DTGCF: Diversified Tag-Aware Recommendation with Graph Collaborative Filtering

In tag-aware recommender systems, users are strongly encouraged to utilize arbitrary tags to mark items of interest. These user-defined tags can be viewed as a bridge linking users and items. Most tag-aware recommendation models focus on improving the accuracy by introducing ingenious design or complicated structures to handle the tagging information appropriately. Beyond accuracy, diversity is considered to be another important indicator affecting the user satisfaction. Recommending more diverse items will provide more interesting items and commercial sales. Therefore, we propose a diversified tag-aware recommendation model based on graph collaborative filtering. The proposed model establishes a generic graph collaborative filtering framework tailored for tag-aware recommendations. To promote diversity, we adopt two modules: personalized category-boosted negative sampling to select a certain proportion of similar but negative items as negative samples for training, and adversarial learning to make the learned item representation category-free. To improve accuracy, we conduct a two-way TransTag regularization to model the relationship among users, items, and tags. Blending these modules into the proposed framework, we can optimize both the accuracy and diversity concurrently in an end-to-end manner. Experiments on Movielens datasets show that the proposed model can provide diverse recommendations while maintaining a high level of accuracy.

Automated summary

The diversified tag-aware recommendation model based on graph collaborative filtering aims to optimize both accuracy and diversity by introducing personalized category-boosted negative sampling and adversarial learning modules. Experiments on the Movielens dataset demonstrate that the model can provide diverse recommendations while maintaining a high level of accuracy.