NDLI: Result enrichment in commerce search using browse trails

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Result enrichment in commerce search using browse trails

Content Provider	ACM Digital Library
Author	Panigrahi, Debmalya Gollapudi, Sreenivas
Abstract	Commerce search engines have become popular in recent years, as users increasingly search for (and buy) products on the web. In response to an user query, they surface links to products in their catalog (or index) that match the requirements specified in the query. Often, few or no product in the catalog matches the user query exactly, and the search engine is forced to return a set of products that partially match the query. This paper considers the problem of choosing a set of products in response to an user query, so as to ensure maximum user satisfaction. We call this the result enrichment problem in commerce search. The challenge in result enrichment is two-fold: the search engine needs to estimate the extent to which a user genuinely cares about an attribute that she has specified in a query; then, it must display products in the catalog that match the user requirement on the important attributes, but have a similar but possibly non-identical value on the less important ones. To this end, we propose a technique for measuring the importance of individual attribute values and the similarity between different values of an attribute. A novelty of our approach is that we use entire browse trails, rather than just clickthrough rates, in this estimation algorithm. We develop a model for this problem, propose an algorithm to solve it, and support our theoretical findings via experiments conducted on actual user data.
Starting Page	267
Ending Page	276
Page Count	10
File Format	PDF MP4
ISBN	9781450304931
DOI	10.1145/1935826.1935874
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-02-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Streaming algorithms Structured search
Content Type	Audio Text
Resource Type	Article

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