Multi-perspective Information and Multi-task Contrastive Learning for Sequential Recommendations

Sequential recommendations play a crucial role in modern recommender systems because they capture users’ dynamic interests based on his/her historical interactions. Despite the progress of existing methods in sequential recommendation, they only focus on modeling user interaction sequences but ignore multi-perspective sequences information when modeling user preferences, leading to ineffective modeling user preferences. In other words, these methods lack sufficient semantic information to effectively model the one, who considers multi-perspective factors when purchasing an item. To solve this problem, we propose a multi-perspective information and multi-task contrastive learning framework for sequential recommendation, named DIML. DIML can model user preferences by using multi-perspective information, and multi-task contrastive learning can alleviate data sparsity and enhance data quality. Specifically, we first design the information encoding layer, which enriches item semantic information from multi-perspective sequential information to model user preferences more precisely. Then we design a multi-task contrastive learning module to enhance the data quality of multi-perspective information. Finally, experiments on Taobao, JD and MovieLens datasets show that our model is better than the comparison baselines in terms of evaluation metric NDCG, HR and MRR.