Adaptive web search based on user profile constructed without any effort from users

Sugiyama, Kazunari; Hatano, Kenji; Yoshikawa, Masatoshi

doi:10.1145/988672.988764

Cited by 505 publications

(288 citation statements)

References 34 publications

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“…For example, Google search technology ranks the pages according to link popularity rather than users interest (Busby, 2003); it does not consider the intentions of the user in ranking relevant pages to the user (Grimmelmann, 2007). If two different users -with different interestssubmit the same query with different intentions the same result can be returned (Sugiyama, Hatano and Yoshikawa 2004 Google addresses the problem of low rank for new pages by continuously updating its index. How often a web portal is revisited depends on how high is its rank and how often it has changed in the past.…”

Section: Search Engines Retrieval Results Are Based On Link Popularitymentioning

confidence: 99%

The Impact of Modes of Mediation on the Web Retrieval Process

Pannu

Anane

James

2012

Lecture Notes in Computer Science

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Section: Search Engines Retrieval Results Are Based On Link Popularitymentioning

confidence: 99%

The Impact of Modes of Mediation on the Web Retrieval Process

Pannu

Anane

James

2012

Lecture Notes in Computer Science

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“…There has also been a lot of prior work in applying user profiles for improved web search ranking: Sugiyama et al employ two user profiles in combination for every user; one that represents the user's long term browsing history, and another one that represents her current browsing history [18]. Teevan et al also study long-term and short-term interests of users in their profiles for better document ranking [20].…”

Section: Related Workmentioning

confidence: 99%

“…Prior research has dealt with personalization for search results or building user profiles based on their search results interaction, i.e., clicked urls, content of the visited web pages [3], [4], [8], [13], [18]. These works primarily study users' intents over time for better document ranking [1], [4], [9], [11], [12], [15], [17], [19], [20] or they research query suggestion utilizing document click through [3], [8], [13], [16].…”

Section: Introductionmentioning

confidence: 99%

“…These works primarily study users' intents over time for better document ranking [1], [4], [9], [11], [12], [15], [17], [19], [20] or they research query suggestion utilizing document click through [3], [8], [13], [16]. Building deep profiles from terse queries poses many interesting challenges: (1) we have a cold-start problem [17], [18], [20], i.e., information about the user needs to be gathered first before queries or results can be personalized; (2) a substantial fraction of queries does not yield to personalization (navigational queries like 'facebook', 'bank of america', etc.) or purely numeric queries; (3) search queries are terse and often ambiguous.…”

Section: Introductionmentioning

confidence: 99%

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Building Rich User Search Queries Profiles

Aktolga

Jain

Velipasaoglu

2013

User Modeling, Adaptation, and Personalization

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Abstract. It is well-known that for a variety of search tasks involving queries more relevant results can be presented if they are personalized according to a user's interests and search behavior. This can be achieved with user-dependent, rich web search queries profiles. These are typically built as part of a specific search personalization task so that it is unclear which characteristics of queries are most effective for modeling the user-query relationship in general. In this paper, we explore various approaches for explicitly modeling this user-query relationship independently of other search components. Our models employ generative models in layers in a prediction task. The results show that the best signals for modeling the user-query relationship come from the given query's terms and entities together with information from related entities and terms, yielding a relative improvement of up to 24.5% in MRR and Success over the baseline methods.

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“…Sugiyama et al [17] captured both long-term and short-term preferences based on the user's Web browsing activities. Gaps in the interest profiles are automatically filled based on matches to similar users.…”

Section: Related Workmentioning

confidence: 99%

Automatic Topic Learning for Personalized Re-Ordering of Web Search Results

Hoeber

Massie

2010

Advances in Soft Computing

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The fundamental idea behind personalization is to first learn something about the users of a system, and then use this information to support their future activities. When effective algorithms can be developed to learn user preferences, and when the methods for supporting future actions are achievable, personalization can be very effective. However, personalization is difficult in domains where tracking users, learning their preferences, and affecting their future actions is not obvious. In this paper, we introduce a novel method for providing personalized re-ordering of Web search results, based on allowing the searcher to maintain distinct search topics. Search results viewed during the search process are monitored, allowing the system to automatically learn about the users' current interests. The results of an evaluation study show improvements in the precision of the top 10 and 20 documents in the personalized search results after selecting as few as two relevant documents.

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Adaptive web search based on user profile constructed without any effort from users

Cited by 505 publications

References 34 publications

The Impact of Modes of Mediation on the Web Retrieval Process

The Impact of Modes of Mediation on the Web Retrieval Process

Building Rich User Search Queries Profiles

Automatic Topic Learning for Personalized Re-Ordering of Web Search Results

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