Hi am vimala i would like to get details on matlab code for collaborative filtering using clustering ..My friend Justin said matlab code for collaborative filtering using clustering will be available here and now i am living at india and now am doing research .i need help on matlab code for collaborative filtering using clustering tc

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Hi am vimala i would like to get details on matlab code for collaborative filtering using clustering ..My friend Justin said matlab code for collaborative filtering using clustering will be available here and now i am living at india and now am doing research .i need help on matlab code for collaborative filtering using clustering

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Hi I am vimala i would like to get details on matlab code for collaborative filtering using clustering ..My friend Justin said matlab code for collaborative filtering using clustering will be available here and now i am living at india and now am doing research .i need help on matlab code for collaborative filtering using clustering tc

In the series of implementing Recommendation engines, in my previous blog about recommendation system in R, I have explained about implementing user based collaborative filtering approach using R. In this post, I will be explaining about basic implementation of Item based collaborative filtering recommender systems in r.
Item based Collaborative Filtering:
Unlike in user based collaborative filtering discussed previously, in item-based collaborative filtering, we consider set of items rated by the user and computes item similarities with the targeted item. Once similar items are found, and then rating for the new item is predicted by taking weighted average of the user’s rating on these similar items.
Collaborative filtering (CF) is a technique used by some recommender systems.[1] Collaborative filtering has two senses, a narrow one and a more general one.[2]

In the newer, narrower sense, collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). The underlying assumption of the collaborative filtering approach is that if a person A has the same opinion as a person B on an issue, A is more likely to have B's opinion on a different issue x than to have the opinion on x of a person chosen randomly. For example, a collaborative filtering recommendation system for television tastes could make predictions about which television show a user should like given a partial list of that user's tastes (likes or dislikes).[3] Note that these predictions are specific to the user, but use information gleaned from many users. This differs from the simpler approach of giving an average (non-specific) score for each item of interest, for example based on its number of votes.

In the more general sense, collaborative filtering is the process of filtering for information or patterns using techniques involving collaboration among multiple agents, viewpoints, data sources, etc.[2] Applications of collaborative filtering typically involve very large data sets. Collaborative filtering methods have been applied to many different kinds of data including: sensing and monitoring data, such as in mineral exploration, environmental sensing over large areas or multiple sensors; financial data, such as financial service institutions that integrate many financial sources; or in electronic commerce and web applications where the focus is on user data, etc. The remainder of this discussion focuses on collaborative filtering for user data, although some of the methods and approaches may apply to the other major applications as well.