OSGS - A personalized online store for E-commerce environments

Raz Lin, Sarit Kraus, Jeffrey Tew

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


Current online stores suffer from a cardinal problem. There are too many products to offer, and customers find themselves lost due to the vast selection. As opposed to traditional stores, there is little or no guidance that helps the customers as they search. In this paper, we propose a new approach for designing a successful personalized online store enabling the successful searching of customers in the store. This approach is based on algorithms commonly used in recommendation systems, but which are rarely used for searches in online stores. We employ this approach for both keyword and browse searches, and present an implementation of this approach. We compared several search guide algorithms experimentally, and the experiments' results show that the suggested algorithms are applicable to the domain of online stores.

Original languageEnglish
Pages (from-to)369-394
Number of pages26
JournalInformation Retrieval
Issue number3-4
StatePublished - 2004


  • Browse search
  • Collaborative filtering
  • E-commerce
  • Information retrieval
  • Keyword search


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