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Identification of User Preference Factor Using Review Information
- timothy.dim12@gmail.com
Jiyoung Shim (Yonsei University)
- jiyoung_shim@yonsei.ac.kr
Abstract
This study analyzed the contents of Goodreads review data, which is a social cataloging service with the participation of book users around the world, to identify the preference factors that affect book users’ book recommendations in the library information service environment. To understand user preferences from a more detailed point of view, sub-datasets for each rating group, each book, and each user were constructed in the sample selection process. Stratified sampling was also performed based on the result of topic modeling of review text data to include various topics. As a result, a total of 90 preference factors belonging to 7 categories(‘Content’, ‘Character’, ‘Writing’, ‘Reading’, ‘Author’, ‘Story’, ‘Form’) were identified. Also, the general preference factors revealed according to the ratings, as well as the patterns of preference factors revealed in books and users with clear likes and dislikes were identified. The results of this study are expected to contribute to more sophisticated recommendations in future recommendation systems by identifying specific aspects of user preference factors.
- keywords
- user preference, review data, natural language processing, topic modeling, content analysis
- Submission Date
- 2022-08-21
- Revised Date
- 2022-09-04
- Accepted Date
- 2022-09-08
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