》 资源 》论文》Improving the Performance of Recommender Systems by Alleviating the Data Sparsity and Cold Start Problems
Improving the Performance of Recommender Systems by Alleviating the Data Sparsity and Cold Start Problems
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Recommender systems, providing users with personalized recommendations from a plethora of choices, have been an important component for e-commerce applications to cope with the information overload problem. Collaborative fifiltering (CF) is a widely used technique to generate recommendations. The basic principle is that recommendations can be made according to the ratings of like-minded users. However, CF inherently suffers from two severe issues, which are the problems targeted in this research
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