%0 Journal Article %1 keyhere %A Leung, Cane %A Chan, Stephen %A lai Chung, Fu %D 2006 %J Knowledge and Information Systems %K collaborative filtering fuzzy imported %N 3 %P 357--381 %T A collaborative filtering framework based on fuzzy association rules and multiple-level similarity %U http://dx.doi.org/10.1007/s10115-006-0002-1 %V 10 %X Abstract  The rapid development of Internet technologies in recent decades has imposed a heavy information burden on users. This has led to the popularity of recommender systems, which provide advice to users about items they may like to examine. Collaborative Filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. This paper introduces a CF framework based on Fuzzy Association Rules and Multiple-level Similarity (FARAMS). FARAMS extended existing techniques by using fuzzy association rule mining, and takes advantage of product similarities in taxonomies to address data sparseness and nontransitive associations. Experimental results show that FARAMS improves prediction quality, as compared to similar approaches. ER -

@article{keyhere, abstract = {Abstract  The rapid development of Internet technologies in recent decades has imposed a heavy information burden on users. This has led to the popularity of recommender systems, which provide advice to users about items they may like to examine. Collaborative Filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. This paper introduces a CF framework based on Fuzzy Association Rules and Multiple-level Similarity (FARAMS). FARAMS extended existing techniques by using fuzzy association rule mining, and takes advantage of product similarities in taxonomies to address data sparseness and nontransitive associations. Experimental results show that FARAMS improves prediction quality, as compared to similar approaches. ER -}, added-at = {2007-10-19T20:29:21.000+0200}, author = {Leung, Cane and Chan, Stephen and lai Chung, Fu}, biburl = {https://www.bibsonomy.org/bibtex/2c9559e38ce4cb1a2317ac78a4cf2362e/wnpxrz}, description = {SpringerLink - Journal Article}, interhash = {16cd5f827a830f16088c07a9e763ba94}, intrahash = {c9559e38ce4cb1a2317ac78a4cf2362e}, journal = {Knowledge and Information Systems}, keywords = {collaborative filtering fuzzy imported}, month = {#oct#}, number = 3, pages = {357--381}, timestamp = {2007-10-19T20:29:21.000+0200}, title = {A collaborative filtering framework based on fuzzy association rules and multiple-level similarity}, url = {http://dx.doi.org/10.1007/s10115-006-0002-1}, volume = 10, year = 2006 }