Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Agent-based interaction analysis of consumer behavior
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Is seeing believing?: how recommender system interfaces affect users' opinions
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Multi-Agent Simulation of Virtual Consumer Populations in a Competitive Market
SCAI '01 Proceedings of the Seventh Scandinavian Conference on Artificial Intelligence
IEEE Transactions on Knowledge and Data Engineering
A Context-Aware Movie Preference Model Using a Bayesian Network for Recommendation and Promotion
UM '07 Proceedings of the 11th international conference on User Modeling
The value of personalised recommender systems to e-business: a case study
Proceedings of the 2008 ACM conference on Recommender systems
Wasp: A Multi-agent System for Multiple Recommendations Problem
NWESP '08 Proceedings of the 2008 4th International Conference on Next Generation Web Services Practices
Producing timely recommendations from social networks through targeted search
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Scalable Collaborative Filtering Approaches for Large Recommender Systems
The Journal of Machine Learning Research
Improving search in social networks by agent based mining
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Information Sciences: an International Journal
AIS-ADM'07 Proceedings of the 2nd international conference on Autonomous intelligent systems: agents and data mining
Finding useful items and links in social and agent networks
ADMI'10 Proceedings of the 6th international conference on Agents and data mining interaction
| Hi-index | 0.00 |
Recommender systems are increasingly used for personalised navigation through large amounts of information, especially in the e-commerce domain for product purchase advice. Whilst much research effort is spent on developing recommenders further, there is little to no effort spent on analysing the impact of them --- neither on the supply (company) nor demand (consumer) side. In this article, we investigate the diversity impact of a movie recommender. We define diversity for different parts of the domain and measure it in different ways. The novelty of our work is the usage of real rating data (from Netflix) and a recommender system for investigating the (hypothetical) effects of various configurations of the system and users' choice models. We consider a number of different scenarios (which differ in agents' choice models), run extensive simulations, analyse various measurements regarding experimental validation and diversity, and report on selected findings. The scenarios are an essential part of our work, since these can be influenced by the owner of the recommender once deployed. This article contains an overview of related work on data-mining, multi-agent systems, movie recommendation and measurement of diversity; we explain different agents' choice models (which are used to simulate how users react to recommenders); and we report on selected findings from an extensive series of simulation experiments that we ran with real usage data (from Netflix).