Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation

--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. 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. Recommender systems (RS) [1]-[3] are becoming indispensable to web applications owing to their prominent ability in user retention [4] and commercial conversion [5]. Data sparsity and cold-start problems [6]-[8] are still obstacles that most RS suffer from.