Abstract
Aiming at the problem that the single model of the traditional recommendation system cannot accurately capture user preferences, this paper proposes a hybrid movie recommendation system and optimization method based on weighted classification and user collaborative filtering algorithm. The sparse linear model is used as the basic recommendation model, and the local recommendation model is trained based on user clustering, and the top-N personalized recommendation of movies is realized by fusion with the weighted classification model. According to the item category preference, the scoring matrix is converted into a low-dimensional, dense item category preference matrix, multiple cluster centers are obtained, the distance between the target user and each cluster center is calculated, and the target user is classified into the closest cluster. Finally, the collaborative filtering algorithm is used to predict the scores for the unrated items of the target user to form a recommendation list. The items are clustered through the item category preference, and the high-dimensional rating matrix is converted into a low-dimensional item category preference matrix, which further reduces the sparsity of the data. Experiments based on the Douban movie dataset verify that the recommendation algorithm proposed in this article solves the shortcomings of a single algorithm model to a certain extent and improves the recommendation effect.
Highlights
With the rapid development of information technology and social networks, the data generated by the Internet has risen exponentially in recent years, and the era of big data is coming
Because the ultimate goal of the improved algorithm in this paper is to generate a movie recommendation list for current users in line with their interests and preferences, rather than predict how much the target users will score for the movie, so this paper adopts the form of top-N recommendation list when recommending movies for the target users. e commonly used important indicators to measure the accuracy of recommendation system are accuracy and recall, so this paper uses accuracy and recall as the evaluation metrics of recommendation algorithm
At present, personalized recommendation system has been widely used in video websites, music websites, e-commerce, news reading websites, and other fields and has attracted more and more attention from scholars and industry. is paper proposes a hybrid movie recommendation system optimization based on weighted classification and user collaborative filtering algorithm. e research focus of the algorithm is to consider the user’s behavior information and item category preference information at the same time
Summary
With the rapid development of information technology and social networks, the data generated by the Internet has risen exponentially in recent years, and the era of big data is coming. With the increase of data, it is more and more difficult for people to find the information they really want from the massive data. At this time, the recommender system can play the maximum application value [1, 2]. It recommends information that meets users’ personalized needs to users by analyzing users’ historical behavior, applying recommendation algorithm, or establishing users’ interest model [5, 6]. E initial dataset included 3328 users, 28615 movies, and 389184 ratings. The experimental dataset includes 3156 users, 3524 movies, 302673 ratings, and 4232 movie tags. According to the different dimension reduction methods, many clustering methods based on dimension reduction are produced, such as Kohonen self-organizing feature mapping, principal component analysis, multidimensional scaling, and so on
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