A Tripartite-Graph Based Recommendation Framework for Price-Comparison Services

Sang-Chul Lee1, Sang-Wook Kim1, Sunju Park2 and Sunju Park 2 , and Dong-Kyu Chae 11

  1. Department of Computer and Software
    Hanyang University, Republic of Korea
    {korly, wook, kyu899}@hanyang.ac.kr
  2. School of Business
    Yonsei University, Republic of Korea
    boxenju@yonsei.ac.kr

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.

Key words

recommendation systems, price-comparison services, random walk with restart

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS123456789X

Publication information

Volume 16, Issue 2 (June 2019)
Year of Publication: 2019
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Lee, S., Kim, S., Park, S., 1, S. P. 2. ,. a. D. C.: A Tripartite-Graph Based Recommendation Framework for Price-Comparison Services. Computer Science and Information Systems, Vol. 16, No. 2, 333-357. (2019), https://doi.org/10.2298/CSIS123456789X