Application of Item Response Theory to Collaborative Filtering

Authors:
Biyun Hu;Yiming Zhou;Jun Wang;Lin Li;Lei Shen
Affiliations:
School of Computer Science & Engineering, Beihang University, Beijing, China 100191;School of Computer Science & Engineering, Beihang University, Beijing, China 100191;School of Computer Science & Engineering, Beihang University, Beijing, China 100191;School of Computer Science & Engineering, Beihang University, Beijing, China 100191;School of Computer Science & Engineering, Beihang University, Beijing, China 100191
Venue:
ISNN '09 Proceedings of the 6th International Symposium on Neural Networks on Advances in Neural Networks
Year:
2009

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Abstract

Although many approaches to collaborative filtering have been proposed, few have considered the data quality of the recommender systems. Measurement is imprecise and the rating data given by users is true preference distorted. This paper describes how item response theory, specifically the rating scale model, may be applied to correct the ratings. The theoretically true preferences were then used to substitute for the actual ratings to produce recommendation. This approach was applied to the Jester dataset and traditional k-Nearest Neighbors (k-NN) collaborative filtering algorithm. Experiments demonstrated that rating scale model can enhance the recommendation quality of k-NN algorithm. Analysis also showed that our approach can predict true preferences which k-NN cannot do. The results have important implications for improving the recommendation quality of other collaborative filtering algorithms by finding out the true user preference first.