Tag2Vec: Tag Embedding for Top-N Recommendation

Abstract

It is already well known that recommender systems usually suffer from data sparsity issue of user-item interactions. However, representation learning can efficiently measure correlations between objects, which presents an unprecedented opportunity to alleviate this issue. In this paper, we propose a new distributional vector space model, Tag2Vec, for capturing meaningful relationships of users and items to improve the performance of recommender systems. First, we represent users and items as vectors respectively using tag embedding. With this innovative representation, the semantic relationships between users and items can be captured. To be specific, tag2vec learns representations of users and items in low-dimensional space from user-tag-item interactions using the skip-gram model. Second, we measure similarity between both users and items, and collaborative filtering can then be performed in the learned embedding space. To evaluate the performance of Tag2Vec, we conduct extensive experiments with two real world datasets for Top-N recommendation tasks. The results demonstrate that our proposed method significantly outperforms existing approaches.