A collaborative filtering recommendation algorithm and device based on similarity and similarity credibility
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A collaborative filtering recommendation and similarity technology, applied in the field of recommendation algorithms, can solve problems such as not being fully solved
Active Publication Date: 2018-12-07
WUHAN UNIV OF TECH
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[0004] Compared with the neighborhood-based collaborative filtering recommendation algorithm, the matrix factorization model effectively alleviates the problem of scoring sparsity, but the problem has not been fully resolved
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[0060] One, at first introduce method principle of the present invention, the present invention mainly comprises:
[0061] Step 1, establish the user-item rating matrix R, the user-item rating matrix R includes several elements R u,i , R u,i Indicates the rating of user u on item i, u=1,2,..N, i=1,2,3,..M, N represents the number of users in the recommendation system, M represents the number of items in the recommendation system;
[0062] Step 2, establish scoring indicator matrix I R , scoring indicator matrix I R includes several elements Indicates whether user i has rated item j, and if so, then if not, then
[0063] Step 3, calculate the user similarity matrix D through cosine similarity, the user similarity matrix D includes several elements D u,p ,D u,p Indicates the similarity between user u and user p,
[0064]
[0065] Among them, I represents the set of items jointly evaluated by users u and p, represents the average rating of all rated items by user...
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Abstract
The invention provides a collaborative filtering recommendation algorithm and device based on similarity and similarity credibility. The algorithm restricts the included angle cosine of a potential eigenvector through a similarity relation on the basis of a probability matrix decomposition model, and influences the constraint weight through the similarity credibility. Due to the constraint of similarity credibility, the algorithm effectively weakens the influence of sparsity on similarity relations, enhances the learning ability of important similarity relations, and alleviates the sparsity problem of scoring. Experimental results show that the proposed algorithm is superior to other similar algorithms. The next work is to apply this algorithm to the actual recommendation system to overcome the data sparsity problem and the poor recommendation effect of a conventional recommendation system in the practical application.
Description
technical field [0001] The present invention relates to the field of recommendation algorithms, and mainly improves the Probabilistic Matrix Factorization (PMF) model to alleviate the problem of data sparsity and improve the recommendation effect, and specifically relates to a collaborative filtering recommendation based on similarity and similarity credibility Algorithms and devices. Background technique [0002] The recommendation system collects users' historical ratings, interactions (browsing, favorites, "likes", "dislikes" and other interactive behaviors), user portraits (age, occupation, gender, etc.), social networks and context (time, location, activity status, etc.) , people around, etc.), analyze the user's historical interests and preferences, dig out the user's favorite items (video, audio, books, dishes, Web services, etc.), and then actively recommend relevant information to the user to meet the user's needs. user's individual needs. The recommendation algor...
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