Search

Volume 27(6) of the Intelligent Data Analysis (IDA) Journal contains fifteen articles covering theoretical contributions and applied Intelligent Data Analysis. The theoretical papers discuss topics such as new methods for data preparation and preprocessing, graph-based recommendation systems, and Bayesian network classifiers. The application papers focus on computer vision and image processing, including the detection of small objects in unmanned aerial vehicle images and the detection and classification of brain tumors using Magnetic Resonance Images. Other application papers cover topics such as classifying smart contracts, identifying important nodes in social networks, extracting failure evidence from complex hybrid data in QA systems, and producing note-level transcriptions of music melodies. [Extracted from the article]

The major challenge of recommendation system (RS) based on implict feedback is to accurately model users' preferences from their historical feedback. Nowadays, researchers has tried to apply adversarial technique in RS, which had presented successful results in various domains. To a certain extent, the use of adversarial technique improves the modeling of users' preferences. Nonetheless, there are still many problems to be solved, such as insufficient representation and low-level interaction. In this paper, we propose a recommendation algorithm NCGAN which combines neural collaborative filtering and generative adversarial network (GAN). We use the neural networks to extract users' non-linear characteristics. At the same time, we integrate the GAN framework to guide the recommendation model training. Among them, the generator aims to make user recommendations and the discriminator is equivalent to a measurement tool which could measure the distance between the generated distribution and users' ground distribution. Through comparison with other existing recommendation algorithms, our algorithm show better experimental performance in all indicators. [ABSTRACT FROM AUTHOR]

Collaborative filtering (CF), a representative algorithm of recommendation systems, is a method of using information of the neighbors of active user. The main idea of CF is that users who agreed in the ratings of certain items are likely to agree again in new items. The degree to which the two users' tendencies in the ratings of the co-rated items are consistent is measured using a similarity measure. Therefore, the similarity measure in CF plays a key role in the extraction of the representative neighbors. Studies on the improvement of similarity indicators for selecting representative neighbors are still ongoing. Recently, a new similarity measure, named OS, was proposed to enhance the recommendation performance by utilizing mathematical equations, such as the integral equation, system of linear differential equations, and non-linear systems. This study aims to understand the limitations of OS and overcome these limitations using the proposed method. In the proposed method, a sigmoid function was used to reflect preferences, such as the positive or negative sentiment of user ratings. In addition, to consider the absolute score difference, some of the formulas were modified, and finally, the performance improvement of the recommendation system was proved through experiments. [ABSTRACT FROM AUTHOR]

Recommender systems apply machine learning and data mining techniques for filtering unseen information, and they can provide an opportunity to predict whether a user would be interested in a given item. The main types of recommender systems are collaborative filtering (CF) and content-based filtering, which suffer from scalability and data sparsity resulting in poor quality recommendations and reduced coverage. There are two incremental algorithms based on Singular Value Decomposition (SVD) with high scalability for recommender systems which are named the incremental SVD algorithm and incremental Approximating the Singular Value Decomposition (ApproSVD) algorithm. In both mentioned methods, the estimated value of rank for approximating the recommender systems' data matrix is chosen experimentally in the related literature. In this paper, we investigate the role of singular values for estimating a more reliable amount of rank in the mentioned dimensionality reduction techniques to improve the recommender systems' performance. In other words, we offered a strategy for choosing the optimal rank that approximates the data matrix more accurately in incremental algorithms with the help of singular values. The numerical results illustrate that the suggested strategy improves the accuracy of the recommendations and run times of both algorithms when employs for Movielens, Netflix, and Jester dataset. [ABSTRACT FROM AUTHOR]

Ordinal regression has been widely used in applications, such as credit portfolio management, recommendation systems, and ecology, where the core task is to predict the labels on ordinal scales. Due to its learning efficiency, online ordinal regression using passive aggressive (PA) algorithms has gained a much attention for solving large-scale ranking problems. However, the PA method is sensitive to noise especially in the scenario of streaming data, where the ranking of data samples may change dramatically. In this paper, we propose a noise-resilient online learning algorithm using the Ramp loss function, called PA-RAMP, to improve the performance of PA method for noisy data streams. Also, we validate the order preservation of thresholds of the proposed algorithm. Experiments on real-world data sets demonstrate that the proposed noise-resilient online ordinal regression algorithm is more robust and efficient than state-of-the-art online ordinal regression algorithms. [ABSTRACT FROM AUTHOR]

The purpose of aspect-level sentiment analysis is to identify the contextual sentence expressions given by sentiment for some aspects. For previous works, many scholars have proved the importance of the interaction between aspects and contexts. However, most existing methods ignore or do not specifically capture the position information of the aspect targets in the sentence. Thus, we propose an aspect-level sentiment analysis based on joint aspect and position hierarchy attention mechanism network. At the same time, the model adopts a joint approach to make the model of the aspect features and the position features. On the one hand, this method clearly captures the interaction between aspect words and context when inputting word vector information. On the other hand, this method can enhance the importance of position information in the sentence and boost the information retrieval ability of the model. Additionally, the model utilizes a hierarchical attention mechanism to extract feature information and to differentiate sentiment towards, which is similar to filtering useless information again. Experiment on the SemEval 2014 dataset represent that our model achieves better performance on aspect-level sentiment classification. [ABSTRACT FROM AUTHOR]

Explainable recommendations enable users to understand why certain items are suggested and, ultimately, nurture system transparency, trustworthiness, and confidence. Large crowdsourcing recommendation systems ought to crucially promote authenticity and transparency of recommendations. To address such challenge, this paper proposes the use of stream-based explainable recommendations via blockchain profiling. Our contribution relies on chained historical data to improve the quality and transparency of online collaborative recommendation filters – Memory-based and Model-based – using, as use cases, data streamed from two large tourism crowdsourcing platforms, namely Expedia and TripAdvisor. Building historical trust-based models of raters, our method is implemented as an external module and integrated with the collaborative filter through a post-recommendation component. The inter-user trust profiling history, traceability and authenticity are ensured by blockchain, since these profiles are stored as a smart contract in a private Ethereum network. Our empirical evaluation with HotelExpedia and Tripadvisor has consistently shown the positive impact of blockchain-based profiling on the quality (measured as recall) and transparency (determined via explanations) of recommendations. [ABSTRACT FROM AUTHOR]

