User preference and embedding learning with implicit feedback for recommender systems
| Autor: | Massih-Reza Amini, Mikhail Trofimov, Charlotte Laclau, Yury Maximov, Oleh Horodnytskyi, Sumit Sidana |
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| Rok vydání: | 2021 |
| Předmět: |
Computer Networks and Communications
business.industry Computer science 02 engineering and technology Recommender system Machine learning computer.software_genre Preference Computer Science Applications Consistency (database systems) Ranking 020204 information systems 0202 electrical engineering electronic engineering information engineering Collaborative filtering 020201 artificial intelligence & image processing Empirical risk minimization Artificial intelligence business Representation (mathematics) Preference relation computer Information Systems |
| Zdroj: | Data Mining and Knowledge Discovery. 35:568-592 |
| ISSN: | 1573-756X 1384-5810 |
| DOI: | 10.1007/s10618-020-00730-8 |
| Popis: | In this paper, we propose a novel ranking framework for collaborative filtering with the overall aim of learning user preferences over items by minimizing a pairwise ranking loss. We show the minimization problem involves dependent random variables and provide a theoretical analysis by proving the consistency of the empirical risk minimization in the worst case where all users choose a minimal number of positive and negative items. We further derive a Neural-Network model that jointly learns a new representation of users and items in an embedded space as well as the preference relation of users over the pairs of items. The learning objective is based on three scenarios of ranking losses that control the ability of the model to maintain the ordering over the items induced from the users’ preferences, as well as, the capacity of the dot-product defined in the learned embedded space to produce the ordering. The proposed model is by nature suitable for implicit feedback and involves the estimation of only very few parameters. Through extensive experiments on several real-world benchmarks on implicit data, we show the interest of learning the preference and the embedding simultaneously when compared to learning those separately. We also demonstrate that our approach is very competitive with the best state-of-the-art collaborative filtering techniques proposed for implicit feedback. |
| Databáze: | OpenAIRE |
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