# A Probabilistic Model for the Cold-Start Problem in Rating Prediction using Click Data

## TL;DR

This paper introduces a probabilistic item embedding model using click data to effectively address the cold-start problem in rating prediction, outperforming existing methods especially in sparse data scenarios.

## Contribution

It proposes a novel probabilistic model that leverages abundant click data to learn item representations, enhancing cold-start recommendation performance.

## Key findings

- Effective in recommending items with no prior ratings
- Outperforms competing methods in sparse data conditions
- Demonstrates success on three real-world datasets

## Abstract

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.