Review recommendation: personalized prediction of the quality of online reviews.

ABSTRACTThe problem of identifying high quality and helpful reviews automatically has attracted many attention recently. Current methods assume that the helpfulness of a review is independent from the readers of that review. However, we argue that the quality of a review may not be the same for different users. In this paper, we employ latent factor models to address this problem. We evaluate the proposed models using a real life database from Epinions.com. The experiments demonstrate that the latent factor models outperform the state-of-the-art approaches and confirms that the helpfulness of a review is indeed not the same for all users.