Attribute-aware multi-task recommendation

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 it...

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Bibliographic Details
Published in:The Journal of supercomputing 2021-05, Vol.77 (5), p.4419-4437
Main Authors: Wang, Suhua, Zhang, Lisa, Yu, Mengying, Wang, Yuling, Ma, Zhiqiang, Zhao, Yu
Format: Article
Language:English
Subjects:
Compilers
Computer Science
Deep Learning in IoT: Emerging Trends and Applications - 2019
Interpreters
Learning
Processor Architectures
Programming Languages
Recommender systems
Representations
Semantics
Social networks
Trustworthiness
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Summary: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 Multi-task 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.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-020-03440-6