Detecting dominant locations from search queries

Wang, Lee; Wang, Chuang; Xie, Xing; Forman, Joshua J.; Lu, Yilong; Ma, Wei‐Ying; Y, Li

doi:10.1145/1076034.1076107

Cited by 69 publications

(41 citation statements)

References 12 publications

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“…We try to determine good places, which is different from trying to determine the region to which a query is relevant. Other work [22] in this line of research aims to determine the so-called dominant location for each query (the location most important for a particular query). The techniques used include query tokenization, query log analysis, and exploration of the snippets of search results for the top-k results.…”

Section: Related Workmentioning

confidence: 99%

“…The challenge is to produce a good ranking of the places and to do so in a scalable fashion so that the places can be ranked for any query location. While previous work on local-web querying (e.g., [3,22]) assumes IP-based positioning and therefore knows the location of the user within a ZIP code, we assume that the user's location is known within tens to hundreds of meters, leading to a fundamentally different problem.…”

Section: Introductionmentioning

confidence: 99%

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Hyper-local, directions-based ranking of places

et al. 2011

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Studies find that at least 20% of web queries have local intent; and the fraction of queries with local intent that originate from mobile properties may be twice as high. The emergence of standardized support for location providers in web browsers, as well as of providers of accurate locations, enables so-called hyper-local web querying where the location of a user is accurate at a much finer granularity than with IP-based positioning.This paper addresses the problem of determining the importance of points of interest, or places, in local-search results. In doing so, the paper proposes techniques that exploit logged directions queries. A query that asks for directions from a location a to a location b is taken to suggest that a user is interested in traveling to b and thus is a vote that location b is interesting. Such user-generated directions queries are particularly interesting because they are numerous and contain precise locations.Specifically, the paper proposes a framework that takes a user location and a collection of near-by places as arguments, producing a ranking of the places. The framework enables a range of aspects of directions queries to be exploited for the ranking of places, including the frequency with which places have been referred to in directions queries. Next, the paper proposes an algorithm and accompanying data structures capable of ranking places in response to hyper-local web queries. Finally, an empirical study with very large directions query logs offers insight into the potential of directions queries for the ranking of places and suggests that the proposed algorithm is suitable for use in real web search engines.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hyper-local, directions-based ranking of places

et al. 2011

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“…The goal of a navigational query is to reach a particular web site; the intent of an informational query is to acquire information on web pages; and a user who inputs transactional queries are to perform some "web-mediated" activity. User search goals can also be represented using topical categories ( [18] [9] and [22]) or location attributes [21]. A few efforts have been invested in automatically identify user search goals [4][10] [20] [21].…”

Section: Introductionmentioning

confidence: 99%

“…User search goals can also be represented using topical categories ( [18] [9] and [22]) or location attributes [21]. A few efforts have been invested in automatically identify user search goals [4][10] [20] [21].…”

Section: Introductionmentioning

confidence: 99%

“…They built query classifiers based on past user-click behavior and anchor-link distribution to achieve the same query categorization problem. Lee Wang et al [21] built location information detector based on multiple data sources, including query result page content (snippets) and query logs. In [15], similarity between two queries was computed from both the keywords similarity and the common search result landing pages selected by users.…”

Section: Introductionmentioning

confidence: 99%

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Detecting online commercial intention (OCI)

Dai

Zhao

Nie

et al. 2006

Proceedings of the 15th International Conference on World Wide Web

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150

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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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Analyzing geographic query reformulation: An exploratory study

Aloteibi

Sanderson

2013

Asso for Info Science & Tech

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Search engine users typically engage in multiquery sessions in their quest to fulfill their information needs. Despite a plethora of research findings suggesting that a significant group of users look for information within a specific geographical scope, existing reformulation studies lack a focused analysis of how users reformulate geographic queries. This study comprehensively investigates the ways in which users reformulate such needs in an attempt to fill this gap in the literature. Reformulated sessions were sampled from a query log of a major search engine to extract 2,400 entries that were manually inspected to filter geo sessions. This filter identified 471 search sessions that included geographical intent, and these sessions were analyzed quantitatively and qualitatively. The results revealed that one in five of the users who reformulated their queries were looking for geographically related information. They reformulated their queries by changing the content of the query rather than the structure. Users were not following a unified sequence of modifications and instead performed a single reformulation action. However, in some cases it was possible to anticipate their next move. A number of tasks in geo modifications were identified, including standard, multi-needs, multi-places, and hybrid approaches. The research concludes that it is important to specialize query reformulation studies to focus on particular query types rather than generically analyzing them, as it is apparent that geographic queries have their special reformulation characteristics.

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Detecting dominant locations from search queries

Cited by 69 publications

References 12 publications

Hyper-local, directions-based ranking of places

Hyper-local, directions-based ranking of places

Detecting online commercial intention (OCI)

Analyzing geographic query reformulation: An exploratory study

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