TagEmbedSVD: Leveraging Tag Embeddings for Cross-Domain Collaborative Filtering

Abstract

Cross-Domain Collaborative Filtering (CDCF) mitigates data sparsity and cold-start issues present in conventional recommendation systems by exploiting and transferring knowledge from related domains. Leveraging user-generated tags (e.g. ancient-literature, military-history) for bridging the related domains is becoming a popular way for enhancing personalized recommendations. However, existing tag based models bridge the domains based on common tags between domains and their co-occurrence frequencies. This results in capturing the syntax similarities between the tags and ignoring the semantic similarities between them. In this work, to address these, we propose TagEmbedSVD, a tag-based CDCF model to cross-domain setting. TagEmbedSVD makes use of the pre-trained word embeddings (word2vec) for tags to enhance personalized recommendations in the cross-domain setting. Empirical evaluation on two real-world datasets demonstrates that our proposed model performs better than the existing tag based CDCF models.