{"id":38769,"date":"2019-01-25T14:46:54","date_gmt":"2019-01-25T13:46:54","guid":{"rendered":"https:\/\/aton.com\/?p=38769"},"modified":"2023-11-28T17:34:53","modified_gmt":"2023-11-28T16:34:53","slug":"recommender-system-who-knows-you-better-than-yourself","status":"publish","type":"post","link":"https:\/\/aton.com\/en\/recommender-system-who-knows-you-better-than-yourself\/","title":{"rendered":"Recommender System:  Who knows You Better Than Yourself?"},"content":{"rendered":"<h2>As Steve Jobs once said, \u201cpeople do not often know what they want till they see it\u201d.<\/h2>\n<p>Nowadays we are the target of innumerable messages which \u201cadvise\u201d us goods or service acquisitions we would have never made had they not been pulled under our nose, therefore pushing us to new needs or waking up latent ones.<\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-70287 aligncenter\" src=\"https:\/\/aton.com\/wp-content\/uploads\/2019\/01\/reccomendersystem_stevejobs.jpg\" alt=\"\" width=\"854\" height=\"268\" \/><\/p>\n<p>Some concrete examples*:<\/p>\n<ul>\n<li>Two thirds of films watched on Netflix are suggested to users by the famous video streaming platform<\/li>\n<li>Google News: 38% of click-throughs are originated by links suggested by the web giant\u2019s algorithms<\/li>\n<li>Amazon generates a good 35% of its sales from suggestions to users<\/li>\n<\/ul>\n<p>(*)\u00a0<b>Source:\u00a0<\/b>VentureBeat.com <img loading=\"lazy\" decoding=\"async\" class=\"aligncenter  wp-image-37077\" src=\"https:\/\/aton.com\/media\/ventureBeat_netflix.png\" alt=\"Netflix\" width=\"857\" height=\"353\" \/><\/p>\n<p>Behind these focused suggestions there are systems which can be most complex: the <i>recommender systems. <\/i>These systems have the following objectives:<\/p>\n<ul>\n<li>Maintain customer\u2019s loyalty<\/li>\n<li>Increase service users\u2019 traffic volume<\/li>\n<li>Provide a user experience (UX) as focused on expectations as possible<\/li>\n<li>Widen market limits<\/li>\n<\/ul>\n<p>But, above all, make people buy what we want to sell. From a more technical point of view, the problem a <i>recommendation engine <\/i>faces is developing a mathematical model or an \u201cobjective\u201d fuction which can foresee how much a user will desire a product or service. <img decoding=\"async\" class=\"aligncenter size-large wp-image-37088\" src=\"https:\/\/aton.com\/media\/reccomendedsystem_definition-1024x318.png\" alt=\"Reccomended System definition\" width=\"100%\" \/><\/p>\n<p>For every \u201cu\u201d user, it is necessary to choose the \u201ci\u201d element which maximizes the \u201cobjective\u201d function. <img decoding=\"async\" class=\"aligncenter size-large wp-image-37094\" src=\"https:\/\/aton.com\/media\/content-basedfiltering_-collaborativefiltering1-1024x505.png\" alt=\"content-basedfiltering_ collaborativefiltering\" width=\"100%\" \/><\/p>\n<p>There are several recommendation typologies:<\/p>\n<ol>\n<li>based on content: \u201cyou have read a book, I suggest similar books\u201d<\/li>\n<li>based on correlation among users with similar characteristics in their purchase model<\/li>\n<\/ol>\n<p>And it is right here artificial intelligence joins the game: the recommender systems which take advantage of artificial intelligence were born with the aim to advising users about objects they had never bought before! Similarity among users can be calculated in various ways:<\/p>\n<ul>\n<li>social network: the assumption is that two \u00abfriends\u00bb on a social network should have common interests<\/li>\n<li>past valutations: certain particular patterns are \u201cdiscovered\u201d from the analysis of past users\u2019 valutations<\/li>\n<\/ul>\n<p>How is it possible to know whether a <b>recommender system works?<\/b><br \/>\nHere below some valutation meters:<\/p>\n<ul>\n<li><b>Precision<\/b>: Which is the difference between a real rating and its prevision?<\/li>\n<li><b>Diversity<\/b>: How diverse are recommendations?<\/li>\n<li><b>Coverage<\/b>: Which is the space percentage for user elements that can be recommended?<\/li>\n<li><b>Serendipity<\/b>: How surprising are pertinent recommendations?<\/li>\n<li><b>Novelty<\/b>: How surprising are recommendations in general?<\/li>\n<li><b>Relevance<\/b>: How relevant are recommendations?<\/li>\n<\/ul>\n<p>That said, we cannot not consider the fact that recommender systems have a tremendous potential in supporting omnichannel sales, anticipating customers\u2019 wishes.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>As Steve Jobs once said, \u201cpeople do not often know what they want till they see it\u201d. Nowadays we are the target of innumerable messages which \u201cadvise\u201d us goods or service acquisitions we would have never made had they not been pulled under our nose, therefore pushing us to new needs or waking up latent [&hellip;]<\/p>\n","protected":false},"author":4,"featured_media":37091,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false},"class_list":["post-38769","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","keyword-prosperity-en","keyword-sell-out-en"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.2 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Recommender System: Who knows You Better Than Yourself?<\/title>\n<meta name=\"description\" content=\"Behind targeted purchasing suggestions there are some systems that can be very complex: recommender systems. 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