other algorithms, we verify its efficacy in improving travel recommendations based on the hotel data crawled from Ctrip and the major evaluation indicators. Our study contributes to the literature by building a knowledge model for tourist hotels based on the analysis of user-generated data and providing practical guidance for hotel managers and users. [ABSTRACT FROM AUTHOR]

The target review has been proven to be able to predict the target user's rating of the target item. However, in practice, it is difficult to obtain the target review promptly. In addition, the target review and the rating may sometimes be inconsistent (such as preference reviews and low ratings). There is currently a lack of research on the above issues. Therefore, this paper proposed a Recommendation algorithm that Simulates the generation of target review semantics and fuses the ID Information (RSII). Specifically, based on the characteristics of the target review available during the model training, this paper designed a teacher module and a review semantics learning module. The teacher module learned the semantics of the target review and guided the review semantics learning model to learn these semantics. Then, this study used the fusion module to dynamically fuse the target review semantics and the ID information, enriching the representation of predictive features, thereby, alleviating the problem of inconsistency between the target review and the rating. Finally, the RSII model was extensively tested on three public datasets. The results showed that compared with seven of the latest and most advanced models, the RSII model improved the MSE metric by 8.81% and the MAE metric by 10.29%. [ABSTRACT FROM AUTHOR]

The emerging topic of sequential recommender (SR) has attracted increasing attention in recent years, which focuses on understanding and learning the sequential dependencies of user behaviors hidden in the user-item interactions. Previous methods focus on capturing the point-wise sequential dependencies with considering the time evenly spaced. However, in the real world, the time and semantic irregularities are hidden in the user's successive actions. Meanwhile, with the tremendous increase of users and items, the hardness of modeling user interests from spare explicit feedback. To this end, we seek to explore the influence of item-aspect reviews sequence with varied time intervals on sequential modeling. We present RTiSR, a review-driven time interval-aware sequential recommendation framework, to predict the user's next purchase item by jointly modeling the sequence dependencies from aspect-aware reviews. The main idea is twofold: (1) explicitly learning user and item representation from reviews by assigning different weights, and (2) leveraging a hybrid neural network to capture the collective sequence patterns with a flexible order from aspect-aware review sequences. We conduct extensive experiments on industrial datasets to evaluate the effectiveness of RTiSR. Experimental results demonstrate the superior performance of RTiSR in different evaluation metrics, compared to the state-of-the-art competitors. [ABSTRACT FROM AUTHOR]

The internet and social networks produce an increasing amount of data. There is a serious necessity for a recommendation system because exploring through the huge collection is time-consuming and difficult. In this study, a multi-modal classifier is introduced which makes use of the output from dual deep neural networks: GRU for text analysis and Faster R-CNN for image analysis. These two networks reduce overall complexity with minimal computational time while retaining accuracy. More precisely, the GRU network is utilized to process movie reviews and the Faster RCNN is used to recognize each frames of the movie trailers. Gated Recurrent Unit (GRU) is a well-known variety of RNN that computes sequential data across recurrent structures. Faster RCNN is an enhanced version of Fast RCNN, it combines with the rectangular region proposals and with the features is extract by the ResNet-101. Initially, the trailer of the movie is manually splitted into frames and these frames are pre-processed using fuzzy elliptical filter for image analysis and the movie reviews are also tokenized for text analysis. The pre-processed text is taken as an input for GRU to classify offensive and non-offensive movies and the pre-processed images are taken as an input for Faster R-CNN to classify violence and non- violence movies based on the extracted features from the movie trailer. Afterwards, the four classified outputs are given as input for fuzzy decision-making unit for recommending best movies based on the Mamdani fuzzy inference system with gauss membership functions. The performance of the dual deep neural networks was evaluated using the specific parameters like specificity, precision, recall, accuracy and F1 score measures. The proposed GRU yields accuracy range of 97.73% for reviews and FRCNN yields the accuracy range of 98.42% for movie trailer. [ABSTRACT FROM AUTHOR]

In modern days, making recommendation for news articles poses a great challenge due to vast amount of online information. However, providing personalized recommendations from news articles, which are the sources of condense textual information is not a trivial task. A recommendation system needs to understand both the textual information of a news article, and the user contexts in terms of long-term and temporary preferences via the user's historic records. Unfortunately, many existing methods do not possess the capability to meet such need. In this work, we propose a neural deep news recommendation model called CupMar, that not only is able to learn the user-profile representation in different contexts, but also is able to leverage the multi-aspects properties of a news article to provide accurate, personalized news recommendations to users. The main components of our CupMar approach include the News Encoder and the User-Profile Encoder. Specifically, the News Encoder uses multiple properties such as news category, knowledge entity, title and body content with advanced neural network layers to derive informative news representation, while the User-Profile Encoder looks through a user's browsed news, infers both of her long-term and recent preference contexts to encode a user representation, and finds the most relevant candidate news for her. We evaluate our CupMar model with extensive experiments on the popular Microsoft News Dataset (MIND), and demonstrate the strong performance of our approach. [ABSTRACT FROM AUTHOR]

Recommender systems for requirements are typically built on the assumption that similar requirements can be used as proxies to retrieve similar software. When a stakeholder proposes a new requirement, natural language processing (NLP)-based similarity metrics can be exploited to retrieve existing requirements, and in turn, identify previously developed code. Several NLP approaches for similarity computation between requirements are available. However, there is little empirical evidence on their effectiveness for code retrieval. This study compares different NLP approaches, from lexical ones to semantic, deep-learning techniques, and correlates the similarity among requirements with the similarity of their associated software. The evaluation is conducted on real-world requirements from two industrial projects from a railway company. Specifically, the most similar pairs of requirements across two industrial projects are automatically identified using six language models. Then, the trace links between requirements and software are used to identify the software pairs associated with each requirements pair. The software similarity between pairs is then automatically computed with JPLag. Finally, the correlation between requirements similarity and software similarity is evaluated to see which language model shows the highest correlation and is thus more appropriate for code retrieval. In addition, we perform a focus group with members of the company to collect qualitative data. Results show a moderately positive correlation between requirements similarity and software similarity, with the pre-trained deep learning-based BERT language model with preprocessing outperforming the other models. Practitioners confirm that requirements similarity is generally regarded as a proxy for software similarity. However, they also highlight that additional aspect comes into play when deciding software reuse, e.g., domain/project knowledge, information coming from test cases, and trace links. Our work is among the first ones to explore the relationship between requirements and software similarity from a quantitative and qualitative standpoint. This can be useful not only in recommender systems but also in other requirements engineering tasks in which similarity computation is relevant, such as tracing and change impact analysis. [ABSTRACT FROM AUTHOR]

