Abstract
We present a transparent and tunable recommender system using neural networks in which the user’s preference for each tag calculated from his or her rating history is extracted as a user feature, and latent knowledge about the relationship between an item and a tag is extracted as an item feature. To improve user satisfaction with recommender systems, researchers have been focusing not only on a system’s recommendation accuracy but also on its transparency, novelty, and serendipity as evaluation indices. Furthermore, the degree of user involvement in the recommendation process has been shown to substantially affect user satisfaction. Therefore, we propose showing a tag cloud as the user’s profile as captured by the system from the user’s interaction history and providing the user with a way to tune the recommendation results so that user satisfaction can be improved.
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Acknowledgment
This work was partially supported by JST, CREST Grant Number JPMJCR22M2, and Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Numbers 23H03401 and 23H03694, Japan.
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Xu, M., Chang, Q., Miyazaki, J. (2023). How Does the System Perceive Me? — A Transparent and Tunable Recommender System. In: Strauss, C., Amagasa, T., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2023. Lecture Notes in Computer Science, vol 14147. Springer, Cham. https://doi.org/10.1007/978-3-031-39821-6_3
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DOI: https://doi.org/10.1007/978-3-031-39821-6_3
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