A Probabilistic Model for the Cold-Start Problem in Rating Prediction using Click Data
This addresses the cold-start problem in recommendation systems for users and platforms, offering an incremental improvement by leveraging abundant click data.
The paper tackles the cold-start problem in collaborative filtering by proposing EMB-MF, a model that combines probabilistic item embeddings from click data with matrix factorization for rating prediction. Experiments on three real-world datasets show it effectively recommends items with no previous ratings and outperforms competing methods, especially in sparse data scenarios.
One of the most efficient methods in collaborative filtering is matrix factorization, which finds the latent vector representations of users and items based on the ratings of users to items. However, a matrix factorization based algorithm suffers from the cold-start problem: it cannot find latent vectors for items to which previous ratings are not available. This paper utilizes click data, which can be collected in abundance, to address the cold-start problem. We propose a probabilistic item embedding model that learns item representations from click data, and a model named EMB-MF, that connects it with a probabilistic matrix factorization for rating prediction. The experiments on three real-world datasets demonstrate that the proposed model is not only effective in recommending items with no previous ratings, but also outperforms competing methods, especially when the data is very sparse.