Cross domain recommender systems (CDRS) can assist recommendations in a target
domain based on knowledge learned from a source domain. CDRS consists of three …

The proliferation of e-commerce sites and online social media has allowed users to provide
preference feedback and maintain profiles in multiple systems, reflecting a variety of their …

The behavior of users in certain services indicates their preferences, which may be used to
make recommendations for other services they have never used. However, the cross …

Graphs are a fundamental data structure and have been employed to model objects as well
as their relationships. The similarity of objects on the web (eg, webpages, photos, music …

Most recommender algorithms produce types similar to those the active user has accessed
before. This is because they measure user similarity only from the co-rating behaviors …

Analyzing “what topics” a user discusses with others is important in social network analysis.
Since social relationships can be represented as multiobject relationships (eg, those …

We propose a cross-domain recommendation method for predicting the ratings of items in
different domains, where neither users nor items are shared across domains. The proposed …

Manifold Ranking is a graph-based ranking algorithm being successfully applied to retrieve
images from multimedia databases. Given a query image, Manifold Ranking computes the …

The lasso-based L1-graph is used in many applications since it can effectively model a set
of data points as a graph. The lasso is a popular regression approach and the L1-graph …

Cross-domain recommendation is a promising solution in recommendation systems by
using relatively rich information from the source domain to improve the recommendation …