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Detecting online commercial intention (OCI (2006)
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| Venue: | In Proceedings of the 15th International World Wide Web Conference (WWW-06 |
| Citations: | 34 - 4 self |
BibTeX
@INPROCEEDINGS{Dai06detectingonline,
author = {Honghua (kathy Dai and Lingzhi Zhao},
title = {Detecting online commercial intention (OCI},
booktitle = {In Proceedings of the 15th International World Wide Web Conference (WWW-06},
year = {2006},
pages = {829--837}
}
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Abstract
Understanding goals and preferences behind a user’s online activities can greatly help information providers, such as search engine and E-Commerce web sites, to personalize contents and thus improve user satisfaction. Understanding a user’s intention could also provide other business advantages to information providers. For example, information providers can decide whether to display commercial content based on user’s intent to purchase. Previous work on Web search defines three major types of user search goals for search queries: navigational, informational and transactional or resource [1][7]. In this paper, we focus our attention on capturing commercial intention from search queries and Web pages, i.e., when a user submits the query or browse a Web page, whether he / she is about to commit or in the middle of a commercial activity, such as purchase, auction, selling, paid service, etc. We call the commercial intentions behind a user’s online activities as OCI (Online Commercial Intention). We also propose the notion of “Commercial Activity Phase ” (CAP), which identifies in which phase a user is in his/her commercial activities: Research or Commit. We present the framework of building machine learning models to learn OCI based on any Web page content. Based on that framework, we build models to detect OCI from search queries and Web pages. We train machine learning models from two types of data sources for a given search query: content of algorithmic search result page(s) and contents of top sites returned by a search engine. Our experiments show that the model based on the first data source achieved better performance. We also discover that frequent queries are more likely to have commercial intention. Finally we propose our future work in learning richer commercial intention behind users’ online activities.
Keyphrases
search query commercial intention online commercial intention user online activity information provider web page commercial activity search engine future work machine learning model building machine first data source top site major type business advantage previous work commercial activity phase web search defines web page content frequent query user intent user intention user search goal data source e-commerce web site commercial content algorithmic search result page
