{"id":31092,"date":"2017-06-28T16:16:48","date_gmt":"2017-06-28T13:16:48","guid":{"rendered":"https:\/\/www.altoros.com\/blog\/?p=31092"},"modified":"2021-03-12T04:08:08","modified_gmt":"2021-03-12T01:08:08","slug":"tensorflow-for-recommendation-engines-and-customer-feedback-analysis","status":"publish","type":"post","link":"https:\/\/www.altoros.com\/blog\/tensorflow-for-recommendation-engines-and-customer-feedback-analysis\/","title":{"rendered":"TensorFlow for Recommendation Engines and Customer Feedback Analysis"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_79_2 counter-hierarchy ez-toc-counter ez-toc-transparent ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.altoros.com\/blog\/tensorflow-for-recommendation-engines-and-customer-feedback-analysis\/#Finding_the_right_product\" >Finding the right product<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.altoros.com\/blog\/tensorflow-for-recommendation-engines-and-customer-feedback-analysis\/#Recommending_relevant_products\" >Recommending relevant products<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.altoros.com\/blog\/tensorflow-for-recommendation-engines-and-customer-feedback-analysis\/#Understanding_customer_feedback\" >Understanding customer feedback<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.altoros.com\/blog\/tensorflow-for-recommendation-engines-and-customer-feedback-analysis\/#Want_details_Watch_the_video\" >Want details? Watch the video!<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.altoros.com\/blog\/tensorflow-for-recommendation-engines-and-customer-feedback-analysis\/#Further_reading\" >Further reading<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.altoros.com\/blog\/tensorflow-for-recommendation-engines-and-customer-feedback-analysis\/#About_the_experts\" >About the experts<\/a><\/li><\/ul><\/nav><\/div>\n<h3><span class=\"ez-toc-section\" id=\"Finding_the_right_product\"><\/span>Finding the right product<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>According to <a href=\"https:\/\/www.linkedin.com\/in\/ram-ramanathan-a4839b\/\" target=\"_blank\" rel=\"noopener noreferrer\">Ram Ramanathan<\/a> of Google, there are three key scenarios in e-commerce that can be improved with machine learning: <em>finding<\/em> products of interest, <em>recommending<\/em> relevant items, and <em>analyzing<\/em> customer feedback. This may be applied to a variety of industries, including retail, telco, etc.<\/p>\n<p>At <a href=\"https:\/\/cloud.withgoogle.com\/next\" target=\"_blank\" rel=\"noopener noreferrer\">Google Cloud Next 2017<\/a> in San Francisco, Ram overviewed how TensorFlow and Google Cloud Machine Learning can help to implement these scenarios. The session also featured a demo of a recommendation engine built for a TV services provider, suggesting relevant programs.<\/p>\n<p>Ram Ramanathan highlighted that the textual search becomes kind of out-of-date experience, as a user\u2019s decision what to buy roots back to what he\/she has seen on the web: fashion blogs, mood boards on Pinterest, etc. This way, textual search is hardly of any help when a user actually needs a search by image.<\/p>\n<p><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/tensorflow-to-transoform-retail-processes-ram-ramanathan-at-google-cloud-next-2017-v11.jpg\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/tensorflow-to-transoform-retail-processes-ram-ramanathan-at-google-cloud-next-2017-v11-1024x678.jpg\" alt=\"\" width=\"640\" class=\"aligncenter size-large wp-image-31101\" \/><\/a><\/p>\n<blockquote><p><em>\u201cHave you ever wondered how we can enable customers to search the most relevant and the right product at the right time, and how you can enable them to search easier than just doing the data textual query? Like, the people are more and more visual, how do we enable them to search better?\u201d \u2014Ram Ramanathan, Google<\/em><\/p><\/blockquote>\n<p>The technologies to aid one in enabling such a scenario, as well as numerous others, may be TensorFlow and <a href=\"https:\/\/cloud.google.com\/products\/ai\" target=\"_blank\" rel=\"noopener noreferrer\">Cloud Machine Learning services<\/a>.<\/p>\n<blockquote><p><em>\u201cWe (Google) have cloud machine learning engine that helps you to really drive your machine learning both from a training perspective or prediction perspective. That\u2019s targeted at data scientists and folks who want to really manage the machine learning life cycle from the end-to-end perspective.