Using ODP metadata to personalize search

Chirita, Paul-Alexandru; Nejdl, Wolfgang; Paiu, Raluca; Kohlschütter, Christian

doi:10.1145/1076034.1076067

Cited by 206 publications

(144 citation statements)

References 20 publications

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“…User profiles can be represented by a weighted term vector [7], weighted concept hierarchical structures [10] [12] like ODP 3 , or other implicit user interest hierarchy [11]. For the purposes of selectively exposing users' interests to search engines, the user profile is a term based hierarchical structure that is related to frequent term based clustering algorithms [16] [17].…”

Section: Related Workmentioning

confidence: 99%

Privacy-enhancing personalized web search

Wang

Zhang

et al. 2007

Proceedings of the 16th International Conference on World Wide Web

111

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Personalized web search is a promising way to improve search quality by customizing search results for people with individual information goals. However, users are uncomfortable with exposing private preference information to search engines. On the other hand, privacy is not absolute, and often can be compromised if there is a gain in service or profitability to the user. Thus, a balance must be struck between search quality and privacy protection. This paper presents a scalable way for users to automatically build rich user profiles. These profiles summarize a user's interests into a hierarchical organization according to specific interests. Two parameters for specifying privacy requirements are proposed to help the user to choose the content and degree of detail of the profile information that is exposed to the search engine. Experiments showed that the user profile improved search quality when compared to standard MSN rankings. More importantly, results verified our hypothesis that a significant improvement on search quality can be achieved by only sharing some higher-level user profile information, which is potentially less sensitive than detailed personal information.

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Section: Related Workmentioning

confidence: 99%

Privacy-enhancing personalized web search

Wang

Zhang

et al. 2007

Proceedings of the 16th International Conference on World Wide Web

111

View full text Add to dashboard Cite

show abstract

“…8, where each point represents the accuracy of the classifiers (trained with unweighted boolean vectors) when considering only their results for pages with a minimum np. We see that both the J48 and NaiveBayes are better at clas-sifying popular pages, and that this is not a bias in the data, since the random classifier does not share this property 3 . We suggest that there is a correlation between popularity and purely structural similarity, which may account for the fact that weighting the instances does not significantly improve the accuracy -since this is information that the classifiers learn anyway.…”

Section: A Weighted Stak Classifiermentioning

confidence: 93%

“…However, new tools are also needed to gather, harness, reuse and share, in the most efficient and enjoyable way, the experiences captured by UGC [4,15]. One particular line of research has focused on using recommendation technologies in an effort to make Web search more personal: by learning about the preferences and interests of individual searchers, personalized Web search systems can influence search results in a manner that better suits the individual searcher [3,18]. Recently, another complementary research direction has seen researchers explore the collaborative potential of Web search by proposing that the conventional solitary nature of Web search can be enhanced in many search scenarios by recognising and supporting the sharing of search experiences to facilitate synchronous or asynchronous collaboration among searchers [12,8].…”

Section: Introductionmentioning

confidence: 99%

Coping with Noisy Search Experiences

Champin

Briggs

Coyle

et al. 2009

Research and Development in Intelligent Systems XXVI

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The so-called social Web has helped to change the very nature of the Internet by emphasising the role of our online experiences as new forms of content and service knowledge. User-generated content, from blogs and wikis to ratings and comments, all add an important layer of experiential knowledge to our online interactions. In this paper we describe an approach to improving mainstream Web search by harnessing the search experiences of groups of likeminded searchers. We focus on the HeyStaks system (www.heystaks.com) and look in particular at the experiential knowledge that drives its search recommendations. Specifically we describe how this knowledge can be noisy, and we describe and evaluate a recommendation technique for coping with this noise and discuss how it may be incorporated into HeyStaks as a useful feature.Experience is the name everyone gives to their mistakes.-Oscar Wilde

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“…Most re-ranking strategies attempt to construct a user profile from the user historical behaviors, and use the profile to filter out resources unmatched with his/her interests. [11] modeled both user profiles and resources as topic vectors from ODP hierarchy, thus the matching between user interest and content can be measured by their vector distance. A personalized PageRank algorithm was proposed in [12], which was a modification to the global PageRank on Web, and the search results were personalized based on the hyperlink structure.…”

Section: Personalized Searchmentioning

confidence: 99%

Tag-Group Based User Profiling for Personalized Search in Folksonomies

Liu

Huang

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYWith the development of the Internet, there are more and more shared resources on the Web. Personalized search becomes increasingly important as users demand higher retrieval quality. Personalized search needs to take users' personalized profiles and information needs into consideration. Collaborative tagging (also known as folksonomy) systems allow users to annotate resources with their own tags (features) and thus provide a powerful way for organizing, retrieving and sharing different types of social resources. To capture and understand user preferences, a user is typically modeled as a vector of tag:value pairs (i.e., a tag-based user profile) in collaborative tagging systems. In such a tag-based user profile, a user's preference degree on a group of tags (i.e., a combination of several tags) mainly depends on the preference degree on every individual tag in the group. However, the preference degree on a combination of tags (a tag-group) cannot simply be obtained from linearly combining the preference on each tag. The combination of a user's two favorite tags may not be favorite for the user. In this article, we examine the limitations of previous tag-based personalized search. To overcome their problems, we model a user profile based on combinations of tags (tag-groups) and then apply it to the personalized search. By comparing it with the state-of-the-art methods, experimental results on a real data set shows the effectiveness of our proposed user profile method.

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Using ODP metadata to personalize search

Cited by 206 publications

References 20 publications

Privacy-enhancing personalized web search

Privacy-enhancing personalized web search

Coping with Noisy Search Experiences

Tag-Group Based User Profiling for Personalized Search in Folksonomies

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