A tripartite-graph based recommendation framework for price-comparison services

Sang Chul Lee, Sang Wook Kim, Sunju Park, Dong Kyu Chae

Research output: Contribution to journalArticle

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.

Original languageEnglish
Pages (from-to)333-357
Number of pages25
JournalComputer Science and Information Systems
Volume16
Issue number2
DOIs
Publication statusPublished - 2019 Jun

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All Science Journal Classification (ASJC) codes

  • Computer Science(all)

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