Click-through rate (CTR) prediction is a research point for measuring recommendation systems and calculating AD traffic. Existing studies have proved that deep learning performs very well in prediction tasks, but most of the existing studies are based on deterministic models, and there is a big gap in capturing uncertainty. Modeling uncertainty is a major challenge when using machine learning solutions to solve real-world problems in various domains. In order to quantify the uncertainty of the model and achieve accurate and reliable prediction results. This paper designs a CTR prediction framework combining feature selection and feature interaction. In this framework, a CTR prediction model based on Bayesian deep learning is proposed to quantify the uncertainty in the prediction model. On the squeeze network and DNN parallel prediction model framework, the approximate posterior parameter distribution of the model is obtained using the Monte Carlo dropout, and obtains the integrated prediction results. Epistemic and aleatoric uncertainty are defined and adopt information entropy to calculate the sum of the two kinds of uncertainties. Epistemic uncertainty could be measured by mutual information. Experimental results show that the model proposed is superior to other models in terms of prediction performance and has the ability to quantify uncertainty. [ABSTRACT FROM AUTHOR]

Employers couldn't find relevant candidates without great effort, and job seekers must spend a long time researching the right jobs. To fix this issue, recommender systems are an effective way to make talent and job research more effective and easier to complete. It is possible to analyze job offers and candidate profiles and make pertinent recommendations using techniques like the Recurrent Neural Network (RNN) approach. To accomplish this, we propose a model based on the classification of job profiles using a variant of RNN called LSTM ("Long short-term memory"). It is a long-term memory model based on a deep learning algorithm. To maintain the semantic meaning, first, the input of the LSTM model is transformed into a set of vectors using the doc2Vec method, which transforms a document into a vector of numbers. [ABSTRACT FROM AUTHOR]

Rating prediction is an important issue in recommender systems, and its accuracy affects the experience of the user and the revenue of the company. Traditional recommender systems use Factorization Machines for rating predictions and each feature is selected with the same weight. Thus, there are problems with inaccurate ratings and limited data representation. This study proposes a deep recommendation model based on self-attention Factorization (SAFMR) to solve these problems. Thismodel uses Convolutional Neural Networks to extract features from user and item reviews. The obtained features are fed into self-attention mechanism Factorization Machines, where the self-attention network automatically learns the dependencies of the features and distinguishes the weights of the different features, thereby reducing the prediction error. The model was experimentally evaluated using six classes of dataset. We compared MSE, NDCG and time for several real datasets. The experiment demonstrated that the SAFMR model achieved excellent rating prediction results and recommendation correlations, thereby verifying the effectiveness of the model. [ABSTRACT FROM AUTHOR]

With the development of Internet big data and e-commerce, the widespread popularity of information, information acquisition and personalized recommendation technologies have attracted extensive attention. The core value of personalized recommendation is to provide more accurate content and services around users. The recommended scenarios are not uniform, and different dimensions need to be considered. For example, we are facing enterprises or individuals, different age groups, different levels of education, social life and other aspects. In this paper, the classic DNN (Deep Neural Networks) double tower recommendation algorithm in the recommendation algorithm is used as the ranking algorithm of the recommendation system. It is divided into user and item for embedding respectively. The network model is built using tensorflow. The data processed by the initial data through feature engineering is sent into the model for training, and the trained DNN double tower model is obtained. Recall adopts collaborative filtering algorithm, and applies tfidf, w2v, etc. to process feature engineering, so as to better improve the accuracy of the system and balance the EE problem of the recommendation system. The recommendation module of this system is divided into data cleaning as a whole. Feature engineering includes the establishment of user portraits, the analysis of multiple recall and sorting algorithms, the adoption of multiple recall mode, and the implementation of a classic recommendation system with in-depth learning. This makes the recommendation system better balance the interests of both the platform and users, and achieve a win-win situation. [ABSTRACT FROM AUTHOR]

The content-based filtering technique has been used effectively in a variety of Recommender Systems (RS). The user explicitly or implicitly provides data in the Content-Based Recommender System. The system collects this data and creates a profile for all the users, and the recommendation is generated by the user profile. The recommendation generated via content-based filtering is provided by observing just a single user's profile. The primary objective of this RS is to recommend a list of movies based on the user's preferences. A content-based movie recommendation model is proposed in this research, which recommends movies based on the user's profile from the Facebook platform. The recommendation system is built with a hybrid model that combines the Monarch Butterfly Optimization (MBO) with the Deep Belief Network (DBN). For feature selection, the MBO is utilized, while DBN is used for classification. The datasets used in the experiment are collected from Facebook and MovieLens. The dataset features are evaluated for performance evaluation to validate if data with various attributes can solve the matching recommendations. Each file is compared with features that prove the features will support movie recommendations. The proposed model's mean absolute error (MAE) and root-mean-square error (RMSE) values are 0.716 and 0.915, and its precision and recall are 97.35 and 96.60 percent, respectively. Extensive tests have demonstrated the advantages of the proposed method in terms of MAE, RMSE, Precision, and Recall compared to state-of-the-art algorithms such as Fuzzy C-means with Bat algorithm (FCM-BAT), Collaborative filtering with k-NN and the normalized discounted cumulative gain method (CF-kNN+NDCG), User profile correlation-based similarity (UPCSim), and Deep Autoencoder. [ABSTRACT FROM AUTHOR]

