In this paper we tackle the problem of recommendation in the scenarios with binary
relevance data, when only a few (k) items are recommended to individual users. Past work …

Abstract Collaborative Filtering with Implicit Feedbacks (eg, browsing or clicking records),
named as CF-IF, is demonstrated to be an effective way in recommender systems. Existing …

Typical recommender systems use the root mean squared error (RMSE) between the
predicted and actual ratings as the evaluation metric. We argue that RMSE is not an optimal …

Cold-start problems are arguably the biggest challenges faced by collaborative filtering (CF)
used in recommender systems. When few ratings are available, CF models typically fail to …

In this paper, we tackle the problem of top-N context-aware recommendation for implicit
feedback scenarios. We frame this challenge as a ranking problem in collaborative filtering …

Collaborative filtering (CF) plays a critical role in the development of recommender systems.
Most CF methods utilize an encoder to embed users and items into the same representation …

One-Class Collaborative Filtering (OCCF) is a task that naturally emerges in recommender
system settings. Typical characteristics include: Only positive examples can be observed …

Collaborative filtering techniques rely on aggregated user preference data to make
personalized predictions. In many cases, users are reluctant to explicitly express their …

While a user's preference is directly reflected in the interactive choice process between her
and the recommender, this wealth of information was not fully exploited for learning …

Recommender systems typically operate on high-dimensional sparse user-item matrices.
Matrix completion is a very challenging task to predict one's interest based on millions of …