Personalizing Web Search using Long Term Browsing History

Personalizing web search results has long been recognized as an avenue to greatly improve the search experience. We present a personalization approach that builds a user interest profile using users' complete browsing behavior, then uses this model to rerank web results. We show that using a combination of content and previously visited websites provides effective personalization. We extend previous work by proposing a number of techniques for filtering previously viewed content that greatly improve the user model used for personalization. Our approaches are compared to previous work in offline experiments and are evaluated against unpersonalized web search in large scale online tests. Large improvements are found in both cases.

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In  ACM International Conference on Web Search and Data Mining (WSDM)

Publisher  Association for Computing Machinery, Inc.
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