Recommendation system Based On Cosine Similarity Algorithm
Publication Date : 06/09/2017
Recommender system recommends the object based upon the similarity measures. Similarity between this objects can help in organizing similar kind of objects. Similarity can also be seen as the numerical distance between multiple objects that represented as value between the range of 0 (not similar at all) and 1 (completely similar).Similarity are highly subjective in nature and dependent on the domain and application. , system build a model from a user’s past activities as well as similar decisions made by other users; then use that model to predict items(or ratings for items) that user may have an interest in. In this model, a user rates a set of items based on which we find the similarities between the users who has nearly same ratings for a set of items, similarity is calculated based on cosine similarity method. After finding the similar object, we recommend relevant item sets to the users who are similar with the users who had already rated the items which we had recommended. This feature is extremely beneficial for the users as well as the website because an item that seems excellent to one person may seem dull for the other to buy. It basically tries to find the users which are similar to the current user behavior.
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