{"id":145450,"date":"2001-01-01T00:00:00","date_gmt":"2001-01-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/using-temporal-data-for-making-recommendations\/"},"modified":"2018-10-16T20:17:15","modified_gmt":"2018-10-17T03:17:15","slug":"using-temporal-data-for-making-recommendations","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/using-temporal-data-for-making-recommendations\/","title":{"rendered":"Using Temporal Data for Making Recommendations"},"content":{"rendered":"<div class=\"asset-content\">\n<p>We treat collaborative filtering as a univariate time series estimation problem: given a user&#8217;s previous votes, predict the next vote. We describe two families of methods for transforming data to encode time order in ways amenable to off-the-shelf classification and density estimation tools, and examine the results of using these approaches on several real-world data sets. The improvements in predictive accuracy we realize recommend the use of other predictive algorithms that exploit the temporal order of data.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We treat collaborative filtering as a univariate time series estimation problem: given a user&#8217;s previous votes, predict the next vote. We describe two families of methods for transforming data to encode time order in ways amenable to off-the-shelf classification and density estimation tools, and examine the results of using these approaches on several real-world data [&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":"Morgan Kaufmann Publishers","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Proceedings of Seventeenth Conference on Uncertainty in Artificial Intelligence, \u00ae Seattle, 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