types of recommendation engines the major area of distinction in different recommendation engines comes from the entity that they assume is the most important in the process of generating recommendations there are different options for choosing the central entity and that choice will determine the type of recommendation engine we will develop user-based recommendation enginesin these types of recommendation enginesthe user is the central entity the algorithm will look for similarities among users and on the basis of those similarities will come up with the recommendation content-based recommendation engineson the other end of the recommendation engine spectrumwe have the content-based recommendation engine in thesethe central entity is the content that we are trying to recommendfor examplein our case the entity will be songs we are trying to recommend these algorithm will attempt to find features about the content and find similar content then these similarities will be used to make recommendations to the end users hybrid-recommendation enginesthese types of recommendation engines will take into account both the features of the users and the content to develop recommendations these are also sometimes termed as collaborative filtering recommendation engines as they "collaborateby using the similarities of content as well as users these are one of the most effective classes of recommendation enginesas they take the best features of both classes of recommendation engines utility of recommendation engines in the previous we discussed an important requirement of any organizationunderstanding the customer this requirement is made more important for online businesseswhich have almost no physical interaction with their customers recommendation engines provide wonderful opportunities to these organizations to not only understand their clientele but also to use that information to increase their revenues another important advantage of recommendation engines is that they potentially have very limited downside the worst thing the user can do is not pay attention to the recommendation made to him the organization can easily integrate crude recommendation engine in its interaction with the users and thenon the basis of its performancemake the decision to develop more sophisticated version although unverified claims are often made about the impact of recommendation engines on the sales of major online service providers like netflixamazonyoutubeetc an interesting insight into their effectiveness is provided by several papers we encourage you to read one such paper at amazonaws com/ba / cb baa pdf the study claims that good recommendation engine tends to increase sales volume by around and also leads customers to discover more productswhich in turns adds to positive customer experience before we start discussing various recommendation engineswe would like to thank our friend and fellow data scientistauthor and course instructorsiraj raval for helping us with major chunk of the code used in this pertaining to recommendation engines as well as sharing his codebase for developing our recommendation engines (check out siraj' github at be modifying some of his code samples to develop the recommendation engines that we discuss in the subsequent sections interested readers can also check out siraj' youtube channel at www youtube com/csirajology where he makes excellent videos on machine learningdeep learningartificial intelligence and other fun educational content