{"id":484,"date":"2015-08-28T07:28:43","date_gmt":"2015-08-28T04:28:43","guid":{"rendered":"http:\/\/kusuaks7\/?p=89"},"modified":"2025-02-06T15:04:50","modified_gmt":"2025-02-06T15:04:50","slug":"leveraging-on-a-recommender-to-boost-your-e-commerce","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/leveraging-on-a-recommender-to-boost-your-e-commerce\/","title":{"rendered":"Leveraging on a Recommender to Boost your e-Commerce"},"content":{"rendered":"\t\t
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\nThe Problem<\/b>\u00a0<\/b><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIf you already own an e-commerce storefront like Amazon.com or Cold Stone Creamery, then you probably recognize the utmost importance of having a sound recommendation system built into your e-commerce site\u0097so that when customers make purchases, they are provided with additional purchase suggestions or offers based on their past selections. A good recommender almost always help sales growth. One problem is that very often, e-commerce business owners do not have in-house data scientists or product developers. On the other hand, these entrepreneurs may not have a business large enough to justify outsourcing the development project for a recommendation system. On rare occasions, the owner himself might be a data scientist or a keen programmer, but that does not guarantee that he will possess the right insights to develop an effective recommender.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nSo how can this common and growing problem be solved?<\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Two educated minds collaborate to solve the problem<\/b><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The e-book titled Practical Machine Learning Innovations in Recommendation<\/a><\/i><\/b> published by O’Reilly upholds some tested programming practices that can kick start any average product-development team to build reasonably effective recommendation systems for e-commerce. Co-authored by Ted Dunning and Ellen Friedman, this book discusses extensive practical techniques for developing recommenders in the Apache Hadoop environment.\u00a0<\/b><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Chief Applications Architect at MapR Technologies, happens to be a PMC member of the Apache Mahout, Apache Zoo Keeper, and Apache Drill projects, and is also the mentor for Apache Storm. Ellen Friedman is\u00a0<\/b>a consultant and commentator with experience writing about big data. With Ted’s technical background and Ellen’s Bioscience research and publications background they have jointly created a mini bible on building recommenders with Hadoop<\/i>.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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As you may have noticed from your own online shopping experiences that an appealing recommendation system not only helps you make buying choices, but they may actually enhance the seller’s business image in your eyes. In a digital age, a tool like a product or service recommender is an implicit marketing tool that does its job silently.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tThis book indicates that strangely enough, the mathematical algorithms form a small part of the total development effort. The main thrust behind building a good recommendation system comes from providing the right data to the recommendation engine<\/i>!\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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How to build an effective recommender<\/b>\u00a0<\/b><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIf you can afford it, an easier way to install a recommender is by outsourcing the project to a reputed machine-learning consultancy company. Many consultants can supply quick and effective simulations along with recommenders. The strategy they usually employ is\n
throwing a huge collection of algorithms at each problem, and\u0097based on extensive experience in analyzing such situations\u0097selecting the algorithm that gives the best outcome.<\/blockquote>\nAs the Hadoop technology platform continues to evolve, big data projects will gradually become more cost-friendly.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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The theory of co-occurrence<\/b><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tWithout going into unnecessary technical details, it is worth mentioning that central to the theme of creating an effective recommender model is the theory of co-occurrence. This theory relies on capturing substantial user histories to study purchase-behavior patterns. The book provides an in-depth discussion on how co-occurrence works with simulations.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tPractical Machine Learning Innovations in Recommendation\u00a0<\/i>contains the following chapters:\n\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tChapter 1: Practical Machine Learning\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tChapter 2: Careful Simplification\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tChapter 3: What I Do, Not What I Say\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tChapter 4: Co-occurrence and Recommendation\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tChapter 5: Deploy the Recommender\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tChapter 6: Example: Music Recommender\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tChapter 7: Making It Better\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tChapter 8: Lessons Learned\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAppendix A: Additional Resources\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIf you have decided to go in-house<\/b>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tYou have a small, development team in place and are willing to try building a system in-house, provided you get reliable guidance\u0097then this book is for you! \u00a0The book models a tutorial approach\u0097taking readers through a step-by-step process from beginning to end of building a recommendation system. The early chapters introduce simple, understandable concepts, then slowly progress towards more difficult concepts of making a system work. Chapter 3 presents an excellent overview of how to select user input data for the predictive analysis part of the project. Some nifty methods for collecting user behavior data has been provided in this chapter. Chapters 7 and 8 are the troubleshooting chapters of the book \u0097they skillfully navigate the readers into systematic, product improvement techniques. The book can serve as a constant reference or guide while constructing a developmental model. Chapter 6 showcases an actual working system, which can serve as an inspiration to hesitant innovators! The appendix provides some highly acclaimed publications that can certainly expand an inexperienced developer\u0092s intellectual horizon.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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<\/h3><\/div><\/div><\/div><\/div><\/div><\/section><\/div> (more…)<\/span><\/a>","protected":false},"excerpt":{"rendered":"

The Problem\u00a0 If you already own an e-commerce storefront like Amazon.com or Cold Stone Creamery, then you probably recognize<\/p>\n","protected":false},"author":11,"featured_media":2755,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[136],"ppma_author":[1606],"class_list":["post-484","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-retail-analytics"],"authors":[{"term_id":1606,"user_id":11,"is_guest":0,"slug":"cameron-turner","display_name":"Cameron Turner","avatar_url":{"url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2024\/09\/cameron.jpeg","url2x":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2024\/09\/cameron.jpeg"},"user_url":"","last_name":"Turner","first_name":"Cameron","job_title":"","description":""}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/484","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/11"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=484"}],"version-history":[{"count":4,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/484\/revisions"}],"predecessor-version":[{"id":37252,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/484\/revisions\/37252"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/2755"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=484"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=484"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=484"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=484"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}