Knowledge recommendation plays a crucial role in online learning platforms. It aims to optimize the service quality so as to improve users' learning efficiency and outcomes. Existing approaches generally leverage RNN-based methods in combination with attention mechanisms to learn user preference. There is a lack of in-depth understanding of users' knowledge-level changes over time and the impact of knowledge item categories on recommendation performance. To this end, we propose the knowledge-level-evolution and category-aware personalized knowledge recommendation (KLECA) model. The model firstly leverages bidirectional GRU and the time adjustment function to understand users' learning evolution by analyzing their learning trajectory data. Secondly, it considers the effect of item categories and descriptive information and enhances the accuracy of knowledge recommendation by introducing a cross-head decorrelation module to capture the information of knowledge items based on a multi-head attention mechanism. In addition, a personalized attention mechanism and gated function are introduced to grab the relationship between items, item categories and user learning trajectory to strengthen the representation of information. Through extensive experiments on real-world data collected from an online learning platform, the proposed approach has been shown to significantly outperform other approaches. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many deep learning-based recommender systems have been proposed recently. Where they involve all the users in datasets to build the latent representation of input data to be used later for predicting the missing rates. Despite the fact that, users have different interests, and these differences reduce the model prediction accuracy. This paper proposed a novel cluster-based denoising autoencoder model (cluster-based DAE) for rate prediction recommender systems. Instead of building a single model, it builds K models by using k-means algorithm to divide the users into groups based on their preferences. Each group trains a DAE model to generate recommendations for its members. The proposed method was trained and tested with MovieLens (100K, 1M, and 10M) datasets where 80% of the data are used for training and 20% for testing. The performance of the proposed method compared against other state-of-the-art methods that use deep learning to build rate prediction models. It outperformed the other compared methods in term of mean absolute error (with 12.9%, 14.7%, and 22.3%) and root mean squared error (with 24.2%, 18%, and 21.1%) using MovieLens 100K, 1M, and 10M datasets respectively. [ABSTRACT FROM AUTHOR]

In the current technological scenario of artificial intelligence growth, especially using machine learning, large datasets are necessary. Recommender systems appear with increasing frequency with different techniques for information filtering. Few large wine datasets are available for use with wine recommender systems. This work presents X-Wines, a new and consistent wine dataset containing 100,000 instances and 21 million real evaluations carried out by users. Data were collected on the open Web in 2022 and pre-processed for wider free use. They refer to the scale 1–5 ratings carried out over a period of 10 years (2012–2021) for wines produced in 62 different countries. A demonstration of some applications using X-Wines in the scope of recommender systems with deep learning algorithms is also presented. [ABSTRACT FROM AUTHOR]

Internet applications such as Online Social Networking and Electronic commerce are becoming incredibly common, resulting in more content being available. Recommender systems (RS) assist users in identifying appropriate information out of a large pool of options. In today's internet applications, RS are extremely important. To increase user satisfaction, this type of system supports personalized recommendations based on a massive volume of data. These suggestions assist clients in selecting products, while concerns can increase product utilization. We discovered that much research work is going on in the field of recommendation and that there are some effective systems out there. In the context of social information, sentimental analysis (SA) can aid in increasing the knowledge of a user's behaviour, views, and reactions, which is helpful for incorporating into RS to improve recommendation accuracy. RS has been found to resolve information overload challenges in information retrieval, but they still have issues with cold-start and data sparsity. SA, on the other hand, is well-known for interpreting text and conveying user choices. It's frequently used to assist E-Commerce in tracking customer feedback on their products and attempting to comprehend customer needs and preferences. To improve the accuracy and correctness of any RS, this paper proposes a recommendation model based on a Hybrid Recommendation Model (HRM) and hybrid SA. In the proposed method, we first generate a preliminary recommendation list using a HRM. To generate the final recommendation list, the HRM with SA is used. In terms of various evaluation criteria, the HRM with SA outperforms traditional models. [ABSTRACT FROM AUTHOR]

Research into session-based recommendation systems (SBSR) has attracted a lot of attention, but each study focuses on a specific class of methods. This work examines and evaluates a large range of methods, from simpler statistical co-occurrence methods to embeddings and SotA deep learning methods. This paper analyzes theoretical and practical issues in developing and evaluating methods for SBSR in e-commerce applications, where user profiles and purchase data do not exist. The major tasks of SBRS are reviewed and studied, namely: prediction of next-item, next-basket and purchase intent. For physical retail shopping where no information about the current session exists, we treat the previous baskets purchased by the user as previous sessions drawn from a loyalty system. Mobile application scenarios such as push notifications and calling tune recommendations are also presented. Recommender models using graphs, embeddings and deep learning methods are studied and evaluated in all SBRS tasks using different datasets. Our work contributes a number of very interesting findings. Among all tested models, LSTMs consistently outperform other methods of SBRS in all tasks. They can be applied directly because they do not need significant fine-tuning. Additionally, they naturally model the dynamic browsing that happens in e-commerce web applications. On the other hand, another important finding of our work is that graph-based methods can be a good compromise between effectiveness and efficiency. Another important conclusion is that a "temporal locality principle" holds, implying that more recent behavior is better suited for prediction. In order to evaluate these systems further in realistic environments, several session-based recommender methods were integrated into an e-shop and an A/B testing method was applied. The results of this A/B testing are in line with the experimental results, which represents another important contribution of this paper. Finally, important parameters such as efficiency, application of business rules, re-ranking issues, and the utilization of hybrid methods are also considered and tested, providing comprehensive useful insights into SBRS and facilitating the transferability of this research work to other domains and recommendation scenarios. [ABSTRACT FROM AUTHOR]

Face detection is an important problem in computer vision because it enables a wide range of applications, such as facial recognition and an analysis of human behavior. The problem is challenging because of the large variations in facial appearance across different individuals and lighting and pose conditions. One way to detect faces is to utilize a highly advanced face detection method, such as RetinaFace or YOLOv7, which uses deep learning techniques to achieve high accuracy in various datasets. However, even the best face detectors can produce false positives, which can lead to incorrect or unreliable results. In this paper, we propose a method for reducing false positives in face detection by using information from a depth map. A depth map is a two-dimensional representation of the distance of objects in an image from the camera. By using the depth information, the proposed method is able to better differentiate between true faces and false positives. The method proposed by the authors is tested on a dataset of 549 images, which includes 614 upright frontal faces. The outcomes of the evaluation demonstrate that the method effectively minimizes false positives without compromising the overall detection rate. These findings suggest that incorporating depth information can enhance the accuracy of face detection. [ABSTRACT FROM AUTHOR]

