Sparsity Regularization For Cold-Start Recommendation
This work improves recommendation systems for new users by mitigating over-fitting on warm users, though it appears incremental as it builds on existing GAN-based methods.
The paper tackles the problem of cold-start recommendation by addressing training instability due to sparse user purchase behavior, introducing a hybrid system that combines collaborative filtering and content-based recommendation to achieve state-of-the-art results on two datasets.
Recently, Generative Adversarial Networks (GANs) have been applied to the problem of Cold-Start Recommendation, but the training performance of these models is hampered by the extreme sparsity in warm user purchase behavior. In this paper we introduce a novel representation for user-vectors by combining user demographics and user preferences, making the model a hybrid system which uses Collaborative Filtering and Content Based Recommendation. Our system models user purchase behavior using weighted user-product preferences (explicit feedback) rather than binary user-product interactions (implicit feedback). Using this we develop a novel sparse adversarial model, SRLGAN, for Cold-Start Recommendation leveraging the sparse user-purchase behavior which ensures training stability and avoids over-fitting on warm users. We evaluate the SRLGAN on two popular datasets and demonstrate state-of-the-art results.