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Research on the Cold Start Recommender System of New Users Based on Seed-set Rating in the Collaborative Filtering System
Received date: 2012-11-20
Revised date: 2013-02-19
Online published: 2013-03-05
This paper believes that building a seed set to guide users’ evaluation is one of the solutions to resolve the cold start problem of new users. An integrated approach based on multiple-attribute comprehensive assessment is brought up by bringing the relevance into the construction strategy of seed set. User environment of the recommender system is simulated through the experiment designed according to public data sets, in order to compare the accuracy and success rate of forecast. The result shows that the recommender effectiveness is improved by considering the relevance of seeds, if a small amount of seeds sets which are more suitable to the real recommendation system are adopted.
Jing Minchang , Zhang Qin , Tang Diguan . Research on the Cold Start Recommender System of New Users Based on Seed-set Rating in the Collaborative Filtering System[J]. Library and Information Service, 2013 , 57(05) : 124 -128 . DOI: 10.7536/j.issn.0252-3116.2013.05.022
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