A Tripartite-Graph Based Recommendation Framework for Price-Comparison Services
- Authors
- Lee, Sang-Chul; Kim, Sang-Wook; Park, Sunju; Chae, Dong-Kyu
- Issue Date
- Jun-2019
- Publisher
- COMSIS CONSORTIUM
- Keywords
- recommendation systems; price-comparison services; random walk with restart
- Citation
- COMPUTER SCIENCE AND INFORMATION SYSTEMS, v.16, no.2, pp.333 - 357
- Indexed
- SCIE
SCOPUS
- Journal Title
- COMPUTER SCIENCE AND INFORMATION SYSTEMS
- Volume
- 16
- Number
- 2
- Start Page
- 333
- End Page
- 357
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/147691
- DOI
- 10.2298/CSIS181012005L
- ISSN
- 1820-0214
- Abstract
- The recommender systems help users who are going through numerous items (e.g., movies or music) presented in online shops by capturing each user's preferences on items and suggesting a set of personalized items that s/he is likely to prefer [8]. They have been extensively studied in the academic society and widely utilized in many online shops [33]. However, to the best of our knowledge, recommending items to users in price-comparison services has not been studied extensively yet, which could attract a great deal of attention from shoppers these days due to its capability to save users' time who want to purchase items with the lowest price [31]. In this paper, we examine why existing recommendation methods cannot be directly applied to price-comparison services, and propose three recommendation strategies that are tailored to price-comparison services: (1) using click-log data to identify users' preferences, (2) grouping similar items together as a user's area of interest, and (3) exploiting the category hierarchy and keyword information of items. We implement these strategies into a unified recommendation framework based on a tripartite graph. Through our extensive experiments using real-world data obtained from Naver shopping, one of the largest price-comparison services in Korea, the proposed framework improved recommendation accuracy up to 87% in terms of precision and 129% in terms of recall, compared to the most competitive baseline.
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