Attribute-aware multi-task recommendation
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Attribute‑aware multi‑task recommendation Suhua Wang1 · Lisa Zhang1 · Mengying Yu1 · Yuling Wang1 · Zhiqiang Ma1 · Yu Zhao1 Accepted: 21 September 2020 © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract User-item rating interactions in the recommender system have a deep potential connection with the friend relationships in the social network. In short, users who like the same kind of items may be potential friends in social network, and vice versa, friends in social networks tend to like similar items. Although the above-mentioned two kinds of interactive information can complement and inspire each other, either of them is sparse, which is still not enough to make accurate recommendations. In order to make up for this defect, we then mine useful information from attribute information, learning more informative node representation. In this paper, we explore attribute learning and mutual utilization, complementation and inspiration between social data and rating data. We propose a generic Attribute-Aware Multitask Recommendation framework (AAMR) for rating prediction and social prediction, which learns representations for users and items by preserving both rating data and social data and attribute information, so as to conduct both rating prediction and trust relationship prediction tasks. Because many users are both in the rating matrix and in social networks, in the common learning, the two tasks will share the embedding of users, which makes the social data and rating data enrich each other’s semantics and alleviate each other’s sparsity. To justify our proposal, we conduct extensive experiments on a real-world dataset. Compared to the state-of-the-art rating and trust prediction approaches, AAMR can learn more informative representations, achieving substantial gains on both tasks. Keywords Attribute-aware · Social network · Multi-task · Deep learning · Collaborative filtering
* Zhiqiang Ma [email protected] Extended author information available on the last page of the article
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1 Introduction With the rapid development of Internet technology and industry, the number of servers and web pages accessing the Internet also shows an exponential upward trend, and a great deal of information is presented to us at the same time. For example, there are tens of thousands of items on Netflix, millions of books on Amazon and billions of products on Alibaba. In recent years, as an important means of information filtering, recommender systems are the most effective solution to overcome information overload. Rating prediction is one of the most important task of recommender system [1], which infers unknown ratings based on previous ratings, so as to achieve the recommendation. However, rating information is always sparse, sparse data and cold-start users are often barriers to providing high-quality recommendations. To address such issues, researchers consider combining ratings information with social data [2–13]. Social networks are an important platform for p
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