{"id":144955,"date":"2008-07-01T00:00:00","date_gmt":"2008-07-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/a-decision-theoretic-framework-for-ranking-using-implicit-feedback\/"},"modified":"2018-10-16T20:04:48","modified_gmt":"2018-10-17T03:04:48","slug":"a-decision-theoretic-framework-for-ranking-using-implicit-feedback","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/a-decision-theoretic-framework-for-ranking-using-implicit-feedback\/","title":{"rendered":"A Decision Theoretic Framework for Ranking using Implicit Feedback"},"content":{"rendered":"<div class=\"asset-content\">\n<p>This paper presents a decision theoretic ranking system that incorporates both explicit and implicit feedback. The system has a model that predicts, given all available data at query time, different interactions a person might have with search results. Possible interactions include relevance labelling and clicking. We define a utility function that takes as input the outputs of the interaction model to provide a real valued score to the user\u2019s session. The optimal ranking is the list of documents that, in expectation under the model, maximizes the utility for a user session.<\/p>\n<p>The system presented is based on a simple example utility function that combines both click behavior and labelling. The click prediction model is a Bayesian generalized linear model. Its notable characteristic is that it incorporates both weights for explanatory features and weights for each query-document pair. This allows the model to generalize to unseen queries but makes it at the same time flexible enough to keep in a \u2018memory\u2019 where the model should deviate from its feature based prediction. Such a click-predicting model could be particularly useful in an application such as enterprise search, allowing on-site adaptation to local documents and user behaviour. The example utility function has a parameter that controls the trade-off between optimizing for clicks and optimizing for labels. Experimental results in the context of enterprise search show that a balance in the trade-off leads to the best NDCG and good (predicted) click-through.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This paper presents a decision theoretic ranking system that incorporates both explicit and implicit feedback. The system has a model that predicts, given all available data at query time, different interactions a person might have with search results. Possible interactions include relevance labelling and clicking. We define a utility function that takes as input the [&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":"","footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13556,13555],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-post-option":[],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-144955","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-artificial-intelligence","msr-research-area-search-information-retrieval","msr-locale-en_us"],"msr_publishername":"","msr_edition":"SIGIR 2008 Workshop on Learning to Rank for Information 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