Online social networks gain increasing popularity in recent years. In online social networks, trust prediction is significant for recommendations of high reputation users as well as in many other applications. In the literature, trust prediction problem can be solved by several strategies, such as matrix factorization, trust propagation, and
-NN search. However, most of the existing works have not considered the possible complementarity among these mainstream strategies to optimize their effectiveness and efficiency. In this article, we propose a novel trust prediction approach named
iSim
: an integrated time-aware similarity-based collaborative filtering approach leveraging on user similarity, which integrates three kinds of factors to measure user similarity, including vector space similarity, time-aware matrix factorization, and propagated trust. This article is the first work in the literature employing time-aware matrix factorization and propagated trust in the study of similarity. Additionally, we use several methods like adding inverted index to reduce the time complexity of
iSim
, and provide its theoretical time bound. Moreover, we also provide the detailed overview and theoretical analysis of the existing works. Finally, the extensive experiments with real-world datasets show that
iSim
achieves great improvement for both efficiency and effectiveness over the state-of-the-art approaches.
A Score Prediction Approach for Optional Course Recommendation via Cross-User-Domain Collaborative Filtering