Tensors have been explored to share latent user-item relations and have been shown to be effective for recommendation. Tensors suffer from sparsity and cold start problems in real recommendation scenarios; therefore, researchers and engineers usually use matrix factorization to address these issues and improve the performance of recommender systems. In this paper, we propose matrix factorization completed multicontext data for tensor-enhanced algorithm a using matrix factorization combined with a multicontext data method for tensor-enhanced recommendation. To take advantage of existing user-item data, we add the context time and trust to enrich the interactive data via matrix factorization. In addition, Our approach is a high-dimensional tensor framework that further mines the latent relations from the user-item-trust-time tensor to improve recommendation performance. Through extensive experiments on real-world datasets, we demonstrated the superiority of our approach in predicting user preferences. This method is also shown to be able to maintain satisfactory performance even if user-item interactions are sparse. [ABSTRACT FROM AUTHOR]

Recently, the e-learners are drastically increased from the last two decades. Everything is learnt through internet without help of the tutor as well. For this purpose, the e-learners are required more e-learning applications that are able to supply optimal and satisfied data based on their capability. No content recommendation system is available for recommending suitable contents to the learners. For this purpose, this paper proposes a new semantic and fuzzy aware content recommendation system for retrieving the suitable content for the users. In this content recommendation system, we propose two content pre-processing algorithms namely Target Keyword based Data Pre-processing Algorithm (TKDPA) and Intelligent Anova-T Residual Algorithm (IAATRA) for selecting the more relevant features from the document. Moreover, a new Fuzzy rule based Similarity Matching algorithm (FRSMA) is proposed and used in this system for finding the similarity between the two terms and also rank them by using the newly proposed Similarity and Temporal aware Weighted Document Ranking Algorithm (STWDRA). In addition, a content clustering process is also incorporated for gathering relevant content. Finally, a new Fuzzy, Target Keyword and Similarity Score based Content Recommendation Algorithm (FTKSCRA) is also proposed for recommending the more relevant content to the learners accurately. The experiments have been conducted for evaluating the proposed content recommendation system and proved as better than the existing recommendation systems in terms of precision, recall, f-measure and prediction accuracy. [ABSTRACT FROM AUTHOR]

Legal practitioners analyze relevant previous judgments to prepare favorable and advantageous arguments for an ongoing case. In Legal domain, recommender systems (RS) effectively identify and recommend referentially and/or semantically relevant judgments. Due to the availability of enormous amounts of judgments, RS needs to compute pairwise similarity scores for all unique judgment pairs in advance, aiming to minimize the recommendation response time. This practice introduces the scalability issue as the number of pairs to be computed increases quadratically with the number of judgments i.e., O (n2). However, there is a limited number of pairs consisting of strong relevance among the judgments. Therefore, it is insignificant to compute similarities for pairs consisting of trivial relevance between judgments. To address the scalability issue, this research proposes a graph clustering based novel Legal Document Recommendation System (LDRS) that forms clusters of referentially similar judgments and within those clusters find semantically relevant judgments. Hence, pairwise similarity scores are computed for each cluster to restrict search space within-cluster only instead of the entire corpus. Thus, the proposed LDRS severely reduces the number of similarity computations that enable large numbers of judgments to be handled. It exploits a highly scalable Louvain approach to cluster judgment citation network, and Doc2Vec to capture the semantic relevance among judgments within a cluster. The efficacy and efficiency of the proposed LDRS are evaluated and analyzed using the large real-life judgments of the Supreme Court of India. The experimental results demonstrate the encouraging performance of proposed LDRS in terms of Accuracy, F1-Scores, MCC Scores, and computational complexity, which validates the applicability for scalable recommender systems. [ABSTRACT FROM AUTHOR]

Recommender System (RS) is an information filtering approach that helps the overburdened user with information in his decision making process and suggests items which might be interesting to him. While presenting recommendation to the user, accuracy of the presented list is always a concern for the researchers. However, in recent years, the focus has now shifted to include the unexpectedness and novel items in the list along with accuracy of the recommended items. To increase the user acceptance, it is important to provide potentially interesting items which are not so obvious and different from the items that the end user has rated. In this work, we have proposed a model that generates serendipitous item recommendation and also takes care of accuracy as well as the sparsity issues. Literature suggests that there are various components that help to achieve the objective of serendipitous recommendations. In this paper, fuzzy inference based approach is used for the serendipity computation because the definitions of the components overlap. Moreover, to improve the accuracy and sparsity issues in the recommendation process, cross domain and trust based approaches are incorporated. A prototype of the system is developed for the tourism domain and the performance is measured using mean absolute error (MAE), root mean square error (RMSE), unexpectedness, precision, recall and F-measure. [ABSTRACT FROM AUTHOR]

A large number of practical applications of the recommendation system found that the novelty of the recommendation results and the user satisfaction are more closely related, making the novelty recommendation recently widely concerned and studied. Many novelty recommendation algorithms used the popularity of the item to measure novelty, but this method is too simple, and the change of item popularity is more reflective of its novelty. According to the product life cycle theory (PLC), this study proposed a novelty recommendation algorithm that recommends item that be not popular now and may be popular in the future to improve the novelty of the recommendation results, The time change of the popularity of the items to be recommended is analyzed, and the future popularity of the items are predicted by analogy. Two strategies for selecting recommended selection are selecting future popular items (the predicting popularity-based filtering Algorithm, PP algorithm) and excluding future recession items (the Excluding Recession-based filtering algorithm, ER algorithm), according to the definition of novelty of the item, recommended the novelty items to the target user. The effectiveness of the proposed algorithm was verified through an offline experiment. Results indicate that PP algorithm can significantly improve the accuracy and novelty, but seriously sacrifice the coverage and reduce the ability of the recommendation system to mine the long tail items when the number of alternative items N is small, the novelty of the recommendation list of the ER algorithm is remarkably higher than that of traditional algorithms, the novelty is high when the quantity of alternative sets reaches around 350, where the average popularity of the recommendation list declines by 40%, and the coverage is elevated by 150%, thereby improving the ability of the proposed system to extract all kinds of items. This study serves as reference for the improvement of user satisfaction with recommendation systems. [ABSTRACT FROM AUTHOR]

