A collaborative filtering framework based on fuzzy association rules and multiple-level similarity

Authors:
Cane Wing-ki Leung;Stephen Chi-fai Chan;Fu-lai Chung
Affiliations:
Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, P. R. China;Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, P. R. China;Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Hong Kong SAR, P. R. China
Venue:
Knowledge and Information Systems
Year:
2006

Citing 0
Cited 22

Applying Cross-Level Association Rule Mining to Cold-Start Recommendations

WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
A novel knowledge discovering model for mining fuzzy multi-level sequential patterns in sequence databases

Data & Knowledge Engineering
An empirical study of a cross-level association rule mining approach to cold-start recommendations

Knowledge-Based Systems
A novel particle swarm optimization for multiple campaigns assignment problem

CSTST '08 Proceedings of the 5th international conference on Soft computing as transdisciplinary science and technology
Modified algorithms for synthesizing high-frequency rules from different data sources

Knowledge and Information Systems
Receiver-side semantic reasoning for digital TV personalization in the absence of return channels

Multimedia Tools and Applications
A hybrid recommendation technique based on product category attributes

Expert Systems with Applications: An International Journal
The new HYREC: a new hybrid product recommender system

ASC '07 Proceedings of The Eleventh IASTED International Conference on Artificial Intelligence and Soft Computing
Towards a graph-based user profile modeling for a session-based personalized search

Knowledge and Information Systems
Protecting buying agents in e-marketplaces by direct experience trust modelling

Knowledge and Information Systems
MiSPOT: dynamic product placement for digital TV through MPEG-4 processing and semantic reasoning

Knowledge and Information Systems
A survey of collaborative filtering techniques

Advances in Artificial Intelligence
Integrating rating-based collaborative filtering with customer lifetime value: New product recommendation technique

Intelligent Data Analysis
Exploring synergies between digital tv recommender systems and electronic health records

Proceedings of the 8th international interactive conference on Interactive TV&Video
Mining fuzzy association rules from uncertain data

Knowledge and Information Systems
Automatic provision of personalized e-commerce services in Digital TV scenarios with impermanent connectivity

Expert Systems with Applications: An International Journal
Probabilistic user modeling in the presence of drifting concepts

PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
A GA-weighted ANFIS model based on multiple stock market volatility causality for TAIEX forecasting

Applied Soft Computing
Attribute-based collaborative filtering using genetic algorithm and weighted C-means algorithm

International Journal of Business Information Systems
Recommendation algorithm of the app store by using semantic relations between apps

The Journal of Supercomputing
A hybrid recommendation approach based on attributes of products using genetic algorithm and naive Bayes classifier

International Journal of Business Information Systems
An effective recommendation based on user behaviour: a hybrid of sequential pattern of user and attributes of product

International Journal of Business Information Systems

Quantified Score

Hi-index	0.00

Visualization

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.