Learning automata with changing number of actions
IEEE Transactions on Systems, Man and Cybernetics
Learning automata: an introduction
Learning automata: an introduction
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Information filtering based on user behavior analysis and best match text retrieval
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Collecting user access patterns for building user profiles and collaborative filtering
IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Authoritative sources in a hyperlinked environment
Journal of the ACM (JACM)
Mining navigation history for recommendation
Proceedings of the 5th international conference on Intelligent user interfaces
Personalization on the Net using Web mining: introduction
Communications of the ACM
Automatic personalization based on Web usage mining
Communications of the ACM
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Effective personalization based on association rule discovery from web usage data
Proceedings of the 3rd international workshop on Web information and data management
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Discovery and Evaluation of Aggregate Usage Profiles for Web Personalization
Data Mining and Knowledge Discovery
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Knowledge discovery from users Web-page navigation
RIDE '97 Proceedings of the 7th International Workshop on Research Issues in Data Engineering (RIDE '97) High Performance Database Management for Large-Scale Applications
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Mining Association Rules with Weighted Items
IDEAS '98 Proceedings of the 1998 International Symposium on Database Engineering & Applications
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Evaluation of web usage mining approaches for user's next request prediction
WIDM '03 Proceedings of the 5th ACM international workshop on Web information and data management
SEWeP: using site semantics and a taxonomy to enhance the Web personalization process
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Weighted Association Rule Mining using weighted support and significance framework
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Collaborative recommendation: A robustness analysis
ACM Transactions on Internet Technology (TOIT)
Taxonomy-driven computation of product recommendations
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Web personalization integrating content semantics and navigational patterns
Proceedings of the 6th annual ACM international workshop on Web information and data management
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
Incorporating concept hierarchies into usage mining based recommendations
WebKDD'06 Proceedings of the 8th Knowledge discovery on the web international conference on Advances in web mining and web usage analysis
Intelligent techniques for web personalization
ITWP'03 Proceedings of the 2003 international conference on Intelligent Techniques for Web Personalization
Incorporating pageview weight into an association-rule-based web recommendation system
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Notes toward a politics of personalization
Proceedings of the 2011 iConference
Recommender system based on click stream data using association rule mining
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Sleep-based topology control in the Ad Hoc networks by using fitness aware learning automata
Computers & Mathematics with Applications
A hybrid recommendation approach for a tourism system
Expert Systems with Applications: An International Journal
A fuzzy co-clustering approach for hybrid recommender systems
International Journal of Hybrid Intelligent Systems
Hi-index | 12.06 |
Different efforts have been done to address the problem of information overload on the Internet. Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests by extracting knowledge from the previous users' interactions. In this paper, we propose three algorithms to solve the web page recommendation problem. In our first algorithm, we use distributed learning automata to learn the behavior of previous users' and recommend pages to the current user based on learned patterns. By introducing a novel weighted association rule mining algorithm, we present our second algorithm for recommendation purpose. Also, a novel method is proposed to pure the current session window. One of the challenging problems in recommendation systems is dealing with unvisited or newly added pages. By considering this problem and improving the efficiency of first two algorithms we present a hybrid algorithm based on distributed learning automata and proposed weighted association rule mining algorithm. In the hybrid algorithm we employ the HITS algorithm to extend the recommendation set. Our experiments on real data set show that the hybrid algorithm performs better than the other algorithms we compared to and, at the same time, it is less complex than other proposed algorithms with respect to memory usage and computational cost too.