Matrix factorization (MF) models are effective and easy to expand and are widely used in industry, such as rating prediction and item recommendation. The basic MF model is relatively simple. In practical applications, side information such as attributes or implicit feedback is often combined to improve accuracy by modifying the model and optimizing the algorithm. In this paper, we propose an attribute interaction-aware matrix factorization (AIMF) method for recommendation tasks. We partition the original rating matrix into different sub-matrices according to the attribute interactions, train each sub-matrix independently, and merge all the latent vectors to generate the final score. Since the generated sub-matrices vary in size, an adaptive regularization coefficient optimization strategy and an adaptive latent vector dimension optimization strategy are proposed for sub-matrix training, and a variety of latent vector merging methods are put forward. The method AIMF has two advantages. When the original rating matrix is particularly large, the training time complexity of the MF-based model becomes higher and the update cost of the model is also higher. In AIMF, because each sub-matrix is usually much smaller than the original rating matrix, the training time complexity is greatly reduced after using parallel computing technology. Secondly, in AIMF, it is not necessary to modify the matrix factorization model to incorporate attributes and their interactive information into the model to improve the performance. The experimental results on the two classic public datasets MovieLens 1M and MovieLens 100k show that AIMF can not only effectively improve the accuracy of recommendation, but also make full use of parallel computing technology to improve training efficiency without modifying the matrix factorization model. [ABSTRACT FROM AUTHOR]

With the increase of online businesses, recommendation algorithms are being researched a lot to facilitate the process of using the existing information. Such multi-criteria recommendation (MCRS) helps a lot the end-users to attain the required results of interest having different selective criteria – such as combinations of implicit and explicit interest indicators in the form of ranking or rankings on different matched dimensions. Current approaches typically use label correlation, by assuming that the label correlations are shared by all objects. In real-world tasks, however, different sources of information have different features. Recommendation systems are more effective if being used for making a recommendation using multiple criteria of decisions by using the correlation between the features and items content (content-based approach) or finding a similar user rating to get targeted results (Collaborative filtering). To combine these two filterings in the multicriteria model, we proposed a features-based fb-knn multi-criteria hybrid recommendation algorithm approach for getting the recommendation of the items by using multicriteria features of items and integrating those with the correlated items found in similar datasets. Ranks were assigned to each decision and then weights were computed for each decision by using the standard deviation of items to get the nearest result. For evaluation, we tested the proposed algorithm on different datasets having multiple features of information. The results demonstrate that proposed fb-knn is efficient in different types of datasets. [ABSTRACT FROM AUTHOR]

Identifying potential social media influencers (SMIs) accurately can achieve a long-time and effective concept marketing at a lower cost, and then promote the development of the corporate brand in online communities. However, potential SMIs discrimination often faces the problem of insufficient available information of the long-term evolution of the network, and the existing discriminant methods based on link analysis fail to obtain more accurate results. To fill this gap, a consensus smart discriminant algorithm (CSDA) is proposed to identify the potential SMIs with the aid of attention concentration (AC) between users in a closed triadic structure. CSDA enriches and expands the users' AC information by fusing multiple attention concentration indexes (ACIs) as well as filters the noise information caused by multi-index fusion through consensus among the indexes. Specifically, to begin with, to enrich the available long-term network evolution information, the unidirectional attention concentration indexes (UACIs) and the bidirectional attention concentration indexes (BACIs) are defined; next, the consensus attention concentration index (CACI) is selected according to the principle of minimum upper and lower bounds of link prediction bias to filter noise information; the potential SMI is determined by adaptively calculating CACI among the user to be identified, unconnected user group and their common neighbor. The validity and reliability of the proposed method are verified by the actual data of Twitter. [ABSTRACT FROM AUTHOR]

Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user's specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

The existing recommender system provides personalized recommendation service for users in online shopping, entertainment, and other activities. In order to improve the probability of users accepting the system's recommendation service, compared with the traditional recommender system, the interpretable recommender system will give the recommendation reasons and results at the same time. In this paper, an interpretable recommendation model based on XGBoost tree is proposed to obtain comprehensible and effective cross features from side information. The results are input into the embedded model based on attention mechanism to capture the invisible interaction among user IDs, item IDs and cross features. The captured interactions are used to predict the match score between the user and the recommended item. Cross-feature attention score is used to generate different recommendation reasons for different user-items.Experimental results show that the proposed algorithm can guarantee the quality of recommendation. The transparency and readability of the recommendation process has been improved by providing reference reasons. This method can help users better understand the recommendation behavior of the system and has certain enlightenment to help the recommender system become more personalized and intelligent. [ABSTRACT FROM AUTHOR]

The sustainable economic learning course recommendation can quickly find the knowledge information that the user really needs from the massive information space and realize the personalized recommendation to the user. However, the occurrence of trust attacks seriously affects the normal recommendation function of the recommendation system, resulting in its failure to provide users with reliable and reliable recommendation results. In order to solve the vulnerability of the recommendation system to the support attack, based on text vector model and support vector machine, this paper makes a comprehensive analysis of the current research status of the robust recommendation technology. Moreover, based on the idea of suspicious user metrics, this paper has conducts in-depth research on how to design highly robust recommendation algorithms, and constructs a highly reliable sustainable economic learning course recommendation model. In addition to this, this research tests the performance of the system from two perspectives of course recommendation satisfaction and system retrieval accuracy. The experiment proves that the model constructed in this paper performs well in the recommendation of sustainable economic learning courses. [ABSTRACT FROM AUTHOR]

