Personalized recommendation by matrix co-factorization with multiple implicit feedback on pairwise comparison
| Autor: | Wenny Franciska Senjaya, Bernardo Nugroho Yahya, Jei-Zheng Wu, Frans Prathama |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
021103 operations research
General Computer Science Computer science business.industry 0211 other engineering and technologies General Engineering 02 engineering and technology Recommender system Machine learning computer.software_genre Matrix (mathematics) Transformation (function) 0202 electrical engineering electronic engineering information engineering Decomposition (computer science) 020201 artificial intelligence & image processing Pairwise comparison Artificial intelligence Duration (project management) Construct (philosophy) business computer Interpretability |
| Zdroj: | Computers & Industrial Engineering. 152:107033 |
| ISSN: | 0360-8352 |
| DOI: | 10.1016/j.cie.2020.107033 |
| Popis: | Recommendation systems have been tremendously important to assist users to find relevant items. With the information-overloaded problem, it becomes crucial to understand users’ behavior by learning their preferences during the interaction to construct a profile for exploitation in selecting relevant items. Relevant feedback to capture the users’ behavior may not only explicitly exist but also implicitly available. In the real world, it is common that explicit feedback may be unavailable, and the recommender systems rely only on implicit feedback. When only implicit feedback exists, there are interpretability issues on performing recommender systems. In addition, multiple implicit feedback may cause diverse interpretability due to different characteristics and distributions. This study aims to propose a decomposition approach by incorporating joint information rating to improve recommender systems. In prior to the development of the decomposition approach, we develop a framework to explore the proper rating transformation on multiple implicit feedback. The best rating transformation approach is evaluated using the traditional recommender systems and is used as the input for the joint information rating in the decomposition approach using matrix co-factorization. The proposed matrix co-factorization incorporates multiple implicit feedbacks (i.e., frequency and duration). The result proves that incorporating multiple implicit feedbacks with matrix co-factorization improves the recommendation quality. |
| Databáze: | OpenAIRE |
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