Incorporating localized information with browsing history for on-demand search

Wen, Wei-Hsun; Huang, To-Yeh; Teng, Wei-Guang

doi:10.1109/isce.2011.5973773

Cited by 2 publications

(1 citation statement)

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“…Since more and more people access the Internet with their smart phone, the mobile search activities have distinctive features where contextual information can be captured by the search logs, including time and location [22,25]. While consumers are often overwhelmed by excessive number of products in the platform, the specific category that products belong to is a good reflection of consumers' purchase intentions.…”

Section: Search Behavior Analysismentioning

confidence: 99%

Flexible Query Answering Systems 2015

Andreasen

Christiansen²,

Kacprzyk

et al. 2016

Advances in Intelligent Systems and Computing

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Search activity is an essential part for gathering useful information and supporting decision making. With the exponential growth of mobile e-commerce, consumers often search for products and services that are closely relevant to the current context such as location and time. This paper studies the search behaviors of mobile consumers, which reflect their customized purchase intentions. In light of machine learning, a probabilistic generative model is proposed to discover underlying search patterns, i.e., when to search,where to search and in what category. Furthermore, the predicting power of the proposed model is validated on the dataset released by Alibaba, the biggest e-commerce platform in the world. Experimental results show the advantages of the proposed model over the classical content-based methods, and also illustrate the effectiveness of integrating contextual factors into modeling consumers search patterns.

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Section: Search Behavior Analysismentioning

confidence: 99%