User rating information on multiple predefined aspects gathered by hotel recommendation systems generally shows a deviation between the overall rating and detailed criteria ratings. In this study, to address this deviation, we proposed a novel hotel recommendation method that clusters users with different preferences into different groups using the K-means algorithm. Moreover, we allocated weights to different criteria and obtained a comprehensive score. A case study on actual data from Tripadvisor.com showed that compared with three other models, our proposed model demonstrated a more impressive performance. This research can offer advantages to hotel service providers and customers in terms of decision making. [ABSTRACT FROM AUTHOR]

Recommender systems can recommend products by analyzing the interests and habits of users. To make more efficient recommendation, the contextual information should be collected in recommendation algorithms. In the restaurant recommendation, the location and the current time of customers should also be considered to facilitate restaurants to find potential customers and give accurate and timely recommendations. However, the existing recommendation approaches often lack the consideration of the influence of time and location. Besides, the data sparsity is an inherent problem in the collaborative filtering algorithm. To address these problems, this paper proposes a recommendation approach which combines the contextual information including time, price and location. Instead of constructing the user-restaurant scoring matrix, the proposed approach clusters price tags and generates the user-price scoring matrix to alleviate the sparsity of data. The experiment on Foursquare dataset shows that the proposed approach has a better performance than traditional ones. [ABSTRACT FROM AUTHOR]

The recent privacy incidents reported in major media about global social networks raised real public concerns about centralized architectures. P2P social networks constitute an interesting paradigm to give back users control over their data and relations. While basic social network functionalities such as commenting, following, sharing, and publishing content are widely available, more advanced features related to information retrieval and recommendation are still challenging. This is due to the absence of a central server that has a complete view of the network. In this paper, we propose a new recommender system called P2PCF. We use collaborative filtering approach to recommend content in P2P social networks. P2PCF enables privacy preserving and tackles the cold start problem for both users and content. Our proposed approach assumes that the rating matrix is distributed within peers, in such a way that each peer only sees interactions made by her friends on her timeline. Recommendations are then computed locally within each peer before they are sent back to the requester. Our evaluations prove the effectiveness of our proposal compared to a centralized scheme in terms of recall and coverage. [ABSTRACT FROM AUTHOR]

A recommendation system is based on the user and the items, providing appropriate items to the user and effectively helping the user to find items that may be of interest. The most commonly used recommendation method is collaborative filtering. However, in this case, the recommendation system will be injected with false data to create false ratings to push or nuke specific items. This will affect the user's trust in the recommendation system. After all, it is important that the recommendation system provides a trusted recommendation item. Therefore, there are many algorithms for detecting attacks. In this article, it proposes a method to detect attacks based on the beta distribution. Different researchers in the past assumed that the attacker only attacked one target item in the user data. This research simulated an attacker attacking multiple target items in the experiment. The result showed a detection rate of more than 80%, and the false rate was within 16%. [ABSTRACT FROM AUTHOR]

Recommender systems have recently become a significant part of e-commerce applications. Through the different types of recommender systems, collaborative filtering is the most popular and successful recommender system for providing recommendations. Recent studies have shown that using multi-criteria ratings helps the system to know the customers better. However, bringing multi aspects to collaborative filtering causes new challenges such as scalability and sparsity. Additionally, revealing the relation between criteria is yet another optimization problem. Hence, increasing the accuracy in prediction is a challenge. In this paper, an aggregation-function based multi-criteria collaborative filtering system using Rough Sets Theory is proposed as a novel approach. Rough Sets Theory is used to uncover the relationship between the overall criterion and the individual criteria. Experimental results show that the proposed model (RoughMCCF) successfully improves the predictive accuracy without compromising on online performance. [ABSTRACT FROM AUTHOR]

Recommendation systems provide reliable and relevant recommendations to users and also enable users' trust on the website. This is achieved by the opinions derived from reviews, feedbacks and preferences provided by the users when the product is purchased or viewed through social networks. This integrates interactions of social networks with recommendation systems which results in the behavior of users and user's friends. The techniques used so far for recommendation systems are traditional, based on collaborative filtering and content based filtering. This paper provides a novel approach called User-Opinion-Rating (UOR) for building recommendation systems by taking user generated opinions over social networks as a dimension. Two tripartite graphs namely User-Item-Rating and User-Item-Opinion are constructed based on users' opinion on items along with their ratings. Proposed approach quantifies the opinions of users and results obtained reveal the feasibility. [ABSTRACT FROM AUTHOR]

The main goal of a recommendation system is to recommend items of interest to users by analyzing their historical data. Content-based and collaborative filtering are the traditional recommendation strategies, each with its own strengths and weaknesses. Some of their weaknesses can be overcome by combining the two strategies. The resulting hybrid system performs qualitatively better than the traditional recommendation systems. However, historical data of some users may consist largely of only likes or only dislikes. Those users are termed as optimistic or pessimistic users respectively. On an average there are around 10 to 20% of pessimistic users present in a given dataset. For pessimistic users, whose profiles have mostly dislikes and very few likes, content-based filtering can hardly recommend any items of interest. In content-based filtering technique pessimistic users get poor recommendations of either uninteresting movies or no recommendations at all. This can be alleviated by boosting the content profiles of pessimistic users using the top- n recommendations of collaborative filtering. This content boosted hybrid filtering system provides a novel list of recommendations even for pessimistic users, with predictive accuracy better than that of a traditional content-based filtering system. [ABSTRACT FROM AUTHOR]

