Chao-Yuan Wu

Recurrent Recommender Networks

Recommender systems traditionally assume that user profiles and movie attributes are static. Temporal dynamics are purely reactive, that is, they are inferred after they are observed, e.g. after a users taste has changed or based on hand-engineered temporal bias corrections for movies. We propose Recurrent Recommender Networks (RRN) that are able to predict future behavioral trajectories. This is achieved by endowing both users and movies with a Long Short-Term Memory (LSTM) autoregressive model that captures dynamics, in addition to a more traditional low-rank factorization. On multiple real-world datasets, our model offers excellent prediction accuracy and it is very compact, since we need not learn latent state but rather just the state transition function.

Explaining Reviews and Ratings with PACO: Poisson Additive Co-Clustering

Understanding a users motivations provides valuable information beyond the ability to recommend items. Quite often this can be accomplished by perusing both ratings and review texts. Unfortunately matrix factorization approaches to recommendation result in large, complex models that are difficult to interpret. In this paper, we attack this problem through succinct additive co-clustering on both ratings and reviews. Our model yields accurate and interpretable recommendations.

Using Navigation to Improve Recommendations in Real-Time

Implicit feedback is a key source of information for many recommendation and personalization approaches. However, using it typically requires multiple episodes of interaction and roundtrips to a recommendation engine. This adds latency and neglects the opportunity of immediate personalization for a user while the user is navigating recommendations. We propose a novel strategy to address the above problem in a principled manner. The key insight is that as we observe a users interactions, it reveals much more information about her desires. We exploit this by inferring the within-session user intent on-the-fly based on navigation interactions, since they offer valuable clues into a users current state of mind. Using navigation patterns and adapting recommendations in real-time creates an opportunity to provide more accurate recommendations. By prefetching a larger amount of content, this can be carried out entirely in the client (such as a browser) without added latency. We define a new Bayesian model with an efficient inference algorithm. We demonstrate significant improvements with this novel approach on a real-world, large-scale dataset from Netflix on the problem of adapting the recommendations on a users homepage.