Customer-driven content recommendation over a network of customers

Young Ryu, Hyea Kyeong Kim, Jae Kyeong Kim, Yoon Ho Cho

Research output: Contribution to conferencePaperpeer-review

1 Citation (Scopus)

Abstract

To facilitate effective and efficient recommendations of digital contents over a large customer base, a customerdriven recommender is developed utilizing the fast diffusion and information sharing capability of customer networks. A traditional centralized recommender system based on collaborative filtering collects for a host customer a product recommendation list from most similar customers (called neighbors) that have been determined globally by checking the whole customer base. Such a procedure is a very effective method, but its computational overhead can be prohibitive when the customer base is large. The proposed method, called the customer-driven recommender system, follows the collaborative filtering principle, but performs distributed and local search for neighbors from whom a recommendation list for a host customer is generated. In order to validate the effectiveness and performance of the proposed method, we build a customer network for digital contents recommendation using real data in a mobile commerce and compare it against the traditional collaborative filtering-based system. Furthermore, experiments are designed and performed to show why the local search mechanism of the customer-driven recommender system is as accurate as, but much more computationally efficient than, the global search mechanism of the collaborative filtering-based recommender system.

Original languageEnglish
Pages158-164
Number of pages7
Publication statusPublished - 2007
Event17th Workshop on Information Technologies and Systems, WITS 2007 - Montreal, QC, Canada
Duration: 8 Dec 20079 Dec 2007

Conference

Conference17th Workshop on Information Technologies and Systems, WITS 2007
Country/TerritoryCanada
CityMontreal, QC
Period8/12/079/12/07

Keywords

  • Customer Network
  • Recommendation Accuracy and Performance
  • Recommender System

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