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A Two-stage Collaborative Filtering Algorithm Based on Random Walking and Cluster-based Smoothing

Author: ZhouJunJun
Tutor: WangMingWen
School: Jiangxi Normal University
Course: Computer Science and Technology
Keywords: Collaborative Filtering Random Walk Sparsity Correlation description MAE
CLC: TP391.3
Type: Master's thesis
Year: 2011
Downloads: 13
Quote: 0
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Abstract


With the rapid development of the Internet , online information increase exponentially . Difficult for users to find the products or information they are interested in in a short period of time . In order to solve the information overload problem , personalized recommendation system came into being , it is recommended to the user based on the characteristics of the user 's interest in buying behavior , its information or are interested in commodities , is a personalized service system . Collaborative filtering recommendation technology is widely used in the personalized recommendation technology , it is according to the user 's existing evaluation information or purchase history analysis of user preferences , and then its recommended items based on user interest . With the increasing number of users and projects , and project ratings information is very limited , so the user - the project ratings Matrix extreme sparse , performance and recommendation quality of the recommendation system , have been seriously affected . This paper proposes a collaborative filtering algorithm based on two-stage random walk and clustering smooth data sparseness problem . Offline phases: computing projects , the usual method is a statistical correlation between the direct calculation of project , such as the cosine similarity , but these methods are ineffective in sparse data . This paper presents a novel method by weighted cumulative step transition probability described projects . Clustering is smooth , according to the project correlation matrix the project clustering using the clustering information of the the ungraded data smoothing . Online stage : the correlation between the offline stage project neighbors find the target and predict the target user's ratings . The proposed method can enhance the correlation between items described , especially in the case of relatively sparse training sets , using conventional similarity calculation method can not be effectively describe the actual relationship between the project , and the method will work well . The experiments show that the correlation matrix Find neighbors according to the method of project will be more accurate , can effectively mitigate the impact of the sparse data to improve the performance of the recommended .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Retrieval machine
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