{"id":313,"date":"2013-09-23T12:12:49","date_gmt":"2013-09-23T16:12:49","guid":{"rendered":"http:\/\/nm.cms-devl.bu.edu\/everett\/?page_id=313"},"modified":"2014-08-06T10:02:18","modified_gmt":"2014-08-06T14:02:18","slug":"dont-do-that-do-this","status":"publish","type":"page","link":"https:\/\/www.bu.edu\/questrom-magazine\/fall13\/dont-do-that-do-this\/","title":{"rendered":"Don&#8217;t Do That\/Do This"},"content":{"rendered":"<p><strong>\u201cIf you liked X, then you\u2019ll love Y!\u201d<\/strong> Online companies like Netflix and Amazon use this personalized recommender system to promote products to consumers with varying degrees of accuracy. Nachiketa Sahoo, assistant professor of information systems, has developed a way to improve the system\u2014and help you create smarter promotions.<\/p>\n<p><strong>Don\u2019t Do That<\/strong> Many companies will survey consumers\u2019 preferences for only a few months, then use that data to recommend products that might appeal to them now. They figure that if consumers\u2019 tastes change over time, then data about their prior preferences is irrelevant.<\/p>\n<p><strong>Do This<\/strong> \u201cDon\u2019t throw out old data, but integrate it intelligently,\u201d Sahoo says. \u201cThough a consumer\u2019s tastes might have changed, his or her previous tastes might be useful in making recommendations to other consumers. For example, I had certain preferences when I was 15. Though I\u2019m 30 now, the data about my old preferences could be used to make recommendations to consumers who are currently 15. Also, based on what we know about an individual, we can predict what he or she might like in the future. Instead of making recommendations based on who consumers are now, try to figure out who they are going to become.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u201cIf you liked X, then you\u2019ll love Y!\u201d Online companies like Netflix and Amazon use this personalized recommender system to promote products to consumers with varying degrees of accuracy. Nachiketa Sahoo, assistant professor of information systems, has developed a way to improve the system\u2014and help you create smarter promotions. Don\u2019t Do That Many companies will [&hellip;]<\/p>\n","protected":false},"author":2184,"featured_media":0,"parent":26,"menu_order":4,"comment_status":"closed","ping_status":"closed","template":"story.php","meta":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v19.4 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Don&#039;t Do That\/Do This - Questrom Magazine<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.bu.edu\/questrom-magazine\/fall13\/dont-do-that-do-this\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Don&#039;t Do That\/Do This - Questrom Magazine\" \/>\n<meta property=\"og:description\" content=\"\u201cIf you liked X, then you\u2019ll love Y!\u201d Online companies like Netflix and Amazon use this personalized recommender system to promote products to consumers with varying degrees of accuracy. 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