Interact and Decide: Medley of Sub-Attention Networks for Effective Group Recommendation
This paper proposes Medley of Sub-Attention Networks (MoSAN), a new novel neural architecture for the group recommendation task. Group-level recommendation is known to be a challenging task, in which intricate group dynamics have to be considered. As such, this is to be contrasted with the standard...
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Zusammenfassung: | This paper proposes Medley of Sub-Attention Networks (MoSAN), a new novel
neural architecture for the group recommendation task. Group-level
recommendation is known to be a challenging task, in which intricate group
dynamics have to be considered. As such, this is to be contrasted with the
standard recommendation problem where recommendations are personalized with
respect to a single user. Our proposed approach hinges upon the key intuition
that the decision making process (in groups) is generally dynamic, i.e., a
user's decision is highly dependent on the other group members. All in all, our
key motivation manifests in a form of an attentive neural model that captures
fine-grained interactions between group members. In our MoSAN model, each
sub-attention module is representative of a single member, which models a
user's preference with respect to all other group members. Subsequently, a
Medley of Sub-Attention modules is then used to collectively make the group's
final decision. Overall, our proposed model is both expressive and effective.
Via a series of extensive experiments, we show that MoSAN not only achieves
state-of-the-art performance but also improves standard baselines by a
considerable margin. |
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DOI: | 10.48550/arxiv.1804.04327 |