Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Novelty and redundancy detection in adaptive filtering
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
User Modeling for Adaptive News Access
User Modeling and User-Adapted Interaction
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
IEEE Transactions on Knowledge and Data Engineering
An industrial-strength content-based music recommendation system
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
The Long Tail: Why the Future of Business Is Selling Less of More
The Long Tail: Why the Future of Business Is Selling Less of More
Improving Recommendation Novelty Based on Topic Taxonomy
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Context aware recommendations for user-generated content on a social network site
Proceedings of the seventh european conference on European interactive television conference
On the limitations of browsing top-N recommender systems
Proceedings of the third ACM conference on Recommender systems
From hits to niches?: or how popular artists can bias music recommendation and discovery
Proceedings of the 2nd KDD Workshop on Large-Scale Recommender Systems and the Netflix Prize Competition
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
The Journal of Machine Learning Research
Time-evolution of IPTV recommender systems
Proceedings of the 8th international interactive conference on Interactive TV&Video
Serendipitous recommendations via innovators
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Multi-dimensional search result diversification
Proceedings of the fourth ACM international conference on Web search and data mining
A probabilistic approach to semantic collaborative filtering using world knowledge
Journal of Information Science
dbrec: music recommendations using DBpedia
ISWC'10 Proceedings of the 9th international semantic web conference on The semantic web - Volume Part II
Taking advice from intelligent systems: the double-edged sword of explanations
Proceedings of the 16th international conference on Intelligent user interfaces
Design and evaluation of a command recommendation system for software applications
ACM Transactions on Computer-Human Interaction (TOCHI)
Looking for "good" recommendations: a comparative evaluation of recommender systems
INTERACT'11 Proceedings of the 13th IFIP TC 13 international conference on Human-computer interaction - Volume Part III
Rank and relevance in novelty and diversity metrics for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Workshop on novelty and diversity in recommender systems - DiveRS 2011
Proceedings of the fifth ACM conference on Recommender systems
Auralist: introducing serendipity into music recommendation
Proceedings of the fifth ACM international conference on Web search and data mining
Mining the real-time web: A novel approach to product recommendation
Knowledge-Based Systems
Towards a storytelling approach for novel artist recommendations
AMR'10 Proceedings of the 8th international conference on Adaptive Multimedia Retrieval: context, exploration, and fusion
Determining user expertise for improving recommendation performance
Proceedings of the 6th International Conference on Ubiquitous Information Management and Communication
ACM Transactions on Interactive Intelligent Systems (TiiS)
Explicit relevance models in intent-oriented information retrieval diversification
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Pareto-efficient hybridization for multi-objective recommender systems
Proceedings of the sixth ACM conference on Recommender systems
Workshop on recommendation utility evaluation: beyond RMSE -- RUE 2012
Proceedings of the sixth ACM conference on Recommender systems
A comparative study of heterogeneous item recommendations in social systems
Information Sciences: an International Journal
Information Processing and Management: an International Journal
Building user profiles to improve user experience in recommender systems
Proceedings of the sixth ACM international conference on Web search and data mining
Serendipitous Personalized Ranking for Top-N Recommendation
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 01
Exploiting the diversity of user preferences for recommendation
Proceedings of the 10th Conference on Open Research Areas in Information Retrieval
Topic diversity in tag recommendation
Proceedings of the 7th ACM conference on Recommender systems
A probability-based unified framework for semantic search and recommendation
Journal of Information Science
| Hi-index | 0.00 |
This paper presents two methods, named Item- and User-centric, to evaluate the quality of novel recommendations. The former method focuses on analyzing the item-based recommendation network. The aim is to detect whether the network topology has any pathology that hinders novel recommendations. The latter, user-centric evaluation, aims at measuring users' perceived quality of novel, previously unknown, recommendations. The results of the experiments, done in the music recommendation context, show that last.fm social recommender, based on collaborative filtering, is prone to popularity bias. This has direct consequences on the topology of the item-based recommendation network. Pure audio content-based methods (CB) are not affected by popularity. However, a user-centric experiment done with 288 subjects shows that even though a social-based approach recommends less novel items than our CB, users' perceived quality is better than those recommended by a pure CB method.