TransNets: Learning to Transform for Recommendation
This work addresses recommendation accuracy for users and items in systems with review data, but it is incremental as it builds directly on the DeepCoNN model.
The paper tackles the problem of improving recommender systems by leveraging review text, particularly from the target user's review of the target item, even when that review is unavailable, and shows that TransNets and its extensions substantially improve over the previous state-of-the-art.
Recently, deep learning methods have been shown to improve the performance of recommender systems over traditional methods, especially when review text is available. For example, a recent model, DeepCoNN, uses neural nets to learn one latent representation for the text of all reviews written by a target user, and a second latent representation for the text of all reviews for a target item, and then combines these latent representations to obtain state-of-the-art performance on recommendation tasks. We show that (unsurprisingly) much of the predictive value of review text comes from reviews of the target user for the target item. We then introduce a way in which this information can be used in recommendation, even when the target user's review for the target item is not available. Our model, called TransNets, extends the DeepCoNN model by introducing an additional latent layer representing the target user-target item pair. We then regularize this layer, at training time, to be similar to another latent representation of the target user's review of the target item. We show that TransNets and extensions of it improve substantially over the previous state-of-the-art.