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Detecting online commercial intention (OCI)

Published: 23 May 2006 Publication History

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.

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cover image ACM Conferences
WWW '06: Proceedings of the 15th international conference on World Wide Web
May 2006
1102 pages
ISBN:1595933239
DOI:10.1145/1135777
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 23 May 2006

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Author Tags

  1. OCI
  2. SVM
  3. intention
  4. online commercial intention
  5. search intention

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  • (2024)Using keyword analysis for market validation researchJournal of Business & Finance Librarianship10.1080/08963568.2024.241147430:1(1-17)Online publication date: 7-Oct-2024
  • (2023)Improving Worst-case TSN Communication Times of Large Sensor Data Samples by Exploiting SynchronizationACM Transactions on Embedded Computing Systems10.1145/360912022:5s(1-25)Online publication date: 11-Sep-2023
  • (2023) Ask and Ye shall be AnsweredInformation Fusion10.1016/j.inffus.2023.10185699:COnline publication date: 1-Nov-2023
  • (2023)Ensemble Learning for Enhanced Prediction of Online Shoppers’ Intention on Oversampling-Based Reconstructed DataInternational Conference on Innovative Computing and Communications10.1007/978-981-99-4071-4_57(741-752)Online publication date: 26-Oct-2023
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