An Improved Item-Based Collaborative Filtering Algorithm
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Based on an analysis of the problems of multiple interests of the user and multiple contents of the item existing in the collaborative filtering recommendation system, an improved item-based collaborative filtering algorithm is proposed. This new algorithm takes synthetically into account the influence of item attributes and user ratings. Experimental results indicate that the algorithm can satisfactorily solve the problems of multiple interests of the user and multiple contents of the item and provide better recommendation results even if the user ratings are very sparse.Cite
To deal with the sparsity and expansibility of traditional collaborative filtering algorithm, a collaborative filtering algorithm based on item rating was proposed in this paper. The method can calculation the item ratings of project that have not rated based on the analysis of the item characteristic information, and use item-based collaborative filtering algorithm to find the similar items. Moreover, the paper puts forward a new formula to compute the rating values of the item that users have not rated. The experiment results demonstrate that the new algorithm could improve the accuracy of recommendation under the condition of the extreme sparsity of user rating data.
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After analyzing cold-start,sparse and real-time issues in the process of collaborative filtering,we propose an improved collaborative filtering recommendation algorithm based on user and item clustering combination.By predicting the neighboring users with the use of collaborative filtering algorithm based on clustering technology and users,a recommendation list based on final rating scores of unrated target items is obtained,thus the shortcoming of collaborative filtering in the recommendation of new items is compensated,and simultaneously the sparse problem is solved.Then time weight is increased during predicting rating for the considerations of more weight for the latest user interest,thus the proposed algorithm can enhance the quality of recommendations.
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After analyzing the sparsity of traditional collaborative filtering algorithm and the merit and demerit of current solutions,this paper proposed a collaborative filtering recommendation algorithm based on user characteristics and item attributes which was mainly based on the item-based collaborative algorithm. It predicted the unrated items by analyzing different users' interests to various attributes of items and integrating the attributes of rated items to reduce the sparsity of data sets,and then to improve the accuracy of items' similarity calculation. The experimental results based on MovieLens data set show that the sparsity of extreme data sets can be reduced effectively,to a certain extent the proposed algorithm alleviates the cold starting problem in collaborative filtering algorithm and achieves better prediction accuracy.
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Collaborative filtering is a very important technology in e-commerce. Unfortunately, with the increase of users and commodities, the user rating data is extremely sparse, which leads to the low efficient collaborative filtering recommendation system. To address these issues, an optimized collaborative filtering recommendation algorithm based on item is proposed. While calculating the similarity of two items, we obtain the ratio of users who rated both items to those who rated each of them. The ratio is taken into account in this method. The experimental results show that the proposed algorithm can improve the quality of collaborative filtering.
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Traditional User-based collaborative filtering recommendation algorithm in the calculation of similarity between users only considers the users' score to the item, but not takes the difference of rated items into account. Aiming at the shortcomings of the traditional method, with the practical application of recommendation system, a new collaborative filtering recommendation algorithm is proposed which selects neighbors for each target item. Ratings based on item type determine preliminary neighbors from the users, for each target item computing neighbors of the target user, and in the case of not rating the target item, the expanded neighbors are considered, finally predicting and recommending target items. The experimental results show that the algorithm improves the accuracy of similarity calculation and the error performance when comparing with other classic algorithms, and effectively alleviates the user rating data sparsity problem, while improving the accuracy of the forecast.
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The collaborative filtering recommendation algorithm based on user is becoming the more personalized recommendation algorithm. But when the user evaluation for goods is very small and the user didnt evaluate the item, the commodity recommendation based on the item evaluation of user may not be accurate, and this is the sparseness in the collaborative filtering algorithm based on user. In order to solve this problem, this paper presents a collaborative filtering recommendation algorithm based on user and item. The experimental results show that this method has smaller MAE and greatly improve the quality of the recommendation in the recommendation system.
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Traditional similarity measure methods work poor in this situation,which makes the quality of recommendation system decrease dramatically. To address this issue a novel collaborative filtering algorithm based on content and item rating prediction is proposed. This method predicts item ratings that usesr have not rated based on content prediction and then uses item-based collaborative filtering to find similar items and make a prediction. The experiment results suggeste that this method can efficiently improve the extreme sparsity of user rating data,improve accuracy of recommendation using item-based collaborative filtering,and provide better recommendation results than nearest neighborhood collaborative filtering algorithms.
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Although Item-based collaborative filtering recommendation algorithm is one of the most successful technologies in the recommendation systems,it still has such problem as poor recommendation quality.This paper presents an optimized Item-based collaborative filtering recommendation algorithm.In this paper,the calculation of similarity between items,the selection of neighbor items and prediction of ratings are optimized,which make the recommended result more meaningful and accurate.It can be proved that the optimized algorithm can solve the problem of the similarity measurement inaccuracy caused by the sparsity of data.The experiment results show that the algorithm is successful.
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