In this research, we combine relational learning with multi-domain to develop a formal framework for a recommendation system. The design of our framework aims at: (i) constructing general rules for recommendations, (ii) providing suggested items with clear and understandable explanations, (iii) delivering a broad range of recommendations including novel and unexpected items. We use relational learning to find all possible relations, including novel relations, and to form the general rules for recommendations. Each rule is represented in relational logic, a formal language, associating with probability. The rules are used to suggest the items, in any domain, to the user whose preferences or other properties satisfy the conditions of the rule. The information described by the rule serves as an explanation for the suggested item. It states clearly why the items are chosen for the users. The explanation is in if-then logical format which is unambiguous, less redundant and more concise compared to a natural language used in other explanation recommendation systems. The explanation itself can help persuade the user to try out the suggested items, and the associated probability can drive the user to make a decision easier and faster with more confidence. Incorporating information or knowledge from multiple domains allows us to broaden our search space and provides us with more opportunities to discover items which are previously unseen or surprised to a user resulting in a wide range of recommendations. The experiment results show that our proposed algorithm is very promising. Although the quality of recommendations provided by our framework is moderate, our framework does produce interesting recommendations not found in the primitive single-domain based system and with simple and understandable explanations. [ABSTRACT FROM AUTHOR]

Customer-based practices enable benefits to organizations in a contentious business. Offering individualized proposals increase customer loyalty to be able to afloat. Understanding customers is a vital difficulty to perform personalized recommendations. As a demographic feature, gender information essentially cannot be captured by human tracking technologies. Hence, several procedures are improved to predict undiscovered gender information. In the research, the followed indoor paths in a shopping mall are used to predict customer genders using fuzzy c-medoids, one of the soft clustering techniques. A Levenshtein-based fuzzy classification methodology is proposed the followed paths as string data. Although some studies focused on gender prediction, no research has centered on path-oriented. The novelty of the investigation is to analyze customer path data for the gender classes. [ABSTRACT FROM AUTHOR]

Reviews text reflects user interests and product characteristics, which provide rich useful semantic textual information for modeling user and product. Some existing works improve the performance of rating prediction by distinguishing the usefulness of each review. However, most of them ignore the fact that the usefulness of each review should be dynamic and dependent on the target user-product pair. For example, when we predict the user's ratings of a restaurant, the user's previous reviews about restaurants should be more useful. To be more specific, when we model the target user-product pair, the usefulness of each review is associated with the target user and item. To address the above issue, we use a Review-level Dynamic Topic Co-Attention, which combines all reviews written by the user and all reviews that were written for the product to assign attention score for each review collaboratively. Besides, we believe that product category and user co-purchase information can further improve the rating prediction performance. In this paper, we propose a neural joint model called NMRP based on reviews, products category, and users' co-purchase information for rating prediction recommendation. First, the Review Extraction Module learns user and product information from reviews. Then, HIN Extraction Module extracts the association feature for a given target user-product pair from a heterogeneous information network (HIN). Finally, the two parts of data are connected and input into the feature interaction method Attentional Factorization Machines (AFM), to achieve rating prediction. The experimental study on three datasets from Amazon shows that our model outperforms recently proposed baselines such as DeepCoNN, TransNets, and NARRE. [ABSTRACT FROM AUTHOR]

The Internet of Things (smart things) is used in many sectors and applications due to recent technological advances. One of such application is in the transportation system, which is of primary use for the users to move from one place to another place. The smart devices which were embedded in vehicles are useful for the passengers to solve his/her query, wherein future vehicles will be fully automated to the advanced stage, i.e. future cars with driverless feature. These autonomous cars will help people a lot to reduce their time and increases their productivity in their respective (associated) business. In today's generation and in the near future, privacy preserving and trust will be a major concern among users and autonomous vehicles and hence, this paper will be able to provide clarity for the same. Many attempts in previous decade have provided many efficient mechanisms, but they all work only with vehicles along with a driver. However, these mechanisms are not valid and useful for future vehicles. In this paper, we will use deep learning techniques for building trust using recommender systems and Blockchain technology for privacy preserving. We also maintain a certain level of trust via maintaining the highest level of privacy among users living in a particular environment. In this research, we developed a framework that could offer maximum trust or reliable communication to users over the road network. With this, we also preserve privacy of users during traveling, i.e., without revealing identity of respective users from Trusted Third Parties or even Location Based Service in reaching a destination. Thus, Deep Learning based Blockchain Solution (DLBS) is illustrated for providing an efficient recommendation system. [ABSTRACT FROM AUTHOR]

Customers of a webshop are often presented large assortments, which can lead to customers struggling finding their desired product(s), an issue known as choice overload. In order to overcome this issue, recommender systems are used in webshops to provide personalized product recommendations to customers. Though, model-based recommender systems are not able to provide recommendations to new customers (i.e., cold users). To facilitate recommendations to cold users we investigate multiple active learning strategies, and subsequently evaluate which active learning strategy is able to optimally elicit the preferences from the cold users in a matrix factorization context. Our model is empirically validated using a dataset from the webshop of de Bijenkorf, a Dutch department store. We find that the overall best-performing active learning strategy is PopError, an active learning strategy that measures the variance score for each item. [ABSTRACT FROM AUTHOR]

Recommender systems have been widely used in our life in recent years to facilitate our life. And it is very important and meaningful to improve recommendation performance. Generally, recommendation methods use users' historical ratings on items to predict ratings on their unrated items to make recommendations. However, with the increase of the number of users and items, the degree of data sparsity increases, and the quality of recommendations decreases sharply. In order to solve the sparsity problem, other auxiliary information is combined to mine users' preferences for higher recommendation quality. Similar to rating data, review data also contain rich information about users' preferences on items. This paper proposes a novel recommendation model, which harnesses an adversarial learning among auto-encoders to improve recommendation quality by minimizing the gap of the rating and review relation between a user and an item. The empirical studies on real-world datasets show that the proposed method improves the recommendation performance. [ABSTRACT FROM AUTHOR]

