Feedback based Quality Enhancing Query Suggestion in E- Commerce Environment

Govindu Lavanyeswari, Pathuri Siva Kumar

Abstract: Query suggestions have been a valuable feature for e-commerce sites in helping shoppers refine their search intent. In this paper, we develop an algorithm that helps e-commerce sites like eBay mingle the output of different recommendation algorithms. Our algorithm is based on Thompson Sampling a technique designed for solving multi-arm bandit problems where the best results are not known in advance but instead are tried out to gather feedback. Our approach is to treat query suggestions as a competition among data resources we have many query suggestion candidates competing for limited space on the search results page. An arm is played when a query suggestion candidate is chosen for display, and our goal is to maximize the expected reward (user clicks on a suggestion). Our experiments have shown promising results in using the click-based user feedback to drive success by enhancing the quality of query suggestions.

Keywords: Query Recommending, Machine Learning, Electronic Discovery, e-Discovery, Evidence Search

How to Cite?: Govindu Lavanyeswari, Pathuri Siva Kumar, "Feedback based Quality Enhancing Query Suggestion in E- Commerce Environment", Volume 4 Issue 11, November 2015, International Journal of Science and Research (IJSR), Pages: 104-108, https://www.ijsr.net/getabstract.php?paperid=SUB158796, DOI: https://dx.doi.org/10.21275/SUB158796

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