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Recommender systems (RSs) have been employed for many real-world applications including search engines, social networks, and information retrieval systems as powerful intelligent techniques for creating personalized recommendations. RSs mainly utilize different data resources such as user-item ratings, tag data, and social relationships in their recommendation process to overcome the data sparsity issue. One of the main challenges is how to integrate the results obtained by these data resources. Moreover, most of the existing RSs focus on the recommendation accuracy, and do not consider the reliability of the recommendations. To address these challenges, in this study, a reliable recommendation method is developed, which employs deep neural networks and reinforcement learning. The proposed method firstly uses a stacked denoising autoencoder model to obtain the latent features from the rating, tag, and trust data. The similarity value between each pair of users is then calculated in three different aspects according to the extracted latent features. An ensemble approach is developed by integrating the three similarities using reinforcement learning to determine the final similarity value. The ensemble similarity values are used in the rating prediction process, and the reliability of the predicted ratings is evaluated using an effective measure. Finally, a recommendation strategy is developed based on the integration of the predicted ratings and their reliability values. We evaluated the effectiveness of the proposed method by conducting numerous experiments on three datasets where the results show that the proposed method is far superior to other recommendation models in making more accurate recommendations. [ABSTRACT FROM AUTHOR]

Recommender systems are popularly used to deal with an information overload issue. Existing systems mainly focus on user–item interactions and semantic information derived from metadata of users and items to improve recommendation accuracy. Item images provide useful information to infer users' individual preferences, especially for those domains where visual factors are influential such as fashion items. However, this type of information has been ignored by most previous work. To bridge this gap and meet the requirements of performance from the aspects of Accuracy, Scalability, and Explainability evaluation metrics, this paper proposes a scalable and explainable visually-aware recommender system framework called SEV-RS. This framework contains a visually-augmented heterogeneous information network, a scalable meta-path feature extraction method for multi-hop relations, and a shallow explainable meta-path based Collaborative Filtering recommendation approach. We compared SEV-RS with the state-of-the-art models such as the deep learning model using Graph Attention Network on two real-world datasets and one synthetic dataset. The results show that SEV-RS produced more accurate and more explainable recommendations. Also, SEV-RS has substantially less computational time than the compared deep learning models. [ABSTRACT FROM AUTHOR]

In recommender systems, several kinds of user feedback with time stamps are collected and used for recommendation, which is called the time heterogeneous feedback recommendation problem. The existing recommendation methods can handle only one kind of feedback or ignore the time stamps of the feedback. To solve the time heterogeneous feedback recommendation problem, in this paper, we propose a recurrent neural network model to predict the probability that the user will access an item given the time heterogeneous feedback of this user. To our best knowledge, it is the first time to solve the time heterogeneous feedback recommendation problem by deep neural network model. The proposed model is learned by back propagation algorithm and back propagation through time algorithm. The comparison results on four real-life datasets indicate that the proposed method outperforms the compared state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

Data sparsity and cold start are two critical issues which need to be addressed in recommender systems (RSs). Currently, most methods address these issues by applying user history files or some side information to improve the user model and complete the rating matrix. However, such methods cannot perform well when labeled data is scarce or unavailable. In this paper, we propose a dual learning-based recommendation approach (DLRA). DLRA can trigger initial recommendation and improve the quality of recommendations by using the duality characteristics of RSs, even when the available labeled information is scarce. Specifically, DLRA regards the recommendation task as two independent subtasks — primal task and dual task, and these two tasks show strong duality in DLRA. The primal task is item-centered which aims to find users who can rate high for items, while the dual task is user-centered that aims to recommend the most favorite items to users. These two tasks have strong dualities in terms of the recommendation space, selection probability and recommendation basis. Based on these dualities, we design three dual learning strategies to couple the whole recommendation process and realize the self-tuning and self-improvement of each task model, and finally optimize the whole recommendation model. Based on the dataset of Movielens and BookCrossing, we simulate data sparsity and cold start recommendation scenarios, the experimental results show that DLRA achieves substantial improvement when the labeled data is scare, and it outperforms other hybrid recommendation approaches and deep learning strategies with a smaller predictive error as well as better recommendation accuracy. [ABSTRACT FROM AUTHOR]