\u201d \u2014Ram Ramanathan, Google<\/em><\/p><\/blockquote>\n<p>Then, there is a bunch of APIs for <a href=\"https:\/\/cloud.google.com\/vision\/\" target=\"_blank\" rel=\"noopener noreferrer\">image analysis<\/a>, <a href=\"https:\/\/cloud.google.com\/speech-to-text\" target=\"_blank\" rel=\"noopener noreferrer\">audio-to-text conversion<\/a>, <a href=\"https:\/\/cloud.google.com\/solutions\/talent-solution\" target=\"_blank\" rel=\"noopener noreferrer\">job hunting<\/a>, <a href=\"https:\/\/cloud.google.com\/translate\/\" target=\"_blank\" rel=\"noopener noreferrer\">dynamic translation<\/a>, <a href=\"https:\/\/cloud.google.com\/natural-language\/\" target=\"_blank\" rel=\"noopener noreferrer\">natural language processing<\/a>, and <a href=\"https:\/\/cloud.google.com\/video-intelligence\/\" target=\"_blank\" rel=\"noopener noreferrer\">video analysis<\/a> in the cloud.<\/p>\n<p><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/tensorflow-to-transoform-retail-ram-ramanathan-google-cloud-next-2017-v11.jpg\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/tensorflow-to-transoform-retail-ram-ramanathan-google-cloud-next-2017-v11-1024x678.jpg\" alt=\"\" width=\"640\" class=\"aligncenter size-large wp-image-31104\" \/><\/a><\/p>\n<blockquote><p><em>\u201cSo, these are APIs that are really focused around using Google models and Google infrastructure, and providing you with targeted solutions for scenarios.\u201d \u2014Ram Ramanathan<\/em><\/p><\/blockquote>\n<p>Optimization of a search engine is no new thing, but what can really help out is:<\/p>\n<ul>\n<li>finding means to gather image metadata<\/li>\n<li>finding popular logos to get product branding information<\/li>\n<li>recognizing popular objects<\/li>\n<li>finding ways to prevent product fraud<\/li>\n<\/ul>\n<p><a href=\"https:\/\/cloud.google.com\/vision\/\" target=\"_blank\" rel=\"noopener noreferrer\">Cloud Vision API<\/a> may be sufficient in processing image metadata to further deliver such functionality as search by color, for example. The recently developed <a href=\"https:\/\/cloud.google.com\/vision\/docs\/internet-detection\" target=\"_blank\" rel=\"noopener noreferrer\">Web Detection<\/a> feature\u2014underlying Google Image Search\u2014can be employed to find fraud and popular items on the Internet. By crawling through billions of pages daily, the tool indexes the existing images to further find metadata about a particular image and feed it back. So, when a user feeds in an image, he\/she gets back references to different pages containing it.<\/p>\n<p><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/tensorflow-to-transoform-retail-processes-ram-ramanathan-google-cloud-next-2017-v11.jpg\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/tensorflow-to-transoform-retail-processes-ram-ramanathan-google-cloud-next-2017-v11-1024x678.jpg\" alt=\"\" width=\"640\" class=\"aligncenter size-large wp-image-31102\" \/><\/a><\/p>\n<p>However, Ram stressed that Web Detection is not \u201can antidote to all problems.\u201d The thing is that some users may label an image, let say, with a wrong tag\u2014he exemplified a Stormtrooper helmet posted on a Pinterest board, which actually was used to drive a baby shower. Being a popular page with a lots of other pages linked to it, this Pinterest board got highly ranked by Google, and though it obviously features a Stormtrooper, it also contains \u201cpregnancy\u201d metadata.<\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Recommending_relevant_products\"><\/span>Recommending relevant products<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>Ram then moved to a demo to illustrate how it all works in practice. He exemplified YouTube with over a billion hours of videos watched daily. What is the recommendation based upon? The related videos are suggested to a user based on his\/her individual behavior and metadata.<\/p>\n<p><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/tensorflow-to-transoform-retail-ram-ramanathan-google-v11.jpg\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/tensorflow-to-transoform-retail-ram-ramanathan-google-v11-1024x678.jpg\" alt=\"\" width=\"640\" class=\"aligncenter size-large wp-image-31105\" \/><\/a><\/p>\n<blockquote><p><em>\u201cNot just the clickbait, meaning that if you watch a cat video\u2014show more cat videos\u2014but really focus on what\u2019s relevant for this end user. And thinking about time to view and saying is this really a video you really want to look at? That&#8217;s a case of a content perspective.