Journal
ACM/SIGIR PROCEEDINGS 2018
Volume -, Issue -, Pages 645-654Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3209978.3209998
Keywords
Recommender Systems; Group Recommendation; Attention Mechanism; Neural Collaborative Filtering; Cold-Start Problem
Categories
Funding
- Fundamental Research Funds for the Central Universities
- National Natural Science Foundation of China [61702176]
- Hunan Provincial Natural Science Foundation of China [2017JJ3038]
- Outstanding Youth Science Foundation [61722204]
- National Research Foundation, Prime Minister's Office, Singapore under its IRC@SG Funding Initiative
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Due to the prevalence of group activities in people's daily life, recommending content to a group of users becomes an important task in many information systems. A fundamental problem in group recommendation is how to aggregate the preferences of group members to infer the decision of a group. Toward this end, we contribute a novel solution, namely AGREE (short for Attentive Group REcommEndation), to address the preference aggregation problem by learning the aggregation strategy from data, which is based on the recent developments of attention network and neural collaborative filtering (NCF). Specifically, we adopt an attention mechanism to adapt the representation of a group, and learn the interaction between groups and items from data under the NCF framework. Moreover, since many group recommender systems also have abundant interactions of individual users on items, we further integrate the modeling of user-item interactions into our method. Through this way, we can reinforce the two tasks of recommending items for both groups and users. By experimenting on two real-world datasets, we demonstrate that our AGREE model not only improves the group recommendation performance but also enhances the recommendation for users, especially for cold-start users that have no historical interactions individually.
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