Recommender systems aim to maximize the overall accuracy for long-term recommendations. However, most of the existing recommendation models adopt a static view, and ignore the fact that recommendation is a dynamic sequential decision-making process. As a result, they fail to adapt to new situations and suffer from the cold-start problem. Although sequential recommendation methods have been gaining attention recently, the objective of long-term recommendation still has not been explicitly addressed since these methods are developed for short-term prediction situations. To overcome these problems, we propose a novel top-N interactive recommender system based on deep reinforcement learning. In our model, the processes of recommendation are viewed as Markov decision processes (MDP), wherein the interactions between agent (recommender system) and environment (user) are simulated by the recurrent neural network (RNN). In addition, reinforcement learning is employed to optimize the proposed model for the purpose of maximizing long-term recommendation accuracy. Experimental results based on several benchmarks show that our model significantly outperforms previous top-N methods in terms of Hit-Rate and NDCG for the long-term recommendation, and can be applied to both cold-start and warm-start scenarios. • A novel top-N interactive recommender system based on deep reinforcement learning is proposed. • The interactions between recommender system and users are simulated by recurrent neural networks. • The proposed model can deal with both cold-start and warm-start scenarios. • Reinforcement learning and supervised learning are employed to optimize the proposed model for long-term recommendation accuracy. • Experiments and comparisons are conducted to show the merits of the proposed model. [ABSTRACT FROM AUTHOR]

For many years user textual reviews have been exploited to model user/item representations for enhancing the performance of the Recommender System (RS). However, the traditional methods of the RSs basically rely on the static user/item feature vectors and ignore the fine-grained user–item interactions which could affect the accuracy of the RSs. Thus, this paper proposes a RS model that exploits neural attention techniques to learn adaptive user/item representations and fine-grained user–item interaction for enhancing the accuracy of the item recommendation. An attentive pooling layer is first designed based on the Convolutional Neural Network (CNN) to learn the adaptive latent features of the user/item from reviews. A mutual attention network technique is then introduced for modelling the fine-grained user–item​ interaction to enable jointly capturing the most informative features at the higher granularity. Finally, a prediction layer is then applied for the final prediction based on the adaptive user/item representation and the user/item importance. We extensively conduct a series of experiments using Amazon and Yelp reviews, and the results demonstrate that our proposed model performs better than the existing methods in terms of both rating prediction and ranking performances. Statistical paired test show that all the performance improvements are significant at p<0.05. [ABSTRACT FROM AUTHOR]

Manually selecting appropriate scholarly venues is becoming a tedious and time-consuming task for researchers due to many reasons that include relevance, scientific impact, and research visibility. Sometimes, high-quality papers get rejected due to mismatch between the area of the paper and the scope of the journal. Recommending appropriate academic venues can, therefore, enable researchers to identify and take part in relevant conferences and publish in journals that matter the most. A researcher may certainly know of a few leading venues for her specific field of interest. However, a venue recommendation system becomes particularly helpful when exploring a new domain or when more options are needed. Due to high dimensionality and sparsity of text data, and complex semantics of the natural language, journal identification presents difficult challenges. We propose a novel and unified architecture that contains a Bi-directional LSTM (Bi-LSTM) and a Hierarchical Attention Network (HAN) to address the above problems. We call the proposed architecture modularized Hierarchical Attention-based Scholarly Venue Recommender system (HASVRec), which only requires the abstract, title, keywords, field of study, and author of a new paper along with its past publication record to recommend scholarly venues. Experiments on the DBLP-Citation-Network V11 dataset exhibit that our proposed approach outperforms several state-of-the-art methods in terms of accuracy, F 1 , nDCG, MRR, average venue quality, and stability. [ABSTRACT FROM AUTHOR]

Recommender systems have been in existence everywhere with most of them using single ratings in prediction. However, multi-criteria predictions have been proved to be more accurate. Recommender systems have many techniques; collaborative filtering is one of the most commonly used. Deep learning has achieved impressive results in many domains such as text, voice, and computer vision. Lately, deep learning for recommender systems began to gain massive interest, and many recommendation models based on deep learning have been proposed. However, as far as we know, there is not yet any study which gathers multi-criteria recommendation and collaborative filtering with deep learning. In this work, we propose a novel multi-criteria collaborative filtering model based on deep learning. Our model contains two parts: in the first part, the model obtains the users and items' features and uses them as an input to the criteria ratings deep neural network, which predicts the criteria ratings. Those criteria ratings constitute the input to the second part, which is the overall rating deep neural network and is used to predict the overall rating. Experiments on a real-world dataset demonstrate that our proposed model outperformed the other state-of-the-art methods, and this provides evidence pointing to the success of employing deep learning and multi-criteria in recommendation systems. [ABSTRACT FROM AUTHOR]