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 recommendation in the scenarios with binary
relevance data, when only a few (k) items are recommended to individual users. Past work …

In this paper, we study how to address the sparsity, accuracy and serendipity issues of top-N
recommendation with collaborative filtering (CF). Existing studies commonly use rated items …

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 …

The core of recommendation models is estimating the probability that a user will like an item
based on historical interactions. Existing collaborative filtering (CF) algorithms compute the …

Hashing is an effective technique to address the large-scale recommendation problem, due
to its high computation and storage efficiency on calculating the user preferences on items …

Item-based collaborative filtering (ICF) enjoys the advantages of high recommendation
accuracy and ease in online penalization and thus is favored by the industrial recommender …

Collaborative filtering (CF) is one of the most effective techniques in recommender systems,
which can be either rating oriented or ranking oriented. Ranking-oriented CF algorithms …

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

Collaborative Filtering (CF) is the most popular method for recommender systems. The
principal idea of CF is that users might be interested in items that are favorited by similar …