GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Journal of the American Society for Information Science
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Probabilistic Memory-Based Collaborative Filtering
IEEE Transactions on Knowledge and Data Engineering
ACM Transactions on Information Systems (TOIS)
Trust-aware recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
A Resource Recommendation Method Based on User Taste Diffusion Model in Folksonomies
International Journal of Knowledge and Systems Science
Hi-index | 0.00 |
While Spread Activation has shown its effectiveness in solving the problem of cold start and sparsity in collaborative recommendation, it will suffer a decay of performance (over activation) as the dataset grows denser. In this paper, we first introduce the concepts of Rating Similarity Matrix (RSM) and Rating Similarity Aggregation (RSA), based on which we then extend the existing spreading activation scheme to deal with both the binary (transaction) and the numeric ratings. After that, an iterative algorithm is proposed to learn RSM parameters from the observed ratings, which makes it automatically adaptive to the user similarity shown through their ratings on different items. Thus the similarity calculations tend to be more reasonable and effective. Finally, we test our method on the EachMovie dataset, the most typical benchmark for collaborative recommendation and show that our method succeeds in relieving the effect of over activation and outperforms the existing algorithms on both the sparse and dense dataset.