A Degree-Based Method to Solve Cold-Start Problem in Network-Based Recommendation
Recommender systems have become increasingly essential in fields where mass personalization is highly valued. In this paper, we propose a model based on the analysis of the similarity between the new item and the object that the users have selected to solve cold-start problem in network-based recommendation. In order to improve the accuracy of the model, we take the degree of the items that have been collected by the user into consideration. The experiments with MovieLens data set indicate substantial improvements of this model in overcoming the cold-start problem in network-based recommendation.
KeywordsRecommender systems Network-based filtering Similarity Item degree Cold-start
This work has been supported by the National Natural Science Foundation of China under Grant 61172072, 61271308, the Fundamental Research Funds for the Central Universities under Grant 2013JBM006, the Beijing Natural Science Foundation under Grant 4112045, the Research Fund for the Doctoral Program of Higher Education of China under Grant W11C100030, the Beijing Science and Technology Program under Grant Z121100000312024. The authors are also grateful for the comments and suggestions of the reviewers.
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