\u201d<br \/>\n\u2014Ram Ramanathan, Google<\/em><\/p><\/blockquote>\n<p>The point is this knowledge can be easily mapped onto the retail scenario. For instance, if someone bought something last year doesn\u2019t necessarily mean he\/she will do it this year again. So, here emerge multiple factors to take on board. These may include seasonality, social events driving user behavior (e.g., this year is <a href=\"http:\/\/www.harrypotter.bloomsbury.com\/uk\/harrypotter20\/\" target=\"_blank\" rel=\"noopener noreferrer\">the 20th anniversary<\/a> of the first Harry Potter book published with numerous branded goods coming to sale), out of stock, etc.<\/p>\n<p>Better product recommendations are enabled through:<\/p>\n<ul>\n<li>history of user behaviour<\/li>\n<li>current product metadata<\/li>\n<li>organization dynamics<\/li>\n<li>environmental factors<\/li>\n<\/ul>\n<p><center><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/tensorflow-to-transoform-retail-recommendation-pipeline-v11.jpg\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/tensorflow-to-transoform-retail-recommendation-pipeline-v11-1024x572.jpg\" alt=\"\" width=\"640\" class=\"aligncenter size-large wp-image-31106\" \/><\/a><small>A common configuration to enable product recommendation with Google solutions (<a href=\"https:\/\/www.youtube.com\/watch?v=mA_JGDYWriY\" target=\"_blank\" rel=\"noopener noreferrer\">Source<\/a>)<\/small><\/center><\/p>\n<p>Following Ram on improving recommendations with TensorFlow, <a href=\"https:\/\/www.linkedin.com\/in\/rajivsalimath\/\" target=\"_blank\" rel=\"noopener noreferrer\">Rajiv Salimath<\/a> of PwC presented how Google delivered a kiosk app to a large company providing subscription TV services in Mexico. The app provides real-time recommendations based on customer viewing habits, preferences, and <a href=\"https:\/\/www.altoros.com\/blog\/tensorflow-in-finance-discussing-predictive-analytics-and-budget-planning\/\">budget<\/a>.<\/p>\n<p><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/tensorflow-to-transoform-retail-rajiv-salimath-a-case-for-telco-v11.jpg\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/tensorflow-to-transoform-retail-rajiv-salimath-a-case-for-telco-v11-1024x678.jpg\" alt=\"\" width=\"640\" class=\"aligncenter size-large wp-image-31103\" \/><\/a><\/p>\n<p>The input data is driven when a user selects something to watch and is fed into a single-layered neural network\u2014built and deployed on TensorFlow.<\/p>\n<p>The challenge was in filtering what to recommend based on little data about a user. To address the issue, <a href=\"https:\/\/en.wikipedia.org\/wiki\/Collaborative_filtering\" target=\"_blank\" rel=\"noopener noreferrer\">collaborative filtering<\/a> was employed. Basically, it means that if you have all the users on an axis and all the TV shows\/packages on the other axis, you get some data for a user or packages and some \u201choles\u201d in a data matrix.<\/p>\n<p><center><a href=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/tensorflow-to-transoform-retail-archtecture-of-an-app-for-telco-provider-v11.jpg\"><img decoding=\"async\" src=\"https:\/\/www.altoros.com\/blog\/wp-content\/uploads\/2017\/06\/tensorflow-to-transoform-retail-archtecture-of-an-app-for-telco-provider-v11-1024x571.jpg\" alt=\"\" width=\"640\" class=\"aligncenter size-large wp-image-31100\" \/><\/a><small>An architecture of the TV series recommendation app (<a href=\"https:\/\/www.youtube.com\/watch?v=mA_JGDYWriY\" target=\"_blank\" rel=\"noopener noreferrer\">Source<\/a>)<\/small><\/center><\/p>\n<p>The initial matrix in the particular case involved 20,000 users and 300 TV shows. However, soon it has grown to over two million users and around 20,000 data points, which poses a real computational pain. That was the reason for using TensorFlow to easily tweak the models and scale the data amount.<\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Understanding_customer_feedback\"><\/span>Understanding customer feedback<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p>According to Ram Ramanathan, product reviews can be a great power behind customer decision making\u2014submitted both on a product page or in social media. Analyzing sentiment is key to working out a better strategy. Being an online retailer, one has to understand what people are talking about your product, what alternative offers come to their minds, what the competitive advantages there are, etc.