Qualitative analysis of user-based and item-based prediction algorithms for recommendation agents

Authors:
Manos Papagelis;Dimitris Plexousakis
Affiliations:
Institute of Computer Science, Foundation for Research and Technology-Hellas, P.O. Box 1385, GR-71110 Heraklion, Greece and Department of Computer Science, University of Crete, P.O. Box 2208, GR-7 ...;Institute of Computer Science, Foundation for Research and Technology-Hellas, P.O. Box 1385, GR-71110 Heraklion, Greece and Department of Computer Science, University of Crete, P.O. Box 2208, GR-7 ...
Venue:
Engineering Applications of Artificial Intelligence
Year:
2005

Citing 11
Cited 15

User models: theory, method, and practice

International Journal of Man-Machine Studies
Using filtering agents to improve prediction quality in the GroupLens research collaborative filtering system

CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
An algorithmic framework for performing collaborative filtering

Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Explaining collaborative filtering recommendations

CSCW '00 Proceedings of the 2000 ACM conference on Computer supported cooperative work
Methods and metrics for cold-start recommendations

SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Content-boosted collaborative filtering for improved recommendations

Eighteenth national conference on Artificial intelligence
Collaborative filtering via gaussian probabilistic latent semantic analysis

Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering

ACM Transactions on Information Systems (TOIS)
On the value of private information

TARK '01 Proceedings of the 8th conference on Theoretical aspects of rationality and knowledge
REFEREE: an open framework for practical testing of recommender systems using ResearchIndex

VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Empirical analysis of predictive algorithms for collaborative filtering

UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence

A personalized recommendation system combining case-based reasoning and user-based collaborative filtering

CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Item-based collaborative filtering recommendation using self-organizing map

CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Human-robot interfaces for social interaction

International Journal of Robotics and Automation
A collaborative recommender system based on asymmetric user similarity

IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
An improvement for semantics-based recommender systems grounded on attaching temporal information to ontologies and user profiles

Engineering Applications of Artificial Intelligence
SINVLIO: Using semantics and fuzzy logic to provide individual investment portfolio recommendations

Knowledge-Based Systems
Alleviating the sparsity problem of collaborative filtering using trust inferences

iTrust'05 Proceedings of the Third international conference on Trust Management
Improve top-k recommendation by extending review analysis

APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Student progress assessment with the help of an intelligent pupil analysis system

Engineering Applications of Artificial Intelligence
Rough Set Theory Based User Aware TV Program and Settings Recommender

International Journal of Advanced Pervasive and Ubiquitous Computing
Recommendations in a heterogeneous service environment

Multimedia Tools and Applications
Semi-Supervised Policy Recommendation for Online Social Networks

ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Learning User Preference Patterns for Top-N Recommendations

WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Recommender System to Analyze Student's Academic Performance

Expert Systems with Applications: An International Journal
Design of Context Aware Recommendation Engine for Cell Phone using Bayesian Network, Fuzzy Logic, and Rough Set Theory

International Journal of Advanced Pervasive and Ubiquitous Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Recommendation agents employ prediction algorithms to provide users with items that match their interests. In this paper, several prediction algorithms are described and evaluated, some of which are novel in that they combine user-based and item-based similarity measures derived from either explicit or implicit ratings. Both statistical and decision-support accuracy metrics of the algorithms are compared against different levels of data sparsity and different operational thresholds. The first metric evaluates the accuracy in terms of average absolute deviation, while the second evaluates how effectively predictions help users to select high-quality items. The experimental results indicate better performance of item-based predictions derived from explicit ratings in relation to both metrics. Category-boosted predictions lead to slightly better predictions when combined with explicit ratings, while implicit ratings, in the context that have been defined in this paper, perform much worse than explicit ratings.