In the field of Data science and online world, Recommendation Systems (RS) play an important role among the various e-commerce applications. Data sparsity often leads to the problem of precise recommendations in RS as there will be either less number of users or ratings. Collaborative filtering (CF) is one of the key techniques that are used for the RS with the pre-requisite of the adequate information of the users and items. Deep Learning (DL) models haved paved the way for analysis and prediction of sequential textual information in various applications. Hence, CF combined with DL approaches are also being explored to solve the problem of data sparsity in RS with various challenges of analysis and prediction of the sequential information. This paper considers the problem of data sparsity with a novel neural CF based DeepLSGR model to provide better recommendations. It is a bi-directional model composed of stacked hidden layers with Long-short term memory (LSTM) and Gated recurrent unit (GRU) and provide recommendations based on the prediction of rating using the textual reviews from the users. It provided an accuracy of 97%, recall of 61% and RMSE of 0.87 for the experiments conducted on the Amazon Fine Food Reviews and OpinRank datasets. The results of the comparison with the existing works evidently demonstrate that the DeepLSGR provides improved recommendations. [ABSTRACT FROM AUTHOR]

The rapid and ubiquitous digital revolution has led to acceleration towards a digitally connected world where accepting recommendations digitally has become a part of our e-commerce related lifestyle. Most of these recommendations are based on rating values, wherein some values are fraudulent. This is a vulnerability in the security of the system and thus hampers its credibility and trust. It is necessary to identify the fraudulent ratings in real-time and build a trustworthy black-box recommender system. This paper proposes hyper-tuned Restricted Boltzmann Machines to regenerate tabular data models using contrastive divergence learning procedures to enhance accuracy, transparency, and recommendations' ability. Most of the existing recommender systems cannot handle the large dataset and deteriorate their performance. Experiments over the Movies Lens,Film Trust and Netflix rating dataset demonstrate that the tuned RBMs performed well compared with SVD, SVD ++, trust SVD, and Stack Auto Encoder(SAE) models with a moderate increase in computational complexity. We build a trustworthy recommender system based on hyper-tuned RBM, a deep learning-based system widely used by E-commerce companies to recommend products. Netflix recommends television shows and movies depending upon the number of customers who have seen a similar genre. Similarly, Amazon recommends an item to a customer based on whether other consumers would be interested in purchasing the product. Through rigorous evaluations, we have shown that our proposed scheme outperforms the existing recommender systems. [ABSTRACT FROM AUTHOR]

A multiarmed bandit approach that uses a deep neural network to decide personalized recommendations for display in the homepage of an online retailer. Nowadays, websites use a variety of recommendation systems to decide the content to display to their visitors. In this work, we use a multiarmed bandit approach to dynamically select the combination of house ads to exhibit to a heterogeneous set of customers visiting the website of a large retailer. House ads correspond to promotional information displayed on the website to highlight some specific products and are an important marketing tool for online retailers. As the number of clicks they receive not only depends on their own attractiveness but also on how attractive are other products displayed around them, we decide about complete collections of ads that capture those interactions. Moreover, as ads can wear out, in our recommendations we allow for nonstationary rewards. Furthermore, considering the sparsity of customer-level information, we embed a deep neural network to provide personalized recommendations within a bandit scheme. We tested our methods in controlled experiments where we compared them against decisions made by an experienced team of managers and the recommendations of a variety of other bandit policies. Our results show a more active exploration of the decision space and a significant increment in click-through and add-to-cart rates. History: Olivier Toubia served as the senior editor and Hema Yoganarasimhan served as associate editor for this article. Funding: This work was supported by the Institute for Research in Market Imperfections and Public Policy [Grant ICM IS130002]. M. Schiappacasse gratefully acknowledges partial funding by the Institute for Research in Market Imperfections and Public Policy (IS130002 ANID). Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mksc.2022.1378. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Henan Agricultural Sciences is the property of Editorial Board of Journal of Henan Agricultural Sciences and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Users purchase goods to form a user-item interaction graph, where goods are usually displayed in multiple modes. The main role of the recommendation system is to obtain users' preferences after analyzing users' purchasing behavior. However, there are some deviations in the interaction between users and items. For example, users buy goods they are not interested in, which directly affects the user's preference analysis. Most existing models do not focus on analyzing and correcting user preference errors in the user-item relationship graph, which leads to errors. There is little work on the multimodal information of commodities, resulting in the loss of information. In this paper, a preference-corrected multimodal graph convolution recommendation network (PMGCRN) is proposed to provide multimodal recommendation services for users. First, a multichannel network is designed to obtain user preference information under different modes. Then, a positive attention mechanism is proposed to deal with the implicit noise edges that do not match users' interactions in the user-item graph to correct errors in user preferences. Additionally, the simplified graph convolution network acts on the structural information of the user-item bipartite graph as an additional channel to enhance the robustness of the model. Finally, a self-attention mechanism and layer-by-layer superposition are applied to obtain multilevel modal and structural information, respectively, so that valuable information can be obtained by the fusion matrix. Experiments show that our proposed PMGCRN outperforms other baselines on all three datasets, MovieLens, Amazon - Sports and Outdoors, and Douban. [ABSTRACT FROM AUTHOR]

