| | Algorithm input: user rating dataset, current user, system parameters (user similarity threshold sim, number of recommended items Top-N). | | | Algorithm output: Recommends a collection of items for the user. | | | Step l: The first Map Reduce task, data source transformation processing, collects the score of each user on the project by user, and constructs a user-project scoring matrix. The Map stage receives the input <key, value>, key is the current input line number, value is the content of the corresponding line, converts the input data into a key-value pair with UserID as the key, And Item ID and Rating as the value. The Reduce function combines items of the same user and outputs a user-item scoring matrix with key as UserID and value as list (Item ID, Rating). | | | Step 2: The second Map Reduce task calculates the similarity between user IDs of all users. After entering the user item rating matrix output by the Reduce function in Stepl, the project score under each user is extracted, with the user pair (UserID(1), UserIDi)) as the key, and the corresponding item (Rating(i), Rating()) is output to the reduce stage as value to calculate the degree of similarity between users, and the computed result is <(UserID(1), UserIDj), similarity> persisted into the distributed file system. | | | Step 3: The third Map Reduce task, which makes the target user recommendation based on the similarity between the User IDs of all users. The Map stage finds the N nearest neighbors with the highest similarity of UserID of each user, calculates the predictive score of each user UserID on the unrated item Item ID according to the algorithm (3), and the reduce stage is obtained by sorting the score Top-N recommended project results are returned to the user. |
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