TELKOMNIKA Telecommunication, Computing, Electronics and Control
Adopting explicit and implicit social relations by SVD++ for recommendation system improvement

TELKOMNIKA Telecommunication, Computing, Electronics and Control
Adopting explicit and implicit social relations by SVD++ for recommendation system improvement

Recommendation system
Social Relation
Sparsity
SVD++

Recommender systems suffer a set of drawbacks such as sparsity. Social
relations provide a useful source to overcome the sparsity problem. Previous
studies have utilized social relations or rating feedback sources. However, they
ignored integrating these sources. In this paper, the limitations of previous
studies are overcome by exploiting four sources of information, namely:
explicit social relationships, implicit social relationships, users’ ratings, and
implicit feedback information. Firstly, implicit social relationships are
extracted through the source allocation index algorithm to establish new
relations among users. Secondly, the similarity method is applied to find the
similarity between each pair of users who have explicit or implicit social
relations. Then, users’ ratings and implicit rating feedback sources are
extracted via a user-item matrix. Furthermore, all sources are integrated into
the singular value decomposition plus (SVD++) method. Finally, missing

predictions are computed. The proposed method is implemented on three real-
world datasets: Last.Fm, FilmTrust, and Ciao. Experimental results reveal that

the proposed model is superior to other studies such as SVD, SVD++, EU-
SVD++, SocReg, and EISR in terms of accuracy, where the improvement of

the proposed method is about 0.03% for MAE and 0.01% for RMSE when
dimension value (d) = 10.

Mohsin Hasan Hussein, Akeel Abdulkareem Alsakaa, Haydar A. Marhoon

http://journal.uad.ac.id/index.php/TELKOMNIKA

Oct 16, 2020

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