Cross-Modal Manifold Propagation for Image Recommendation

The growing complex user intention gap and information overload are obstacles for users to access the desired content. User interactions and the involved content indicate rich evidence of users' interests. It is required to investigate interaction characters over user interest and information distribution, and this alleviates information overload for personalized recommendation. Therefore, this work explores user interests with interactions and visual information from users' historical records for image recommendation. This paper introduces cross-modal manifold propagation (CMP) for personalized image recommendation. CMP investigates the trend of user preferences by propagating users' historical records along with users' interest distribution, which produces personalized interest-aware image candidates according to user interests. CMP simultaneously leverages visual distribution to spread users' visual records relying on the dense semantic visual manifold. Visual manifold propagation estimates detailed semantic-level user-image correlations for ranking candidate images in recommendations. In the proposed CMP, both user interest manifold and images' visual manifold compensate each other in propagating users' records to predict users' interaction. Experimental results illustrate the effectiveness of collaborative user-image propagation of CMP for personalized image recommendation. Performance improved by more than 20% compared to that of existing baselines.