Tag Tree-Guided Multi-grained Alignment for Multi-Domain Short Video Recommendation
Abstract: Multi-Domain Rcommendation (MDR) aims to leverage data from multiple domains to enhance recommendations through overlapping users or items. However, extreme overlap sparsity in some applications makes it challenging for existing multi-domain models to capture domain-shared information. Moreover, the sparse overlapping users or items result in a cold start problem in every single domain and hinder feature space alignment of different domains, posing a challenge for joint optimization across domains. However, in multi-domain short video recommendation, we identify two key characteristics that can greatly alleviate the overlapping sparsity issue and enable domain alignment. (1) The following relations between users and publishers exhibit strong preferences and a concentration effect, as popular video publishers, who constitute a small portion of all users, are followed by a majority of users across various domains. (2) The tag tree structure shared by all videos can help facilitate multi-grained alignment across multiple domains. Based on these characteristics, we propose tag tree-guided multi-grained alignment with publisher enhancement for multi-domain video recommendation. Our model integrates publisher and tag nodes into the user-video bipartite graph as central nodes, enabling user and video alignment across all domains via graph propagation. Then, we propose a tag tree-guided decomposition method to obtain hierarchical graphs for multi-grained alignment. Further, we design tree-guided contrastive learning methods to capture the intra-level and inter-level node relations respectively. Finally, extensive experiments on two real-world short video recommendation datasets demonstrate the effectiveness of our model.
Primary Subject Area: [Engagement] Multimedia Search and Recommendation
Secondary Subject Area: [Experience] Multimedia Applications
Relevance To Conference: Video is a form of multimodal data that encompasses images, audio, and text. In order to make video recommendations that are tailored to the user's preferences, it is necessary to take into account not only the data itself but also the user and the context. Due to diverse needs of users and commercial considerations, online video platforms generally provide multiple scenarios of recommendation services. To make accurate recommendations for all domains, we present a novel approach that provides user-centric recommendations for multimedia videos across multiple domains, making a valuable contribution to online multimedia recommendation.
Submission Number: 5646
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