Sequential Recommendation with Dual Side Neighbor-based Collaborative Relation Modeling
This work improves sequential recommendation for users by incorporating collaborative relations and item-side dynamics, though it appears incremental as it builds on existing sequential methods.
The paper tackles the problem of sequential recommendation by addressing the lack of collaborative relations and item-side dynamics in existing models, proposing SCoRe which mines high-order collaborative information and uses both user-side and item-side sequences, achieving superior performance on three large-scale datasets.
Sequential recommendation task aims to predict user preference over items in the future given user historical behaviors. The order of user behaviors implies that there are resourceful sequential patterns embedded in the behavior history which reveal the underlying dynamics of user interests. Various sequential recommendation methods are proposed to model the dynamic user behaviors. However, most of the models only consider the user's own behaviors and dynamics, while ignoring the collaborative relations among users and items, i.e., similar tastes of users or analogous properties of items. Without modeling collaborative relations, those methods suffer from the lack of recommendation diversity and thus may have worse performance. Worse still, most existing methods only consider the user-side sequence and ignore the temporal dynamics on the item side. To tackle the problems of the current sequential recommendation models, we propose Sequential Collaborative Recommender (SCoRe) which effectively mines high-order collaborative information using cross-neighbor relation modeling and, additionally utilizes both user-side and item-side historical sequences to better capture user and item dynamics. Experiments on three real-world yet large-scale datasets demonstrate the superiority of the proposed model over strong baselines.