Intent-guided Bilateral Long and Short-Term Information Mining with Contrastive Learning for Sequential Recommendation

The current sequential recommendation systems mainly focus on mining information related to users to make personalized recommendations. However, there are two subjects in the user historical interaction sequence: users and items. We believe that mining sequence information only from the users' perspective is limited, ignoring effective information from the perspective of items, which is not conducive to alleviating the data sparsity problem. To explore potential links between items and use them for recommendation, we propose Intent-guided Bilateral Long and Short-Term Information Mining with Contrastive Learning for Sequential Recommendation (IBLSRec), which interpretively integrates three kinds of information mined from the sequence: user preferences, user intentions, and potential relationships between items. Specifically, we model the potential relationships between interactive items from a long-term and short-term perspective. The short-term relationship between items is regarded as noise; the long-term relationship between items is regarded as a stable common relationship and integrated with the user's personalized preferences. In addition, user intent is used to guide the modeling of user preferences to refine the representation of user preferences further. A large number of experiments on four real data sets validate the superiority of our model.