A recommender system (RS) delivers personalized suggestions on products based on the interest of a particular user. Content-based filtering (CBF) and collaborative filtering (CF) schemes have been previously used for this task. However, the main challenge in RS is cold start problem (CSP). This originates once a new user joins the system which makes the recommendation task tedious due to the shortage of information (clickstream, dwell time, rating, etc.) regarding the user's interest. Therefore, CBF and CF are combined together by developing a knowledge-based preference learning (KBPL) system. This system considers the demographic data that includes gender, occupation, and age for the recommendation task. Initially, the dataset is clustered using the self-organizing map (SOM) technique, then the high dimensional data is decomposed by higher-order singular value decomposition (HOSVD) and finally, Adaptive neuro-fuzzy inference system (ANFIS) predicts the output. For the big dataset, SOM is a robust clustering method and the similarities among the users can be easily observed by grid clustering. The HOSVD extracts the required information from the available data set to find the user similarity by decomposing the dataset in lower dimensions. ANFIS uses IF-THEN rules to recommend similar product to the new users. The proposed KBPL system is evaluated with the Black Friday dataset and the obtained error value is compared with the existing CF and CBF techniques. The proposed KBPL system has obtained root mean squared error (RMSE) of 0.71%, mean absolute error (MAE) of 0.54%, and mean absolute percentage error (MAPE) of 37%. Overall, the outcome of the comparative analysis shows minimum error and better performance in terms of precision, recall, and f-measure for the proposed KBPL system compared to the existing techniques and therefore more suitable for accurately recommending the products for the new users. [ABSTRACT FROM AUTHOR]

In the current era of big data, the recommender system aspires to provide users with a tailored set of personalized items from a pool of a large population. The most popular collaborative filtering system performs this information filtering process by computing similarity among users or items. This paper proposes a similarity metric that comprises of weights and values. Values are calculated by considering the matching set of users for which similarity is to be computed. The optimal values of weights are decided using an upgraded form of the Crow Search Algorithm (CSA). The exploration and exploitation stability of CSA is improvised by making use of Levy flight diffusion, adaptive operator adjustment, and event factor. The performance of the implemented metaheuristic approach is validated on Jester, MovieLens 100K, and MovieLens 1M dataset. Comparative analysis of proposed model against several other traditional metaheuristic based personalization systems reveal that our model is less delicate to the dimension of datasets and it also presents exceptional refinement in terms of prediction complexity and accuracy. [ABSTRACT FROM AUTHOR]

Modeling the music mood has wide applications in music categorization, retrieval, and recommendation systems; however, it is challenging to computationally model the affective content of music due to its subjective nature. In this work, a structured regression framework is proposed to model the valence and arousal mood dimensions of music using a single regression model at a linear computational cost. To tackle the subjectivity phenomena, a confidence-interval based estimated consensus is computed by modeling the behavior of various annotators (e.g. biased, adversarial) and is shown to perform better than using the average annotation values. For a compact feature representation of music clips, variational Bayesian inference is used to learn the Gaussian mixture model representation of acoustic features and chord-related features are used to improve the valence estimation by probing the chord progressions between chroma frames. The dimensionality of features is further reduced using an adaptive version of kernel PCA. Using an efficient implementation of twin Gaussian process for structured regression, the proposed work achieves a significant improvement in R2 for arousal and valence dimensions relative to state-of-the-art techniques on two benchmark datasets for music mood estimation. [ABSTRACT FROM AUTHOR]

In Machine Learning scenarios, privacy is a crucial concern when models have to be trained with private data coming from users of a service, such as a recommender system, a location-based mobile service, a mobile phone text messaging service providing next word prediction, or a face image classification system. The main issue is that, often, data are collected, transferred, and processed by third parties. These transactions violate new regulations, such as GDPR. Furthermore, users usually are not willing to share private data such as their visited locations, the text messages they wrote, or the photo they took with a third party. On the other hand, users appreciate services that work based on their behaviors and preferences. In order to address these issues, Federated Learning (FL) has been recently proposed as a means to build ML models based on private datasets distributed over a large number of clients, while preventing data leakage. A federation of users is asked to train a same global model on their private data, while a central coordinating server receives locally computed updates by clients and aggregate them to obtain a better global model, without the need to use clients' actual data. In this work, we extend the FL approach by pushing forward the state-of-the-art approaches in the aggregation step of FL, which we deem crucial for building a high-quality global model. Specifically, we propose an approach that takes into account a suite of client-specific criteria that constitute the basis for assigning a score to each client based on a priority of criteria defined by the service provider. Extensive experiments on two publicly available datasets indicate the merits of the proposed approach compared to standard FL baseline. [ABSTRACT FROM AUTHOR]

This paper proposes a new content recommendation system which combines the newly proposed embedded feature selection method and the new Fuzzy Temporal Logic based Decision Tree incorporated Convolutional Neural Network classifier. The newly proposed embedded feature selection called Fuzzy Decision Tree and Weighted Gini-Index based Feature Selection Algorithm (FDTWGI-FSA) that contains the existing incorporated the Fuzzy Decision Tree (FDT) and the Weighted Gini-index based Feature Selection Algorithm (WGIFSA) for getting optimized feature subset. Moreover, an enhanced CNN and Fuzzy Temporal Decision Tree for performing the deep learning process which is able to identify the exact e-content from the huge volume of data with the help of the recommended features by the proposed embedded feature selection method. The exact e-content can be identified after performing the five-layer network structure for extracting the relevant features and it also can be classified by applying the Fuzzy Temporal Decision Tree for the e-learners. Finally, the proposed content recommendation system provides exact content to the e-learners according to their level of understanding and it also satisfies them by providing the exact high level contents. The experiments have been conducted for evaluating the proposed content recommendation system and compared with the existing classifier including the standard CNN. [ABSTRACT FROM AUTHOR]

If the online education platform simply presents offline course content and teaching resources, it will result in poor interaction between teachers and students and poor student learning efficiency because of the time and space barriers in distance education. At the same time, it has a one-way teaching for all students without any difference, so its personalization is poor. In order to improve the efficiency of network education knowledge recommendation, based on the traditional neural network, this study combines the actual needs of network education to construct an improved neural network-path sorting algorithm. Moreover, this study builds a network education knowledge recommendation system based on the algorithm and explores the functions of each module of the system to realize all system functions. In addition, this study performs system function tests on a complete system. The results show that the proposed algorithm is higher than the traditional recommendation algorithm in recommendation effect and recommendation efficiency, and has certain practical effects, and can provide theoretical reference for subsequent related research. [ABSTRACT FROM AUTHOR]

