Social Cognitive Theory Enhanced Diversified Recommendation

The diversified recommendation aims to satisfy a user's different preferences and hence alleviates the information cocoon problem. Previous methods focus on increasing the sample probability of interacted items in the long-tail category. However, these methods are limited by the scope of the historical interactions of a single user and confront the challenge of inadequate diversity of past interactions and unpredictable potential diverse preferences. Drawing from social cognitive theory, observational learning ability allows humans to imitate others’ behaviors when their experience is insufficient. Inspired by it, in this paper, we apply the idea of observational learning to the diversified recommendation and introduce a social Cog nitive theory enhanced D iversified R ecommendation model ( Cog4DR ). Specifically, we design a three-step observational learning pipeline, including attention, purification, and retention, corresponding to the three essential stages of observational learning. The pipeline enables the current user to observe other users who have similar tastes but also engage with unique categories, therefore exploring potential diverse preferences and achieving dual improvements in accuracy and diversity. Experimental results indicate that Cog4DR outperforms all previous approaches, demonstrating the effectiveness of imitating other users’ behaviors for diversified recommendations.