Detecting online commercial intention (OCI)

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.

Contextual Search: A Computational Framework

“…According to Dai et al [49], the classification of queries into navigational and informational is not the only one possible. In electronic commerce, further understanding of commercial intents is crucial.…”

“…Hence, one may suggest that deciding whether an intent exists behind a query is in fact a necessary step before trying to retrieve possibly relevant documents. Dai et al [49] report some examples of the relationships between commercial/non-commercial intent and navigational/informational/transactional intents; "u2 music downloads" is a query with transactional and commercial intent whereas "collide lyrics" is a query with transactional and non-commercial intent. In general, commercial intents are behind queries composed of nouns referred to commerce (e.g., "walmart," "dvd," "price").…”

“…Chapelle et al [42]'s research work report interleaving as an alternative approach to collecting relevance assessments, since the conventional Cranfield-based approach to evaluation is not free of drawbacks although it is the most used and well accepted in non-contextual search. Dai et al [49] use "live" WWW. Dang and Croft [50] use TREC collections.…”

Classifying Websites into Non-topical Categories

“…Dai et al [6] classified web pages into commercial and non-commercial classes in an attempt to detect the online commercial intent of a page. This work is close to ours as some of the categories overlap, however the authors only used keywords from text and html attributes as features whereas we are combining non-topical features with word-based features.…”

The Economics of the Online Advertising Industry

“…It has been reported that about 40 percent of search queries involve potential commercial transactions. See Eisenmann, supra note 37; See also Dai, et al (2006) "Detecting Online Commercial Intention," World Wide Web Conference, Edinburgh, Scotland. 40 Search engines typically capture data on searches and accumulate this over time.…”