{"id":25171,"date":"2015-12-01T21:01:59","date_gmt":"2015-12-01T13:01:59","guid":{"rendered":"https:\/\/www.hongkiat.com\/blog\/?p=25171"},"modified":"2023-04-06T19:12:34","modified_gmt":"2023-04-06T11:12:34","slug":"amazon-recommendation-engine-shopping","status":"publish","type":"post","link":"https:\/\/www.hongkiat.com\/blog\/amazon-recommendation-engine-shopping\/","title":{"rendered":"Smart Holiday Shopping with Amazon’s Recommendation Engine"},"content":{"rendered":"

At Christmas time many of us are struggling with choosing the most suitable gift for our loved ones. Gift hunting for Christmas<\/a> may require days of planning, sometimes weeks. Apart from its certain fun and joyful part, Christmas shopping can be a time-consuming and stressful experience<\/strong>.<\/p>\n

Luckily in the age of advanced technology, there are freely available tools that can make the shopping process much more efficient and productive. In this post I will show you how one of the world\u2019s largest retailer site, Amazon.com can assist you in finding the best gifts<\/strong> for your friends and family within a reasonable timeframe<\/strong> with the help of its smart recommendation engine.<\/p>\n

Personalized User Experience<\/h2>\n

The world\u2019s most successful websites such as Amazon, Facebook, and Youtube are so popular because they offer a personalized user experience to everyone<\/strong>.<\/p>\n

Personalizing user experience basically means that companies observe their users while they navigate through their site and perform different actions<\/strong> on it. They collect the data into neat databases, and analyze it.<\/p>\n

Isn\u2019t that harmful for privacy? From a certain point of view, yes it is; these companies may know more about us than our closest friends or even ourselves. On the other hand, they offer us a service that can make our lives easier<\/strong>, and our decisions better-informed.<\/strong><\/p>\n

If we look at it from a transactional point of view we \"pay\" for an enhanced user experience and comfort, with a part of our privacy.<\/p>\n

<\/figure>\n

Of course, legal battles between online content providers and authorities are constant, just think about the not so loved EU cookie law, but as opting out is less and less a realistic option for someone who wants to enjoy a 21st century lifestyle, it can be useful to understand how personalized recommendations work behind the scenes.<\/p>\n

The Tech Behind Amazon\u2019s Recommendations<\/h2>\n

While navigating through Amazon\u2019s website, we can find personalized recommendations everywhere under headings like \u201cNew For You\u201d, \u201cRecommendations For You in Kindle Store\u201d, \u201cFeatured Recommendations\u201d, \u201cCustomers Who Bought This Item Also Bought\u201d, and many others.<\/p>\n

Recommendations have been integrated into every single part<\/strong> of the purchasing process from the product lookup to the checkout<\/strong>. Customized recommendations are powered by an intelligent recommendation engine that gets to know users better and better as they use the site.<\/p>\n

\"Amazon<\/figure>\n

To understand recommendation systems better, it\u2019s a good idea to think about them as advanced versions of search engines<\/strong>. When we look up an item on Amazon, it doesn\u2019t only return the results, but also makes predictions about the products we may need<\/strong>, and shows its recommendations for us.<\/p>\n

Recommender systems use different kinds of machine learning algorithms, and they have become commercially implementable with the evolution of big data technology<\/a>. Recommendation engines are data-driven products<\/strong>, as they need to find the most relevant small dataset in the huge ocean of big data<\/strong>.<\/p>\n

\"Big<\/figure>\n

The computational task that recommendation systems need to solve is the combination of predictive analysis and filtering<\/strong><\/p>\n

They use one of the following approaches:<\/p>\n

(1) <\/em>Collaborative Filtering<\/strong>, that looks for similarities between collaborative data<\/strong> such as purchases, ratings, likes, upvotes, downvotes in:<\/p>\n

    \n
  • either the user-user matrix<\/strong>, where recommendations are generated based on the choices of other customers who liked, purchased, rated, etc. similar products,<\/li>\n
  • or the product-product matrix<\/strong>, where the recommendation engine returns products that are similar in purchases, likes, ratings, etc. to the products the current user has purchased, rated, liked, upvoted before<\/em><\/li>\n<\/ul>\n

    Amazon uses the latter, as it\u2019s more advanced (see in detail in the next section).<\/p>\n

    (2) <\/em>Content-Based Filtering<\/strong>, that makes predictions based on the similarities of objective characteristics of products such as specifics, descriptions, authors, and also on the previous preferences of the user (that here are not compared to the preferences of other users).<\/p>\n

    (3) <\/em>Hybrid Filtering<\/strong>, that uses some kind of combination of collaborative and content-based filtering.<\/p>\n

