Listwise Recommendation Approach with Non-negative Matrix Factorization
Pandey, G., & Wang, S. (2018). Listwise Recommendation Approach with Non-negative Matrix Factorization. In I. Czarnowski, R. J. Howlett, L. C. Jain, & L. Vlacic (Eds.), KES-IDT 2018 : Proceedings of the 10th International KES Conference on Intelligent Decision Technologies (pp. 22-32). Springer. Smart Innovation, Systems and Technologies, 97. https://doi.org/10.1007/978-3-319-92028-3_3
Published inSmart Innovation, Systems and Technologies
© Springer International Publishing AG, part of Springer Nature 2019
Matrix factorization (MF) is one of the most effective categories of recommendation algorithms, which makes predictions based on the user-item rating matrix. Nowadays many studies reveal that the ultimate goal of recommendations is to predict correct rankings of these unrated items. However, most of the pioneering efforts on ranking-oriented MF predict users’ item ranking based on the original rating matrix, which fails to explicitly present users’ preference ranking on items and thus might result in some accuracy loss. In this paper, we formulate a novel listwise user-ranking probability prediction problem for recommendations, that aims to utilize a user-ranking probability matrix to predict users’ possible rankings on all items. For this, we present LwRec, a novel listwise ranking-oriented matrix factorization algorithm. It aims to predict the missing values in the user-ranking probability matrix, aiming that each row of the final predicted matrix should have a probability distribution similar to the original one. Extensive offline experiments on two benchmark datasets against several state-of-the-art baselines demonstrate the effectiveness of our proposal. ...
Parent publication ISBN978-3-319-92027-6
ConferenceInternational KES Conference on Intelligent Decision Technologies
Is part of publicationKES-IDT 2018 : Proceedings of the 10th International KES Conference on Intelligent Decision Technologies
Publication in research information system
MetadataShow full item record
Showing items with similar title or keywords.
Comparing ranking-based collaborative filtering algorithms to a rating-based alternative in recommender systems context Koskela, Pentti (2017)Suuri sisältövalikoima eri internet palveluissa, kuten verkkokaupoissa, voi aiheuttaa liian suurta informaatiomäärää, mikä heikentää asiakaskokemusta. Suosittelujärjestelmät ovat teknologioita, jotka tukevat asiakkaan ...
Assessment of nonnegative matrix factorization algorithms for electroencephalography spectral analysis Hu, Guoqiang; Zhou, Tianyi; Luo, Siwen; Mahini, Reza; Xu, Jing; Chang, Yi; Cong, Fengyu (BioMed Central, 2020)Background Nonnegative matrix factorization (NMF) has been successfully used for electroencephalography (EEG) spectral analysis. Since NMF was proposed in the 1990s, many adaptive algorithms have been developed. However, ...
Pandey, Gaurav; Kotkov, Denis; Semenov, Alexander (ACM Press, 2018)Most recommender algorithms are designed to suggest relevant items, but suggesting these items does not always result in user satisfaction. Therefore, the efforts in recommender systems recently shifted towards serendipity, ...
Kotkov, Denis (University of Jyväskylä, 2018)The number of goods and services (such as accommodation or music streaming) offered by e-commerce websites does not allow users to examine all the available options in a reasonable amount of time. Recommender systems are ...
Kotkov, Denis; Veijalainen, Jari; Wang, Shuaiqiang (SCITEPRESS Science And Technology Publications, 2017)Most recommender systems suggest items to a user that are popular among all users and similar to items the user usually consumes. As a result, a user receives recommendations that she/he is already familiar with or would ...