Meteorological hazards have great influence all over the world. An emergency plan is an important means of coping with meteorological hazards. The preparation of emergency plans needs to refer to historical emergency plans, but these are too numerous and are of uneven quality. We can alleviate these problems by means of recommender systems, which are very useful tools in many domains; however, they suffer from information overload. In this paper, we propose a Semantic-Aware Collaborative Filtering method, which is called SACF, for emergency plans recommendation to address the aforementioned challenges. It is designed to effectively present a highly targeted emergency plan recommendation list and recommend the most appropriate emergency plans for a targeted meteorological hazards event. Specifically, we use semantic knowledge to represent scenario-based meteorological hazards, including target and previous events. The search for similar events (i.e., neighbors) for a collaborative filtering recommendation algorithm is adopted. By helping to avoid both the generation of fake neighbors and also the omission of true neighbors the recommendation process is improved. Finally, extensive experiments are conducted on a real-world dataset, and the results demonstrate that SACF improves the accuracy of emergency plan recommendations. [ABSTRACT FROM AUTHOR]

Recommender systems promote sales of products and services by helping users alleviate the information overload problem. Collaborative filtering is most extensively used approach to design recommender system. The main idea of collaborative filtering is that recommendation for each active user is received by comparing with the preferences of other users who have rated the product in similar way to the active user. Matrix factorization technique is one of the most widely employed collaborative filtering techniques due to its effectiveness and efficiency in dealing with very large user-item rating matrices. One of the principal disadvantages and challenges of the collaborative filtering type algorithms is content awareness, namely, they use only people's behavior to produce recommendations and are not aware of the predicted content's metadata. In this work, we study and compare two ways of incorporating this type of content information directly into the matrix factorization approach. We extend the baseline optimization problem by two techniques. The first one penalizes item and user feature vectors with some small amounts pushing them towards each other in the latent space, and the second one makes two item and user specific latent feature vectors as similar as possible if the two items and users have similar tagging history. The results of the experiments, on the benchmark data sets, show that the proposed model has a better performance compared to some other methods. [ABSTRACT FROM AUTHOR]

Group recommender system provides suggestions for a group of users by exploring the choices of individual users of the group. Popularity of group recommender systems is increasing because many activities such as listening to music, watching movies, traveling, etc. are normally performed in groups rather individually. Group recommender systems like personal recommender systems also suffer from cold start and sparsity issues. The cold start and sparsity issues result into inaccurate recommendation computation which degrades the recommendation quality. To handle the cold start and sparsity issues in a Group Recommender System (GRS), this paper proposes to use cross domain approach and introduces Cross Domain Group Recommender System (CDGRS). The recommendations provided by trustworthy and reputed users in the group enhance the acceptance towards the presented recommendations as compared to the other individuals in the group. We have combined the social factors e.g. trust and reputation to get influential user in the group recommendation. A prototype of the system is developed for tourism domain that incorporates four sub-domains i.e. restaurants, hotels, tourist places and shopping places. The performance of CDGRS is compared with GRS. Spearman's Correlation Coefficient, MAE, RMSE, Precision, Recall and F-measure are used to find the accuracy of the generated recommendations. [ABSTRACT FROM AUTHOR]

Recommender Systems (RS) are expected to suggest the accurate goods to the consumers. Cold start is the most important challenge for RSs. Recent hybrid RSs combine ConF and ColF. We introduce an ontological hybrid RS where the ontology has been employed in its ConF part while improving the ontology structure by its ColF part. In this paper, a new hybrid approach is proposed based on the combination of demographic similarity and cosine similarity between users in order to solve the cold start problem of new user type. Also, a new approach is proposed based on the combination of ontological similarity and cosine similarity between items in order to solve the cold start problem of new item type. The main idea of the proposed method is to expand user/item profiles based on different strategies to build higher-performing profiles for users/items. The proposed method has been evaluated on a real dataset and the experimentations indicate the proposed method has the better performance comparing with the state of the art RS methods, especially in the case of the cold start. [ABSTRACT FROM AUTHOR]

With the popularity of networks and the increasing number of online users, recommender systems have suffered from the privacy leakage of sensitive information. While people enjoy recommender services, their information is exposed to the networks. To protect the privacy of users when using the recommender services, we propose a multi-level combined privacy-preserving model that maintains high accuracy of recommendation with privacy protection and alleviates the data sparsity problem. Our scheme contains two steps of recommendation. First, a multi-level combined random perturbation (MCRP) model is proposed on the client side. Our model dynamically divides multiple disturbance levels and adds noise of different ranges to the rating matrix according to Gaussian and uniform mixed disturbances. Second, on the server side, we propose a pseudo rating prediction filling (PRPF) algorithm based on the matrix factorization model. Combining the PRPF algorithm with the MCRP method significantly improves the recommender accuracy and effectively increases privacy security. Sensitive analysis and comparison experiments show that the proposed privacy method has certain advantages in security and recommender accuracy by using three publicly available datasets. [ABSTRACT FROM AUTHOR]

A fuzzy system is a system that defines input, output, and state variables on a fuzzy set and is a generalization of a deterministic system. The fuzzy system begins at the macro level and covers the fuzzy features of human brain thinking. It has advantages in describing advanced knowledge. Fuzzy sets can mimic the comprehensive conclusions of people solving fuzzy information problems, which are difficult to solve by conventional mathematical methods, so computer applications can be extended to humanities, social sciences and complex systems. In this way, it can better solve nonlinear problems and is widely used in automatic control, decision analysis, time series signal processing, economic information systems, medical diagnostic systems, and earthquake prediction systems. This paper aims to study the data mining algorithm of fuzzy systems based on fuzzy sets. By using the powerful fuzzy data modeling function of fuzzy theory, it combines with other intelligent processing methods, and makes practical use in industrial life. By comparing the application of fuzzy set data mining and algorithm, it is found that in the application state, the economic benefits of the factory are improved by 36% and the production efficiency is improved by 25% under the application of data mining and algorithm. The research data shows that the data mining and recommendation algorithms of fuzzy sets are beneficial to the development and operation of the factory. The research results show that compared with the conventional production and processing plan, the technology uses fuzzy set theory to transform the fuzzy attributes, which is more advantageous in scientific and technical systems and algorithms with its scientificity, accuracy, innovation and extensiveness. [ABSTRACT FROM AUTHOR]

