A probabilistic mixture model for mining and analyzing product search log

Huizhong Duan, Chengxiang Zhai, Jinxing Cheng, Abhishek Gattani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The booming of e-commerce in recent years has led to the generation of large amounts of product search log data. Product search log is a unique new data with much valuable information and knowledge about user preferences over product attributes that is often hard to obtain from other sources. While regular search logs (e.g., Web search logs) contain click-throughs for unstructured text documents (e.g., web pages), product search logs contain clickth-roughs for structured entities defined by a set of attributes and their values. For instance, a laptop can be defined by its size, color, cpu, ram, etc. Such structures in product entities offer us opportunities to mine and discover detailed useful knowledge about user preferences at the attribute level, but they also raise significant challenges for mining due to the lack of attribute-level observations. In this paper, we propose a novel probabilistic mixture model for attribute-level analysis of product search logs. The model is based on a generative process where queries are generated by a mixture of unigram language models defined by each attribute-value pair of a clicked entity. The model can be efficiently estimated using the Expectation- Maximization (EM) algorithm. The estimated parameters, including the attribute-value language models and attribute-value preference models, can be directly used to improve product search accuracy, or aggregated to reveal knowledge for understanding user intent and supporting business intelligence. Evaluation of the proposed model on a commercial product search log shows that the model is effective for mining and analyzing product search logs to discover various kinds of useful knowledge.

Original languageEnglish (US)
Title of host publicationCIKM 2013 - Proceedings of the 22nd ACM International Conference on Information and Knowledge Management
Pages2179-2188
Number of pages10
DOIs
StatePublished - 2013
Event22nd ACM International Conference on Information and Knowledge Management, CIKM 2013 - San Francisco, CA, United States
Duration: Oct 27 2013Nov 1 2013

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Other

Other22nd ACM International Conference on Information and Knowledge Management, CIKM 2013
Country/TerritoryUnited States
CitySan Francisco, CA
Period10/27/1311/1/13

Keywords

  • Probabilistic mixture model
  • Product search
  • Search log mining

ASJC Scopus subject areas

  • General Decision Sciences
  • General Business, Management and Accounting

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