published by Damascus University
in 2018
in Informatics Engineering
and research's language is
العربية
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Abstract in English
Can implicit feedback substitute for explicit ratings in recommender systems?
If so, we could avoid the difficulties associated with gathering explicit ratings from users.
How, then, can we capture useful information unobtrusively, and how might we use that information to make recommendations?
References used
Lin, Chia-Yu, Li-Chun Wang, and Kun-Hung Tsai. "Hybrid Real-Time Matrix Factorization for Implicit Feedback Recommendation Systems." IEEE Access 6 (2018).
Jannach, Dietmar, Lukas Lerche, and Markus Zanker. "Recommending based on implicit feedback." Social Information Access. Springer, Cham, 2018.
K. Wang, H. Peng, Y. Jin, C. Sha, and X. Wang, ``Local weighted matrix factorization for top-n recommendation with implicit feedback,'' Data Sci. Eng., vol. 1, no. 4, pp. 252_264, 2016.
Jawaheer, Gawesh, Peter Weller, and Patty Kostkova. "Modeling user preferences in recommender systems: A classification framework for explicit and implicit user feedback." ACM Transactions on Interactive Intelligent Systems (TiiS) 4.2 (2014).
Y. Koren and R. Bell. Advances in collaborative filtering. In Recommender Systems Handbook. Springer, 2011.