Stereotypes in information filtering systems
Information Processing and Management: an International Journal
Predicting the performance of linearly combined IR systems
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Machine Learning
Combining Multiple Evidence from Different Relevant Feedback Networks
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Proceedings of the 2003 ACM symposium on Applied computing
Data Mining
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Despite the fact that stereotyping has been used many times in recommender systems, little is known about why stereotyping is successful for some users but unsuccessful for others. To begin to address this issue, we conducted experiments in which stereotype-based user models were automatically constructed and the performance of overall user models and individual stereotypes observed. We have shown how concepts from data fusion, a previously unconnected field, can be applied to illustrate why the performance of stereotyping varies between users. Our study illustrates clearly that the interactions between stereotypes, in terms of their ratings of items, is a major factor in overall user model performance and that poor performance on the part of an individual stereotype need not directly cause poor overall user model performance.