Intelligent learning diagnosis is a critical engine of intelligent tutoring systems, which aims to estimate learners' current knowledge mastery status and predict their future learning performance. The significant challenge with traditional learning diagnosis methods is the inability to balance diagnostic accuracy and interpretability. Although the existing psychometric-based learning diagnosis methods provide some domain interpretation through cognitive parameters, they have insufficient modeling capability with a shallow structure for large-scale learning data. While the deep learning-based learning diagnosis methods have improved the accuracy of learning performance prediction, their inherent black-box properties lead to a lack of interpretability, making their results untrustworthy for educational applications. To settle the abovementioned problem, the proposed unified interpretable intelligent learning diagnosis framework, which benefits from the powerful representation learning ability of deep learning and the interpretability of psychometrics, achieves a better performance of learning prediction and provides interpretability from three aspects: cognitive parameters, learner-resource response network, and weights of self-attention mechanism. Within the proposed framework, this paper presents a two-channel learning diagnosis mechanism LDM-ID as well as a three-channel learning diagnosis mechanism LDM-HMI. Experiments on two real-world datasets and a simulation dataset show that our method has higher accuracy in predicting learners' performances compared with the state-of-the-art models and can provide valuable educational interpretability for applications such as precise learning resource recommendation and personalized learning tutoring in intelligent tutoring systems. [ABSTRACT FROM AUTHOR]

Recommendation systems, the best way to deal with information overload, are widely utilized to provide users with personalized content and services with high efficiency. Many recommendation algorithms have been researched and deployed extensively in various e-commerce applications, including the movie streaming services over the last decade. However, sparse data cold-start problems are often encountered in many movie recommendation systems. In this paper, we reported a personalized multimodal movie recommendation system based on multimodal data analysis and deep learning. The real-world MovieLens datasets were selected to test the effectiveness of our new recommendation algorithm. With the input information, the hidden features of the movies and the users were mined using deep learning to build a deep-learning network algorithm model for training to further predict movie scores. With a learning rate of 0.001, the root mean squared error (RMSE) scores achieved 0.9908 and 0.9096 for test sets of MovieLens 100 K and 1 M datasets, respectively. The scoring prediction results show improved accuracy after incorporating the potential features and connections in multimodal data with deep-learning technology. Compared with the traditional collaborative filtering algorithms, such as user-based collaborative filtering (User-CF), item-based content-based filtering (Item-CF), and singular-value decomposition (SVD) approaches, the multimodal movie recommendation system using deep learning could provide better personalized recommendation results. Meanwhile, the sparse data problem was alleviated to a certain degree. We suggest that the recommendation system can be improved through the combination of the deep-learning technology and the multimodal data analysis. [ABSTRACT FROM AUTHOR]

Air quality plays a vital role in people's health, and air quality forecasting can assist in decision making for government planning and sustainable development. In contrast, it is challenging to multi-step forecast accurately due to its complex and nonlinear caused by both temporal and spatial dimensions. Deep models, with their ability to model strong nonlinearities, have become the primary methods for air quality forecasting. However, because of the lack of mechanism-based analysis, uninterpretability forecasting makes decisions risky, especially when the government makes decisions. This paper proposes an interpretable variational Bayesian deep learning model with information self-screening for PM2.5 forecasting. Firstly, based on factors related to PM2.5 concentration, e.g., temperature, humidity, wind speed, spatial distribution, etc., an interpretable multivariate data screening structure for PM2.5 forecasting was established to catch as much helpful information as possible. Secondly, the self-screening layer was implanted in the deep learning network to optimize the selection of input variables. Further, following implantation of the screening layer, a variational Bayesian gated recurrent unit (GRU) network was constructed to overcome the complex distribution of PM2.5 and achieve accurate multi-step forecasting. The high accuracy of the proposed method is verified by PM2.5 data in Beijing, China, which provides an effective way, with multiple factors for PM2.5 forecasting determined using deep learning technology. [ABSTRACT FROM AUTHOR]

Commercial e-learning platforms have to overcome the challenge of resource overload and find the most suitable material for educators using a recommendation system (RS) when an exponential increase occurs in the amount of available online educational resources. Therefore, we propose a novel DNN method that combines synchronous sequences and heterogeneous features to more accurately generate candidates in e-learning platforms that face an exponential increase in the number of available online educational courses and learners. Mitigating the learners' cold-start problem was also taken into consideration during the modeling. Grouping learners in the first phase, and combining sequence and heterogeneous data as embeddings into recommendations using deep neural networks, are the main concepts of the proposed approach. Empirical results confirmed the proposed solution's potential. In particular, the precision rates were equal to 0.626 and 0.492 in the cases of Top-1 and Top-5 courses, respectively. Learners' cold-start errors were 0.618 and 0.697 for 25 and 50 new learners. [ABSTRACT FROM AUTHOR]

Video recommendation has become a crucial role in mitigating the semantic gap in recommending the video based on visual features. This article proposed the exploitation of low-level visual features extracted from videos, and the input data to generate relevant recommendations. Initially, the video is pre-processed with Motion Adaptive Gaussian Denoising Filtering, which eliminates noise from video frames and achieves improved efficiency with high quality and video resolution, which requires less computation. After pre-processing, the paper proposed a content-based extraction approach to retrieve the temporal and spatial characteristics. The temporal characteristics represent the dynamic viewpoints of video, including average shot time and object movement, while the spatial characteristics illustrate a static effect, such as colour and lighting key. Subsequently, this utilizes a series of representative visual features to make the video content more accurate. Finally, the work incorporates a deep neural network to predict the video according to the input and the features extracted. The supervised learning algorithm Multilayer feed-forward is therefore proposed, which generated a series of outputs from a given input set (input data and the extracted features). The majority of deep learning solutions deliver deterministic outcomes and do not measure or monitor prediction variance, which can contribute to a loss of faith in automatic evaluation. Subsequently, Monte Carlo's uncertainty techniques are used to estimate the exact video in accordance with the recommendation. The proposed method is implemented using MATLAB r2020a software with less computation time of 0.999 s and the performance of the proposed method is compared with the different existing methods like MMM, LP-LGSN, and CDPRec. Consequently, the proposed method produces higher performance in terms of precision, recall, F-measures, and nDCG and it produces higher accuracy of 0.94%, respectively. [ABSTRACT FROM AUTHOR]