When a person visits an unknown large city having multiple interesting locations, it is not so easy for him to find one location that is lively and convenient to visit in a given time-frame. To overcome such a problem, this paper proposes to make use of two technologies: smartphones, equipped with sensors for reading GPS coordinates; and multi-agent systems, providing assistance to users and gathering collective knowledge. Data collected by means of devices are analysed and organised in such a way to find locations that could be of immediate interest to people. Proposed agents gather opinions from several users, in terms of scores quantifying the level of satisfaction on visiting some place on a given time-frame. While gathering such an opinion, a solution is put into place to preserve user privacy (his location). Suggestions are made to potentially interested users by selecting for them locations according to closeness and satisfaction scores. In this approach, interesting locations emerge from the analysis of data gathered, hence scores and suggestions can be available for any large city in any place, provided that enough people hand data to the system. Moreover, such places are found dynamically according to people behaviour and preferences. [ABSTRACT FROM AUTHOR]

In the last decades, individual urban traffic flows have increased all over the world with a consequent growth of road congestion and environmental pollution. In this context, car-pooling is an interesting car-based alternative to satisfy the individual mobility demand by optimizing the car loading factor with respect to the number of passengers, provided that all the participants share trip origin and destination in the same time slot. To make the system more appealing, this paper proposes an on-demand car-pooling service adopting variable fares, on the basis of trip length and number of participants. Multi-agent, reputation and recommender system technologies in synergy with a routing algorithm have been used to this aim. Experiments on simulated data proved the potentiality of the proposed approach. [ABSTRACT FROM AUTHOR]

Recommender systems are widely used to provide users with items they may be interested in without explicitly searching. However, they suffer from low accuracy and scalability problems. Although existing clustering techniques have been incorporated to solve these inherent problems, most of them fail to achieve further improvement in recommendation accuracy because of ignoring the correlations between items and the different effects of item attributes on recommendation results. In this article, we propose a novel recommendation algorithm to alleviate these issues to a large extent. First of all, users and items are clustered into multiple cluster subsets based on user-item rating matrix and item attribute deriving from domain experts, respectively. Then we use a selection method relying on item attribute to mine candidate items and only their predictions will be calculated in the next step, which can save the computation time greatly. Furthermore, by weighting the predictions with TF-IDF (Term Frequency-Inverse Document Frequency) weights, the top-N recommendations are generated to the target user for return. Finally, comparative experiments on two real datasets demonstrate that this algorithm provides superior recommendation accuracy in terms of MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). [ABSTRACT FROM AUTHOR]

High Utility Itemset Mining (HUIM) is playing an important role in extracting meaningful knowledge that is profitable itemsets rather than their occurrence frequency. HUIM result thrives into a huge number of utility itemsets that makes it difficult in decision policy. The alternative procedure is condensed representation of HUI's that contains utility itemsets without any redundancy. Closed High Utility Itemsets (CHUI) and High Utility Generators (HUG) are lossless utility itemsets useful in recommendation systems. In this paper, we address how to extract Closed and Generator HUI's from the given dataset. For faster accessing, it is proposed hash based technique that store itemsets with the frequency, utility value, closed flag, and generator flag. Experimental results shows that it outperforms other approaches in time and space. [ABSTRACT FROM AUTHOR]

Recommender systems are decision support systems that play an important part in generating a list of product or service recommendations for users based on the past experiences and interactions. The most popular recommendation method is Collaborative Filtering (CF) that is based on the users' rating history to generate the recommendation. Although, recommender systems have been applied successfully in different areas such as e-Commerce and Social Networks, the popularity bias is still one of the challenges that needs to be further researched. Therefore, we propose a multi-level method that is based on a switching approach which solves the long tail recommendation problem (LTRP) when CF fails to find the target case. We have evaluated our method using two public datasets and the results show that it outperforms a number of bases lines and state-of-the-art alternatives with a further reduce of the recommendation error rates for items found in the long tail. [ABSTRACT FROM AUTHOR]

Collaborative filtering (CF), one of the most famous methods for building recommendation systems, recommends relevant items to users or predicting ratings of users' unknown items. Matrix factorization (MF) models are well-known model to deal with predicting the rating problem. However, the recommendation system based on matrix factorization is hard to keep up with the rapidly changing real-world data. When ratings on new users or new items come, the static model can not fit well on new data. As a consequence, if the current thing does not apply, the prediction accuracy will lose. In addition, it is a significant computation cost to rebuild the model on the whole data. To capture these changes, in this paper, we construct an online-and-offline Collaborative Filtering with a multi-method model to improve the traditional CF method, called Online SGD with Offline Knowledge (OSGDO for short). Besides, we propose a real-time incremental recommendation framework on Apache Flink, which is a scalable stream and batch data processing platform. Meanwhile, we implement our proposed method on our proposed framework. Our method proves to be good at online training when new observations arrive. And the results of experiments show that the dynamic training process we proposed is more efficient than rebuild the model on all the data. At the same time, our algorithm performs well in practice and can achieve impressive accuracy quickly when it is tested with the well-known data sets of MoviesLens and Netflix. [ABSTRACT FROM AUTHOR]

Recommender systems have been very important components to prevent people from dwelling in the overwhelming information. In this paper we analyze the difference between item-based recommendation algorithms and SVR-based collaborative filtering algorithms, and it can be found that item-based method performs much better while the data is not sparse significantly, and SVR-based method performs better while the data is dense and small. On this premise we propose a method that can combine the advantages of these two methods by predicting a small part of ratings using SVR method firstly and then predicting the rest of ratings using the item-based algorithm, which can solve the problem of data sparsity to certain extend. Finally, we evaluate our results compared with the benchmark on different datasets and prove our method's advantages. [ABSTRACT FROM AUTHOR]