    The Product-Product Matrix<\/h2>\n

    The traditional way of collaborative filtering makes use of the user-user matrix, and above a certain amount of data it has serious performance problems.<\/p>\n

    To match the preferences, ratings, purchases of all users, and find those who are the closest to the active user<\/strong>, the recommendation engine has to analyze every user<\/em> in the database and match them against the current one.<\/p>\n

    If we think about Amazon\u2019s size, it\u2019s clear that this kind of filtering isn\u2019t feasible for them, so Amazon\u2019s engineers developed an upgraded version of the former method, and called it item-to-item collaborative filtering<\/strong>.<\/p>\n

    Item-to-item collaborative filtering keeps collaborative success<\/strong> as the benchmark instead of the objective qualities of a product (see content-based filtering above), but runs the queries in the product-product matrix that means it doesn\u2019t compare users, instead it compares products.<\/p>\n

    The recommendation engine takes a look at the products we have purchased, rated, put onto our wish list, commented on, etc. so far, then looks up other items in the database that has similar rates and purchases, aggregates them, then returns the best matches as recommendations.<\/p>\n

    \"Product<\/figure>\n

    How To Get Better Recommendations<\/h2>\n

    Back to Christmas shopping, it\u2019s possible to train Amazon\u2019s recommendation engine to get better results<\/strong>. If you only have a vague idea about what to buy for a loved one, you don\u2019t have to do anything else other than leaving traces on the website while browsing.<\/p>\n

    For the sake of this post I\u2019ve tried this out by myself.<\/p>\n

    My starting point was that I wanted to find some smaller office furniture, but didn\u2019t exactly know what. So I entered some related keywords into the search bar, and began to browse the results. I put the items I liked into my wishlist, rated some reviews as \u201cHelpful\u201d, dropped some office furniture into my basket.<\/p>\n

    If I\u2019ve ever bought a similar item on Amazon, it would have been quite useful to write a review on it, but actually I couldn\u2019t do that (you can only write reviews on products that you\u2019ve already purchased).<\/p>\n

    After about 10-15 minutes I stopped, and clicked on my Recommendation pages (which can be found under the \u201c[Your Name]\u2019s Amazon\u201d menu point). Before the experiment I only had books on this page, as that\u2019s what I usually buy on Amazon. After my extensive lookup, the books have disappeared and have been replaced by cool office furniture, as you can see below.<\/p>\n

    \"Private<\/figure>\n

    Tweaking The Engine<\/h2>\n

    It\u2019s possible to further train the recommendation engine, as below each recommendation there\u2019s a \u201cWhy recommended?\u201d link<\/strong>. Among my recommendations you can see a bin liner (last item), which isn\u2019t an office furniture product and one I don\u2019t want to buy for Christmas.<\/p>\n

    So let\u2019s see why it\u2019s here.<\/p>\n

    After clicking on the link, Amazon informs me it was recommended because I put a certain office computer chair into my basket. Well, that\u2019s an interesting connection, but I still don\u2019t need it.<\/p>\n

    I have two options here, I can either tick the \u201cNot interested\u201d checkbox next to the bin liner, or the \u201cDon\u2019t use for recommendations\u201d next to the office chair. I tick the \u201cNot interested\u201d checkbox.<\/p>\n

    \"Why<\/figure>\n

    And at this point the bin liner has disappeared, replaced with another recommended product, which means I\u2019m one step closer to the perfect gift.<\/p>\n

    \"Private<\/figure>\n

    Too bad if I\u2019ll ever need that exact bin liner in the future though. Oh wait. I\u2019ve found the solution for that. Under the \u201cImprove Your Recommendations\u201d menu point, I can edit the items I\u2019ve marked with the \u201cNot interested\u201d label<\/p>\n

    When I have found my imagined gift hunt, I can simply uncheck the products I may want to see among my recommendations in the future.<\/p>","protected":false},"excerpt":{"rendered":"

    At Christmas time many of us are struggling with choosing the most suitable gift for our loved ones. Gift hunting for Christmas may require days of planning, sometimes weeks. Apart from its certain fun and joyful part, Christmas shopping can be a time-consuming and stressful experience. Luckily in the age of advanced technology, there are…<\/p>\n","protected":false},"author":146,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[3398],"tags":[1101,4650,2369],"topic":[4521],"class_list":["entry-content","is-maxi"],"acf":[],"yoast_head":"\nSmart Holiday Shopping with Amazon's Recommendation Engine - Hongkiat<\/title>\n<meta name=\"description\" content=\"At Christmas time many of us are struggling with choosing the most suitable gift for our loved ones. 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