Collaborative Preference Embedding against Sparse Labels

Living in the era of the internet, we are now facing a big bang of online information. As a consequence, we often find ourselves troubling with hundreds and thousands of options before making a decision. As a way to improve the quality of users’ online experience, the Recommendation System aims to facilitate personalized online decision-making processes by predicting users’ responses toward different options. However, the vast majority of the literature in the field merely focuses on datasets with sufficient amount of samples. Different from the traditional methods, we propose a novel method named Collaborative Preference Embedding (CPE), which directly deals with sparse and insufficient user preference information. Specifically, we represent the intrinsic pattern of users/items with a high-dimensional embedding space. On top of this embedding space, we design two schemes specifically against the limited generalization ability in terms of sparse labels. On the one hand, we construct a margin function that could indicate the consistency between the embedding space and the true user preference. From the margin theory point-of-view, we then propose a generalization enhancement scheme for sparse and insufficient labels via optimizing the margin distribution. On the other hand, regarding the embedding as a code for a user/item, we then improve the generalization ability from the coding point of view. Specifically, we leverage a compact embedding space by reducing the dependency across different dimensions of a code (embedding). Finally, extensive experiments on a number of real-world datasets demonstrate the superior generalization performance of the proposed algorithm.