Recommender system or recommendation system is becoming an increasingly important technology on e-commerce websites to help users find products that suit their preferences. However, the growing number of users and products makes finding the right product difficult. Therefore, this study aims to develop a recommender system on e-commerce websites using collaborative filtering and deep learning techniques. Collaborative filtering is used to find similarities between users based on their preferences, while deep learning is used to improve the performance of the recommender system in generating more accurate recommendations. The test method is carried out by comparing the performance of the recommender system developed with the recommender system that already exists on the e-commerce website. The results of the test show that the recommender system developed is able to provide recommendations that are more accurate and more in line with user preferences compared to the existing recommender system. [ABSTRACT FROM AUTHOR]

Nowadays, review systems have been developed with social media Recommendation systems (RS). Although research on RS social media is increasing year by year, the comprehensive literature review and classification of this RS research is limited and needs to be improved. The previous method did not find any user reviews within a time, so it gets poor accuracy and doesn't filter the irrelevant comments efficiently. The Recursive Neural Network-based Trust Recommender System (RNN-TRS) is proposed to overcome this method's problem. So it is efficient to analyse the trust comment and remove the irrelevant sentence appropriately. The first step is to collect the data based on the transactional reviews of social media. The second step is pre-processing using Imbalanced Collaborative Filtering (ICF) to remove the null values from the dataset. Extract the features from the pre-processing step using the Maximum Support Grade Scale (MSGS) to extract the maximum number of scaling features in the dataset and grade the weights (length, count, etc.). In the Extracting features for Training and testing method before that in the feature weights evaluating the softmax activation function for calculating the average weights of the features. Finally, In the classification method, the Recursive Neural Network-based Trust Recommender System (RNN-TRS) for User reviews based on the Positive and negative scores is analysed by the system. The simulation results improve the predicting accuracy and reduce time complexity better than previous methods. [ABSTRACT FROM AUTHOR]

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A recommendation system is a refinement system that uses massive amounts of data to forecast and present user-preferred products. We employ web log files, including previously searched data, and browsing history, and transmit it to a SoftMax model in our recommendation model. We also use this data to perform user behaviour analysis using the k means clustering algorithm. Furthermore, we transfer users' feedback data, which is divided into explicit and implicit data, to the EIMNF model, which is a neural matrix model that aids us in forecasting users' preferences. Furthermore, we undertake cross domain analysis with the use of a CNN FT model, and all of the outputs created by the algorithm are referred to as intermediate recommended items, and they are delivered to an item pool to be reranked. Re-ranking improves accuracy and allows us to provide the best possible suggestions to our users. We employ a graph neural network to execute re-ranking, and the best things generated after reranking are provided to our end-user. We compared our model to various models and found that proposed model has 0.91 precision, 0.84 recall, 0.87 FMeasure and holds 91% accuracy. [ABSTRACT FROM AUTHOR]

The exponential outbreak of news articles makes it troublesome for the readers to find, select and read the most relevant ones and alleviate the resulting information and cognitive overload problems. In recent years, various deep learning (DL) models were developed to recommend personalized quality news articles and support readers. Yet, no survey paper highlights the strengths, weaknesses, and trends of news recommendation models employing DL methods. This survey fills this gap in the literature by identifying the current state of DL-based news recommendation methods. Specifically, it covers (1) the classification of DL-based news recommendation models, (2) their performance comparison, and (3) the essential issues faced by these models. It discusses the most commonly used datasets, evaluation methods, and implications for researchers working in this area. [ABSTRACT FROM AUTHOR]

In the context of academic expert finding, this paper investigates and compares the performance of information retrieval (IR) and machine learning (ML) methods, including deep learning, to approach the problem of identifying academic figures who are experts in different domains when a potential user requests their expertise. IR-based methods construct multifaceted textual profiles for each expert by clustering information from their scientific publications. Several methods fully tailored for this problem are presented in this paper. In contrast, ML-based methods treat expert finding as a classification task, training automatic text classifiers using publications authored by experts. By comparing these approaches, we contribute to a deeper understanding of academic-expert-finding techniques and their applicability in knowledge discovery. These methods are tested with two large datasets from the biomedical field: PMSC-UGR and CORD-19. The results show how IR techniques were, in general, more robust with both datasets and more suitable than the ML-based ones, with some exceptions showing good performance.

The purpose of click-through rate (CTR) prediction is to anticipate how likely a person is to click on an advertisement or item. It's required for a lot of internet applications, such online advertising and recommendation systems. The previous click-through rate estimation approach suffered from the following two flaws. On the one hand, input characteristics (such as user id, user age, user age, item id, item category) are usually sparse and multidimensional, making them effective. High-level combination characteristics are used for prediction. Obtaining it manually by domain experts takes a long time and is difficult to finish; also, customer interests are not all the same. The accuracy of the model findings will significantly increase if this immediately recognized component is incorporated in the prediction model. As a consequence, this study creates an IARM (interactive attention rate estimation model) that incorporates user interest as well as a multi-head self-attention mechanism. The deep learning network is used in the model to determine the user's interest expression based on user attributes. The multi-head self-attention mechanism with residual network is then employed to get feature interaction, which enhances the degree of effect of significant characteristics on the estimation result as well as its accuracy. The IARM model outperforms other recent prediction models in the assessment metrics AUC and LOSS, and it has superior accuracy, according to the results from the public experimental data set. [ABSTRACT FROM AUTHOR]

Aiming at the problem that the existing Point of Interest (POI) recommendation model in social network big data is difficult to extract deep feature information, a POI recommendation model based on deep learning in social networks and big data is proposed in this article. The input data are all gathered through intelligent sensors to apply some raw data pre-processing tasks and thus reduce the computational burden on the model. First, a POI static feature extraction method based on symmetric matrix decomposition is designed to capture the geographical location and POI category features in Location-Based Social Networking (LBSN). Second, the improved Continuous Bags-of-Words (CBOW) model is used to extract the semantic features in the user comment information, and realize the implicit vector representation of POI in geographic, category, semantic and temporal feature space. Finally, by adaptively selecting relevant check-in activities from the check-in history to learn and change user preferences, the Geographical-Spatiotemporal Gated Recurrent Unit Network (GSGRUN) can distinguish the user preferences of different check-in. Experiments show that when the length of the recommendation list is 15, the precision of the proposed algorithm on the loc-Gowalla data set is 0.0686, the recall is 0.0769, and the precision on the loc-Brightkite data set is 0.0659, the recall is 0.0835, both of which are better than the comparative recommendation methods. Therefore, compared with the comparison methods, the proposed method can significantly improve the performance of the POI recommendation system. [ABSTRACT FROM AUTHOR]

