Group-Aware Interest Disentangled Dual-Training for Personalized
  Recommendation
Xiaolong Liu Liangwei Yang Zhiwei Liu Xiaohan Li Mingdai Yang Chen Wang Philip S. Yu
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.