Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation
This work addresses data sparsity and cold-start problems for users in personalized recommender systems, but it is incremental as it builds on existing group-based recommendation methods.
The paper tackles the problem of data sparsity and cold-start in personalized recommender systems by leveraging users' group participation on social platforms as side information, proposing IGRec which uses interest-based group representations to improve recommendation accuracy, with experiments on three datasets showing it effectively alleviates these issues.
Personalized recommender systems aim to predict users' preferences for items. It has become an indispensable part of online services. Online social platforms enable users to form groups based on their common interests. The users' group participation on social platforms reveals their interests and can be utilized as side information to mitigate the data sparsity and cold-start problem in recommender systems. Users join different groups out of different interests. In this paper, we generate group representation from the user's interests and propose IGRec (Interest-based Group enhanced Recommendation) to utilize the group information accurately. It consists of four modules. (1) Interest disentangler via self-gating that disentangles users' interests from their initial embedding representation. (2) Interest aggregator that generates the interest-based group representation by Gumbel-Softmax aggregation on the group members' interests. (3) Interest-based group aggregation that fuses user's representation with the participated group representation. (4) A dual-trained rating prediction module to utilize both user-item and group-item interactions. We conduct extensive experiments on three publicly available datasets. Results show IGRec can effectively alleviate the data sparsity problem and enhance the recommender system with interest-based group representation. Experiments on the group recommendation task further show the informativeness of interest-based group representation.