NDLI: A probabilistic mixture model for mining and analyzing product search log

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A probabilistic mixture model for mining and analyzing product search log

Content Provider	ACM Digital Library
Author	Duan, Huizhong Zhai, ChengXiang Cheng, Jinxing Gattani, Abhishek
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.
Starting Page	2179
Ending Page	2188
Page Count	10
File Format	PDF
ISBN	9781450322638
DOI	10.1145/2505515.2505578
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-10-27
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Search log mining Probabilistic mixture model Product search
Content Type	Text
Resource Type	Article

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