NDLI: Latent dirichlet allocation based diversified retrieval for e-commerce search

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Latent dirichlet allocation based diversified retrieval for e-commerce search

Content Provider	ACM Digital Library
Author	Yu, Jun Putthividhya, Duangmanee Pew Mohan, Sunil Wong, Weng-Keen
Abstract	Diversified retrieval is a very important problem on many e-commerce sites, e.g. eBay and Amazon. Using IR approaches without optimizing for diversity results in a clutter of redundant items that belong to the same products. Most existing product taxonomies are often too noisy, with overlapping structures and non-uniform granularity, to be used directly in diversified retrieval. To address this problem, we propose a Latent Dirichlet Allocation (LDA) based diversified retrieval approach that selects diverse items based on the hidden user intents. Our approach first discovers the hidden user intents of a query using the LDA model, and then ranks the user intents by making trade-offs between their relevance and information novelty. Finally, it chooses the most representative item for each user intent to display. To evaluate the diversity in the search results on e-commerce sites, we propose a new metric, average satisfaction, measuring user satisfaction with the search results. Through our empirical study on eBay, we show that the LDA model discovers meaningful user intents and the LDA-based approach provides significantly higher user satisfaction than the eBay production ranker and three other diversified retrieval approaches.
Starting Page	463
Ending Page	472
Page Count	10
File Format	PDF
ISBN	9781450323512
DOI	10.1145/2556195.2556215
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-02-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Diversified retrieval E-commerce search Latent dirichlet allocation
Content Type	Text
Resource Type	Article

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