<\/p>\n<p>In terms of understanding the product perception by customers, one has to find references to the product, analyze the reviews, and look for relevant follow-up items. The product reviews are by far a crucial thing to set your mind on. After digging social media for  reviews, one may apply <a href=\"https:\/\/www.altoros.com\/blog\/natural-language-processing-and-tensorflow-implementation-across-industries\/\">natural language processing<\/a> to analyze what the rumor about the product is, concludes Ram.<\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Want_details_Watch_the_video\"><\/span>Want details? Watch the video!<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><center><iframe loading=\"lazy\" width=\"640\" height=\"360\" src=\"https:\/\/www.youtube.com\/embed\/mA_JGDYWriY\" frameborder=\"0\" allowfullscreen><\/iframe><\/center><\/p>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"Further_reading\"><\/span>Further reading<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<ul>\n<li><a href=\"https:\/\/www.altoros.com\/blog\/natural-language-processing-and-tensorflow-implementation-across-industries\/\">Natural Language Processing and TensorFlow Implementation Across Industries<\/a><\/li>\n<li><a href=\"https:\/\/www.altoros.com\/blog\/machine-learning-for-automating-a-customer-service-chatbots-and-neural-networks\/\">Machine Learning for Automating a Customer Service: Chatbots and Neural Networks<\/a><\/li>\n<li><a href=\"https:\/\/www.altoros.com\/blog\/deep-learning-in-healthcare-finance-and-iiot-tensorflow-use-cases\/\">Deep Learning in Healthcare, Finance, and IIoT: TensorFlow Use Cases<\/a><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3><span class=\"ez-toc-section\" id=\"About_the_experts\"><\/span>About the experts<span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><small><a href=\"https:\/\/www.linkedin.com\/in\/ram-ramanathan-a4839b\/\" target=\"_blank\" rel=\"noopener noreferrer\">Ram Ramanathan<\/a> leads the Machine Learning efforts for the Google Cloud Platform, focused on image and video analysis. Prior to Google, he led product management for BI products at GE Healthcare, as well as for Microsoft SQL Server at Microsoft.<\/small><\/p>\n<p><small><a href=\"https:\/\/www.linkedin.com\/in\/rajivsalimath\/\" target=\"_blank\" rel=\"noopener noreferrer\">Rajiv Salimath<\/a> is an artificial intelligence and machine learning expert and consultant with PwC, who leads a team of researchers and engineers to bring AI\/ML to the PwC products. He was the CEO of a semantic web search company Knewco, the leading broadband over powerlines company Amperion, and the dynamic pricing engine company Haggle. Rajiv is a fellow of the Melton Foundation, an Indian National Talent and Math Scholar, and has a degree in Electronics and Communication Engineering from Bangalore University.<\/small><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Finding the right product<\/p>\n<p>According to Ram Ramanathan of Google, there are three key scenarios in e-commerce that can be improved with machine learning: finding products of interest, recommending relevant items, and analyzing customer feedback. This may be applied to a variety of industries, including retail, telco, etc.<\/p>\n<p>At Google Cloud Next [&#8230;]<\/p>\n","protected":false},"author":3,"featured_media":31119,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","footnotes":"","_links_to":"","_links_to_target":""},"categories":[214],"tags":[748,884,749],"class_list":["post-31092","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tutorials","tag-machine-learning","tag-retail","tag-tensorflow"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>TensorFlow for Recommendation Engines and Customer Feedback Analysis | Altoros<\/title>\n<meta name=\"description\" content=\"Learn how TensorFlow and Google Cloud Machine Learning help to find and recommend products, as well as analyze customer feedback in e-commerce.\" \/>\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.altoros.com\/blog\/tensorflow-for-recommendation-engines-and-customer-feedback-analysis\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"TensorFlow for Recommendation Engines and Customer Feedback Analysis | Altoros\" \/>\n<meta property=\"og:description\" content=\"Finding the right product According to Ram Ramanathan of Google, there are three key scenarios in e-commerce that can be improved with machine learning: finding products of interest, recommending relevant items, and analyzing customer feedback. 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