GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
Multivariate data analysis (4th ed.): with readings
Multivariate data analysis (4th ed.): with readings
Recommending and evaluating choices in a virtual community of use
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Self-organizing maps
Recommender systems in e-commerce
Proceedings of the 1st ACM conference on Electronic commerce
Specifying preferences based on user history
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Predictive Statistical Models for User Modeling
User Modeling and User-Adapted Interaction
TV Scout: Lowering the Entry Barrier to Personalized TV Program Recommendation
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
A Hybrid Recommender System Combining Collaborative Filtering with Neural Network
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
A Case-Based Recommender System Using Implicit Rating Techniques
AH '02 Proceedings of the Second International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems
WhatNext: A Prediction System for Web Requests using N-gram Sequence Models
WISE '00 Proceedings of the First International Conference on Web Information Systems Engineering (WISE'00)-Volume 1 - Volume 1
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Interfaces for eliciting new user preferences in recommender systems
UM'03 Proceedings of the 9th international conference on User modeling
A pseudo-supervised approach to improve a recommender based on collaborative filtering
UM'03 Proceedings of the 9th international conference on User modeling
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Expert Systems with Applications: An International Journal
Web mining based on Growing Hierarchical Self-Organizing Maps: Analysis of a real citizen web portal
Expert Systems with Applications: An International Journal
Data & Knowledge Engineering
Expert Systems with Applications: An International Journal
Research of fast SOM clustering for text information
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
This paper presents a methodology to estimate the future success of a collaborative recommender in a citizen web portal. This methodology consists of four stages, three of them are developed in this study. First of all, a user model, which takes into account some usual characteristics of web data, is developed to produce artificial data sets. These data sets are used to carry out a clustering algorithm comparison in the second stage of our approach. This comparison provides information about the suitability of each algorithm in different scenarios. The benchmarked clustering algorithms are the ones that are most commonly used in the literature: c-Means, Fuzzy c-Means, a set of hierarchical algorithms, Gaussian mixtures trained by the expectation-maximization algorithm, and Kohonen's self-organizing maps (SOM). The most accurate clustering is yielded by SOM. Afterwards, we turn to real data. The users of a citizen web portal (Infoville XXI, http://www.infoville.es) are clustered. The clustering achieved enables us to study the future success of a collaborative recommender by means of a prediction strategy. New users are recommended according to the cluster in which they have been classified. The suitability of the recommendation is evaluated by checking whether or not the recommended objects correspond to those actually selected by the user. The results show the relevance of the information provided by clustering algorithms in this web portal, and therefore, the relevance of developing a collaborative recommender for this web site.