The graph neural network has received significant attention in recent years because of its unique role in mining graph-structure data and its ubiquitous application in various fields, such as social networking and recommendation systems. Although most work focuses on learning low-dimensional node representation in static graphs, the dynamic nature of real-world networks makes temporal graphs more practical and significant. In this paper, we propose a dynamic graph representation learning method based on a temporal graph transformer (TGT), which can efficiently preserve high-order information and temporally evolve structural properties by incorporating an update module, an aggregation module, and a propagation module in a single model. The experimental results on three real-world networks demonstrate that the TGT outperforms state-of-the-art baselines for dynamic link prediction and edge classification tasks in terms of both accuracy and efficiency. [ABSTRACT FROM AUTHOR]

A collaborative recommender system based on a latent factor model has achieved significant success in the field of personalized recommender systems. However, the latent factor model suffers from sparsity problems. It is also limited in its ability to extract non-linear data features, resulting in poor recommendation performance. Inspired by the success of deep learning in different application areas, we incorporate deep learning into our proposed method to overcome the above problems. In this paper, we propose a dual deep learning and embedding-based latent factor model that considers dense user and item feature vectors. The model combines the existing deep learning and latent factor models to extract deep abstractions and non-linear feature representations of the data for rating prediction. The core idea is to map the dense user and item vectors generated by embedding techniques into dual, fully connected deep neural network architectures. In these two separate architectures, it learns the non-linear representation of the input data. The method then predicts the rating score by integrating the factors obtained from the two independent structures using the inner product. From the experimental result, we observe that the proposed model outperformed state-of-the-art existing models in real-world datasets (MovieLens ML-100K and ML-1M). [ABSTRACT FROM AUTHOR]

Bearing fault diagnosis for equipment-safe operation has a crucial role. In recent years, more achievements have been made in bearing fault diagnosis. However, for the fault diagnosis model, the representation and sensitivity of bearing fault features have a great influence on the diagnosis output results; thus, the attention mechanism is particularly important for the selection of features. However, global attention focuses on all sequences, which is computationally expensive and not ideal for fault diagnosis tasks. The local attention mechanism ignores the relationship between non-adjacent sequences. To address the respective shortcomings of global attention and local attention, an adaptive sparse attention network is proposed in this paper to filter fault-sensitive information by soft threshold filtering. In addition, the effects of different signal representation domains on fault diagnosis results are investigated to filter out signal representation forms with better performance. Finally, the proposed adaptive sparse attention network is applied to cross-working conditions diagnosis of bearings. The adaptive sparse attention mechanism focuses on the signal characteristics of different frequency bands for different fault types. The proposed network model achieves better overall performance when comparing the cross-conditions diagnosis accuracy and model convergence speed. [ABSTRACT FROM AUTHOR]

Deep Neural Networks (DNN) has attained impressive results in various natural language processing tasks. It attracts the researchers to apply DNN in the Recommender Systems (RS). Typically, majority of the recommendation algorithms apply Collaborative Filtering (CF) to recommend the items of user interest. Recently, so many researchers have applied CF with deep learning for RS. But most of the recommendations exploit only on the implicit data like user clicks, page visit, item description and employs matrix factorization with an inner product to obtain the correlations. To improve the performance of the recommendation system, we propose a novel architecture named Outer Product Based Residual CNN. The proposed model utilizes an explicit user-item sparse rating matrix and outer product function to learns high-order correlations that exist between the users and items latent features. The experimental result shows that the proposed methods outperform the state of art methods. [ABSTRACT FROM AUTHOR]

Summary: Recommender systems have become a core part of the retail experience. Retailers often rely on recommender systems to help them drive more conversions through targeted communication and advertisements. However, recommender systems are not one size fits all. Specialized retailers require specialized recommender systems to consider various features, attributes, and dynamics about the product category. In this paper, we have proposed a novel fruit recommender system that generates dynamic recommendations while remediating the problem of data sparsity. We have developed a novel fruit recommender system that considers the temporal dynamics in the fruit market, like price fluctuations, fruit seasonality, and quality variations that occur throughout the year. To perform this task, we have used Recurrent Recommender Network (RRN), which uses the deep learning method Long Short‐Term Memory (LSTM) to implement the system model. To ensure that our work and results obtained are practical, we have worked in a real‐world setting, by tying up with a specialty fruit retailer based in New Delhi to get the real‐world Point‐of‐Sale (POS) data of consumers. The result of the study suggests our algorithm performs better than other benchmark algorithms along NDCG and RMSE metrics. [ABSTRACT FROM AUTHOR]

Though relatively good effect has been achieved by the image de‐blurring method based on deep learning, the existing methods still suffer from the problem of unclear restoration of the edges. Therefore, brain‐inspired image restoration model based on human attention and "fine vision" is proposed to improve the blind restoration quality of the image in this paper according to the response mechanism of the different cerebral cortices for high and low spatial resolutions. The designed brain‐inspired model consists of dual‐channel network available to realize the function of feature merger for low and high resolutions, which is used to extract the image edges with detailed information filtered out. Confirmatory experiment is implemented based on the blurred image in the data set of GOPRO, LIVE and set14. As per the result, the model proposed is available for relatively good restoration of blurred image and super‐resolution, as well as looking results by visual inspection. [ABSTRACT FROM AUTHOR]

