Name
Abstract | ||
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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.
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Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2695664.2695748 | SAC 2015: Symposium on Applied Computing
Salamanca
Spain
April, 2015 |
Keywords | Field | DocType |
Thompson sampling, query suggestions, multi-armed bandit | Query optimization,Web search query,Query expansion,Data resources,Computer science,Web query classification,Thompson sampling,Ranking (information retrieval),Artificial intelligence,Multi-armed bandit,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-4503-3196-8 | 3 | 0.39 |
References | Authors | |
15 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chu-Cheng Hsieh | 1 | 31 | 3.44 |
| James Neufeld | 2 | 3 | 0.39 |
| Tracy Holloway King | 3 | 90 | 14.62 |
| Junghoo Cho | 4 | 3088 | 584.54 |