Personalized recommendation by matrix co-factorization with multiple implicit feedback on pairwise comparison

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.

Automated summary

Recommendation systems are crucial for assisting users in finding relevant items by constructing user profiles and learning their preferences. The use of implicit feedback can improve the recommendation quality but poses challenges in interpretability and diverse interpretability due to different characteristics.