Semi-supervised Factorization Machines for Review-Aware Recommendation

Textual reviews, as a useful supplementary of the interaction data, has been widely used to enhance the performance of recommender systems, especially when the interaction data is sparse. However, existing solutions to review-aware recommendation only focus on learning more informative features from reviews, yet ignore the insufficient number of training examples, resulting in limited performance improvements. To this end, we propose a co-training style semi-supervised review-aware recommendation model, called Collaborative Factorization Machines (CoFM), to augment the training dataset as well as increase its informativeness. Our CoFM employs two FMs as base predictors, each of which labels unlabeled examples for its peer predictor in the learning process. Specifically, a user-leaded FM and an item-leaded FM are separately built using different reviews to increase the diversity between two predictors. Furthermore, to exploit unlabeled data safely, the labeling confidence is estimated through validating the influence of the labeling of unlabeled examples on the labeled ones. The final prediction is made by linearly blending the outputs of two predictors. Extensive experiments on three real-world benchmarks demonstrate the superiority of CoFM over several state-of-the-art review-aware and semi-supervised recommendation schemes.