Deep Learning based Recommender System: A Survey and New Perspectives

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.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

An explainable recommender system based on semantically-aware matrix factorization.

Collaborative Filtering techniques provide the ability to handle big and sparse data to predict the ratings for unseen items with high accuracy. Matrix factorization is an accurate collaborative filtering method used to predict user preferences. However, it is a black box system that recommends items to users without being able to explain why. This is due to the type of information these systems use to build models. Although rich in information, user ratings do not adequately satisfy the need for explanation in certain domains. White box systems, in contrast, can, by nature, easily generate explanations. However, their predictions are less accurate than sophisticated black box models. Recent research has demonstrated that explanations are an essential component in bringing the powerful predictions of big data and machine learning methods to a mass audience without a compromise in trust. Explanations can take a variety of formats, depending on the recommendation domain and the machine learning model used to make predictions. Semantic Web (SW) technologies have been exploited increasingly in recommender systems in recent years. The SW consists of knowledge graphs (KGs) providing valuable information that can help improve the performance of recommender systems. Yet KGs, have not been used to explain recommendations in black box systems. In this dissertation, we exploit the power of the SW to build new explainable recommender systems. We use the SW\u27s rich expressive power of linked data, along with structured information search and understanding tools to explain predictions. More specifically, we take advantage of semantic data to learn a semantically aware latent space of users and items in the matrix factorization model-learning process to build richer, explainable recommendation models. Our off-line and on-line evaluation experiments show that our approach achieves accurate prediction with the additional ability to explain recommendations, in comparison to baseline approaches. By fostering explainability, we hope that our work contributes to more transparent, ethical machine learning without sacrificing accuracy

Deep learning-based implicit feedback recommendation

Recommender systems are of vital importance, in the era of the Web, to address the problem of information overload. It can benefit both users by recommending personalized interesting items and service providers by increasing their site traffic. Plenty of use cases have emerged as applied recommender systems, including but not limited to multimedia recommendation (e.g., news, movies, music, and videos) and e-commerce recommendation. A recommendation agent can be trained from user-item interaction data which can be categorized as explicit feedback and implicit feedback. Compared with explicit ratings which depict the user preference explicitly, implicit feedback data like clicks, purchases, and dwell time is more prevalent in the real-world scenario. On the other hand, deep learning has achieved great success recently due to the high model expressiveness and fidelity. In this thesis, we investigate deep learning techniques for recommendation from implicit feedback data. We focus on two learning perspectives: deep supervised learning and deep reinforcement learning. Supervised learning tries to infer knowledge from implicit historical interactions. From this perspective, two models namely Convolutional Factorization Machines (CFM) and Relational Collaborative Filtering (RCF) are proposed. CFM tackles the implicit user-item interactions with side information as feature vectors and utilizes convolutional neural networks to learn high-order interaction signals. RCF considers multiple item relations into the recommendation model and tackles the implicit feedback as relation-enriched data. The two models investigate deep learning techniques for recommendation by tackling the data as two different structures: feature vectors and relations. Experimental results demonstrate that the proposed deep learning models are effective to improve the recommendation accuracy. Besides, RCF also helps to provide explainable recommendation and get a better comprehension of user behaviors. Reinforcement learning is reward-driven and focuses on long-term optimization in a whole interaction session, which conforms more with the objective of recommender systems. From this perspective, we first formulate the next-item recommendation task from implicit feedback data as a Markov Decision Process (MDP). Then we analyzed that directly utilizing reinforcement learning algorithms for recommendation is infeasible due to the challenge of pure off-policy setting and lack of negative reward signals. To address the problems, we proposed Self-Supervised Q-learning (SQN) and Self-Supervised Actor-Critic (SAC). The key insight is to combine reinforcement learning with supervised learning and perform knowledge transfer between the two components. Based on SQN and SAC, we further proposed Self-Supervised Negative Q-learning (SNQN) and Self-Supervised Advantage Actor-Critic (SA2C) to introduce the negative sampling strategy to enhance the learning of the reinforcement component. Experimental results demonstrate that the proposed learning frameworks are effective when integrated with different existing base models. Moreover, we show that combining supervised learning and reinforcement learning is a promising direction for future recommender systems. In that case, reinforcement learning introduces reward-driven objectives and long-term optimization perspectives into supervised learning while supervised learning helps to improve the data efficiency for reinforcement learning