{"id":164604,"date":"2019-01-17T09:53:20","date_gmt":"2019-01-17T17:53:20","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/personalized-ranking-model-adaptation-for-web-search\/"},"modified":"2019-01-17T09:53:20","modified_gmt":"2019-01-17T17:53:20","slug":"personalized-ranking-model-adaptation-for-web-search","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/personalized-ranking-model-adaptation-for-web-search\/","title":{"rendered":"Personalized Ranking Model Adaptation for Web Search"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Search engines train and apply a single ranking model across all users, but searchers&#8217; information needs are diverse and cover a broad range of topics. Hence, a single user-independent ranking model is insufficient to satisfy different users&#8217; result preferences. Conventional personalization methods learn separate models of user interests and use those to re-rank the results from the generic model. Those methods require significant user history information to learn user preferences, have low coverage in the case of memory-based methods that learn direct associations between query-URL pairs, and have limited opportunity to markedly affect the ranking given that they only re-order top-ranked items.<\/p>\n<p>In this paper, we propose a general ranking model adaptation framework for personalized search. Using a given user-independent ranking model trained offline and limited number of adaptation queries from individual users, the framework quickly learns to apply a series of linear transformations, e.g., scaling and shifting, over the parameters of the given global ranking model such that the adapted model can better fit each individual user&#8217;s search preferences. Extensive experimentation based on a large set of search logs from a major commercial Web search engine confirms the effectiveness of the proposed method compared to several state-of-the-art ranking model adaptation methods.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Search engines train and apply a single ranking model across all users, but searchers&#8217; information needs are diverse and cover a broad range of topics. Hence, a single user-independent ranking model is insufficient to satisfy different users&#8217; result preferences. Conventional personalization methods learn separate models of user interests and use those to re-rank the results [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"ACM","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"The 36th Annual ACM SIGIR Conference (SIGIR'2013)","msr_editors":"","msr_how_published":"","msr_isbn":"","msr_issue":"","msr_journal":"","msr_number":"","msr_organization":"","msr_pages_string":"","msr_page_range_start":"","msr_page_range_end":"","msr_series":"","msr_volume":"","msr_copyright":"","msr_conference_name":"The 36th Annual ACM SIGIR Conference (SIGIR'2013)","msr_doi":"","msr_arxiv_id":"","msr_s2_paper_id":"","msr_mag_id":"","msr_pubmed_id":"","msr_other_authors":"Hongning Wang, Wei 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