The sequential recommendation (also known as the next-item recommendation), which aims to predict the following item to recommend in a session according to users’ historical behavior, plays a critical role in improving session-based recommender systems. Most of the existing deep learning-based approaches utilize the recurrent neural network architecture or self-attention to model the sequential patterns and temporal influence among a user’s historical behavior and learn the user’s preference at a specific time. However, these methods have two main drawbacks. First, they focus on modeling users’ dynamic states from a user-centric perspective and always neglect the dynamics of items over time. Second, most of them deal with only the first-order user-item interactions and do not consider the high-order connectivity between users and items, which has recently been proved helpful for the sequential recommendation. To address the above problems, in this article, we attempt to model user-item interactions by a bipartite graph structure and propose a new recommendation approach based on a Position-enhanced and Time-aware Graph Convolutional Network (PTGCN) for the sequential recommendation. PTGCN models the sequential patterns and temporal dynamics between user-item interactions by defining a position-enhanced and time-aware graph convolution operation and learning the dynamic representations of users and items simultaneously on the bipartite graph with a self-attention aggregator. Also, it realizes the high-order connectivity between users and items by stacking multi-layer graph convolutions. To demonstrate the effectiveness of PTGCN, we carried out a comprehensive evaluation of PTGCN on three real-world datasets of different sizes compared with a few competitive baselines. Experimental results indicate that PTGCN outperforms several state-of-the-art sequential recommendation models in terms of two commonly-used evaluation metrics for ranking. In particular, it can make a better trade-off between recommendation performance and model training efficiency, which holds great potential for online session-based recommendation scenarios in the future. [ABSTRACT FROM AUTHOR]

Recommender system can find information that satisfies the user′s individual needs from huge amount of information. With the development of deep learning, deep learning has been widely applied in recommender systems. CTR prediction plays an important role in recommender system and has been widely used in many fields such as personalized recommendation, information retrieval, online advertising and so on. For the issue of large and sparse data in recommender system, this study merges xDeepFM model with attention network, and proposes a new CTR prediction model based on deep learning, which is called Atte-xDeepFM model. This model can solve the issue of feature scarcity, effectively learn the interactions relationship between features, and does not need to manually extract useful information in feature engineering. The comparative experiments on Avazu and Criteo data sets prove the effectiveness of the proposed model. Compared with the algorithm model commonly used in CTR prediction, the proposed model has better recommendation effect. [ABSTRACT FROM AUTHOR]

Generative Adversarial Networks (GANs) has achieved great success in computer vision like Image Inpainting, Image Super-Resolution. Many researchers apply it to improve the effectiveness of recommendation system. However, GANs-based methods obtain users' preferences using a single Neural Network framework in generative model, which may not be fully mined. Furthermore, most GANs-based algorithms adopt cross-entropy loss to get pair-wise bias, but these methods don't reveal global data distribution loss when data are sparse. Those problems will influence the performance of the algorithm and result in poor accuracy. To address these problems, we introduce Wide & Deep Generative Adversarial Networks for Recommendation System (a.k.a W & DGAN) in this paper. On the one hand, we employ Wide & Deep Learning as a generative model capable of extracting both explicit and implicit information of user preferences. Furthermore, we combine Cross-Entropy loss in G with Wasserstein loss in D to get data distribution, then, the joint loss will be to receive the training information feedback from data distribution. Empirical results on three public benchmarks show that W&DGAN significantly outperforms state-of-the-art methods. [ABSTRACT FROM AUTHOR]

To address the problem of poor recommendation accuracy in the session recommendation model, this paper proposed a capsule-based graph convolutional disentanglement for session-aware recommendation (CGCD) method.Specifically, it used disentangled learning technique to transition item embedding into factor embedding based on multiple sub-channels. Then, it used a capsule dynamic fusion strategy to aggregate different factors to obtain a new item embedding.In addition, it used a multi-head attention mechanism to assign weights to each item in the session. Finally, it aggregated the item embedding with other items in the current session according to the assigned weights, and then generated an accurate session representation to achieve item recommendation. Experiments on two publicly available real datasets show that the proposed model can improve the Pre@10, Pre@20, MRR@10 and MRR@20 by 5.17%, 2.99%, 6.56% and 2.94% on average of the recommended performance, which verifies the effectiveness and efficiency of the method in this paper. [ABSTRACT FROM AUTHOR]

In order to improve the safety of pedestrian interaction in complex traffic scenes, this paper proposes a pedestrian trajectory prediction algorithm SAN-GAN (Social Angle Norm-GAN) based on Social-GAN network (Social-Generative Adversarial Network). The algorithm first takes the pedestrian’s historical location information and head information as input, obtains the hidden feature information of the pedestrian through the trajectory generator LSTM network (Long Short Term Memory Networks), captures the dynamic changes of the pedestrian’s visual field based on the pedestrian visual field module, builds a fan-shaped visual field for all pedestrians and filters the valid information, thus driving the neural network model to predict future trajectory changes of pedestrians. By comparing SAN-GAN with LSTM, Social-LSTM (Social-Long Short Term Memory Networks), Social-GAN and other trajectory prediction algorithms, The results show that the SAN-GAN algorithm reduces the ADE by an average of 65.8%, 51.2% and 10.7%, and the FDE by an average of 73.6%, 60.9% and 10.4%, respectively, in predicting the pedestrian trajectory for 3.2 s. The SAN-GAN is effective in predicting the future trajectory of pedestrians interacting in complex traffic environments compared to other algorithms. [ABSTRACT FROM AUTHOR]

Recommender systems are similar to an information filtering system that helps identify items that best satisfy the users' demands based on their preference profiles. Context-aware recommender systems (CARSs) and multi-criteria recommender systems (MCRSs) are extensions of traditional recommender systems. CARSs have integrated additional contextual information such as time, place, and so on for providing better recommendations. However, the majority of CARSs use ratings as a unique criterion for building communities. Meanwhile, MCRSs utilize user preferences in multiple criteria to better generate recommendations. Up to now, how to exploit context in MCRSs is still an open issue. This paper proposes a novel approach, which relies on deep learning for context-aware multi-criteria recommender systems. We apply deep neural network (DNN) models to predict the context-aware multi-criteria ratings and learn the aggregation function. We conduct experiments to evaluate the effect of this approach on the real-world dataset. A significant result is that our method outperforms other state-of-the-art methods for recommendation effectiveness. [ABSTRACT FROM AUTHOR]