figure - recommendation by the popularity recommendation engine item similarity based recommendation engine in the last sectionwe witnessed one of the simplest recommendation engines in this section we deal with slightly more complex solution this recommendation engine is based on calculating similarities between user' items and the other items in our dataset before we proceed further with our development effortlet' describe how we plan to calculate "itemitemsimilaritywhich is central to our recommendation engine usually to define similarity among set of itemswe need feature set on the basis of which both items can be described in our case it will mean features of the songs on the basis of which one song can be differentiated from another although as we don' have ready access to these features (or do we?)we will define the similarity in terms of the users who listen to these songs confusedconsider this mathematical formulawhich should give you little more insight into the metric similarityij intersection(usersi usersj )/union(usersi usersjthis similarity metric is known as the jaccard index (indexand in our case we can use it to define the similarities between two songs the basic idea remains that if two songs are being listened to by large fraction of common users out of the total listenersthe two songs can be said to be similar to each other on the basis of this similarity metricwe can define the steps that the algorithm will take to recommend song to user