On Deep Learning for Trust-Aware Recommendations in Social Networks
01 May 2017-IEEE Transactions on Neural Networks (IEEE Trans Neural Netw Learn Syst)-Vol. 28, Iss: 5, pp 1164-1177
TL;DR: A two-phase recommendation process is proposed to utilize deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user’s trusted friendships.
Abstract: With the emergence of online social networks, the social network-based recommendation approach is popularly used. The major benefit of this approach is the ability of dealing with the problems with cold-start users. In addition to social networks, user trust information also plays an important role to obtain reliable recommendations. Although matrix factorization (MF) becomes dominant in recommender systems, the recommendation largely relies on the initialization of the user and item latent feature vectors. Aiming at addressing these challenges, we develop a novel trust-based approach for recommendation in social networks. In particular, we attempt to leverage deep learning to determinate the initialization in MF for trust-aware social recommendations and to differentiate the community effect in user’s trusted friendships. A two-phase recommendation process is proposed to utilize deep learning in initialization and to synthesize the users’ interests and their trusted friends’ interests together with the impact of community effect for recommendations. We perform extensive experiments on real-world social network data to demonstrate the accuracy and effectiveness of our proposed approach in comparison with other state-of-the-art methods.
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13 May 2019
TL;DR: This paper provides a principled approach to jointly capture interactions and opinions in the user-item graph and proposes the framework GraphRec, which coherently models two graphs and heterogeneous strengths for social recommendations.
Abstract: In recent years, Graph Neural Networks (GNNs), which can naturally integrate node information and topological structure, have been demonstrated to be powerful in learning on graph data. These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning latent factors of users and items is the key. However, building social recommender systems based on GNNs faces challenges. For example, the user-item graph encodes both interactions and their associated opinions; social relations have heterogeneous strengths; users involve in two graphs (e.g., the user-user social graph and the user-item graph). To address the three aforementioned challenges simultaneously, in this paper, we present a novel graph neural network framework (GraphRec) for social recommendations. In particular, we provide a principled approach to jointly capture interactions and opinions in the user-item graph and propose the framework GraphRec, which coherently models two graphs and heterogeneous strengths. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed framework GraphRec.
1,111 citations
Cites background from "On Deep Learning for Trust-Aware Re..."
...dressed the task of cross-domain social recommendations for ranking metric, which is different from traditional social recommender systems. Most related to our task with neural networks includes DLMF [6] and DeepSoR [8]. DLMF [6] used auto-encoder on ratings to learn representation for initializing an existing matrix factorization. A two-phase trust-aware recommendation process is proposed to utilize...
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TL;DR: A comprehensive review of recent research efforts on deep learning-based recommender systems is provided in this paper, along with a comprehensive summary of the state-of-the-art.
Abstract: With the growing volume of online information, recommender systems have been an effective strategy to overcome information overload. The utility of recommender systems cannot be overstated, given their widespread adoption in many web applications, along with their potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also to the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. The field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning-based recommender systems. More concretely, we provide and devise a taxonomy of deep learning-based recommendation models, along with a comprehensive summary of the state of the art. Finally, we expand on current trends and provide new perspectives pertaining to this new and exciting development of the field.
1,070 citations
TL;DR: A taxonomy of deep learning-based recommendation models is provided and a comprehensive summary of the state of the art is provided, along with new perspectives pertaining to this new and exciting development of the field.
Abstract: With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.
560 citations
Additional excerpts
...[40, 41, 49, 104, 105] [26, 65, 118] [4, 49, 61, 126, 135] [32, 64, 120, 138] [10, 91] [3, 102] (Diversity) [22] (Coverage)...
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...AE (25) [82], [90], [99], [129] [98], [135], [144], [102] [84] [113], [63], [62], [24] [112] , [145], [3], [136] [139], [122], [123], [106] [7], [146], [107], [22]...
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TL;DR: This study provides a comprehensive review of deep learning-based recommendation approaches to enlighten and guide newbie researchers interested in the subject.
Abstract: Recommender systems are effective tools of information filtering that are prevalent due to increasing access to the Internet, personalization trends, and changing habits of computer users Although existing recommender systems are successful in producing decent recommendations, they still suffer from challenges such as accuracy, scalability, and cold-start In the last few years, deep learning, the state-of-the-art machine learning technique utilized in many complex tasks, has been employed in recommender systems to improve the quality of recommendations In this study, we provide a comprehensive review of deep learning-based recommendation approaches to enlighten and guide newbie researchers interested in the subject We analyze compiled studies within four dimensions which are deep learning models utilized in recommender systems, remedies for the challenges of recommender systems, awareness and prevalence over recommendation domains, and the purposive properties We also provide a comprehensive quantitative assessment of publications in the field and conclude by discussing gained insights and possible future work on the subject
294 citations
TL;DR: A deep learning based collaborative filtering framework, namely, deep matrix factorization (DMF), which can integrate any kind of side information effectively and handily, and implicit feedback embedding (IFE) is proposed, which converts the high-dimensional and sparse implicit feedback information into a low-dimensional real-valued vector retaining primary features.
Abstract: Automatic recommendation has become an increasingly relevant problem to industries, which allows users to discover new items that match their tastes and enables the system to target items to the right users. In this paper, we propose a deep learning (DL) based collaborative filtering framework, namely, deep matrix factorization (DMF), which can integrate any kind of side information effectively and handily. In DMF, two feature transforming functions are built to directly generate latent factors of users and items from various input information. As for the implicit feedback that is commonly used as input of recommendation algorithms, implicit feedback embedding (IFE) is proposed. IFE converts the high-dimensional and sparse implicit feedback information into a low-dimensional real-valued vector retaining primary features. Using IFE could reduce the scale of model parameters conspicuously and increase model training efficiency. Experimental results on five public databases indicate that the proposed method performs better than the state-of-the-art DL-based recommendation algorithms on both accuracy and training efficiency in terms of quantitative assessments.
172 citations
Cites background from "On Deep Learning for Trust-Aware Re..."
...For instance, social recommendation [20]–[22] utilizes social relations or trust relations; content-based recommendation [1], [23] employs the content of items or users such as the text introduction, video content, etc....
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...SCRs can be summarized as several primary models: restricted Boltzmann machine (RBM) [28], autoencoder (AE) [20], [29]–[31], neural autoregressive distri-...
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"On Deep Learning for Trust-Aware Re..." refers background in this paper
...In this section, we investigate how the social trust networks affect users’ decisions on selecting items, and extend the basic MF model by involving the recommendations of trusted friends....
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