Name
Title | ||
|---|---|---|
Prediction of User Preference in Recommendation System Using Associative User Clustering and Bayesian Estimated Value |
Abstract | ||
|---|---|---|
The user predicting preference method using a collaborative filtering (CF) does not only reflect any contents about items but also solve the sparsity and first-rater problem. In this paper, we suggest the method of prediction by using associative user clustering and Bayesian estimated value to complement the problems of the current collaborative filtering system. The Representative Attribute-Neighborhood is for an active user to select the nearest neighbors who have similar preference through extracting the representative attributes that most affects the preference. Associative user behavior pattern 3_UB(associative users are composed of 3-users) is clustered according to the genre through Association Rule Hypergraph Partitioning Algorithm, and new users are classified into one of these genres by Naive Bayes classifier. Besides, to get the similarity between users belonged to the classified genre and new users, this paper allows the different estimated values to items which users evaluated through Naive Bayes learning. We evaluate our method on a large CF database of user rating and it significantly outperforms the previous proposed method. |
Year | DOI | Venue |
|---|---|---|
2002 | 10.1007/3-540-36187-1_25 | Australian Joint Conference on Artificial Intelligence |
Keywords | Field | DocType |
preference method,similar preference,new user,associative user behavior pattern,user preference,associative user clustering,estimated value,user rating,active user,naive bayes classifier,associative user,recommendation system,previous proposed method,naive bayes,nearest neighbor,collaborative filtering,recommender system,association rule | Data mining,Associative property,Computer science,Hypergraph,Artificial intelligence,Cluster analysis,Recommender system,Collaborative filtering,Naive Bayes classifier,Pattern recognition,Association rule learning,Machine learning,Bayesian probability | Conference |
Volume | ISSN | ISBN |
2557 | 0302-9743 | 3-540-00197-2 |
Citations | PageRank | References |
7 | 0.63 | 11 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kyung-Yong Jung | 1 | 63 | 7.86 |
| Jung-Hyun Lee | 2 | 87 | 9.77 |