An algorithmic framework for performing collaborative filtering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
A probabilistic model of information retrieval: development and comparative experiments
Information Processing and Management: an International Journal
Modern Information Retrieval
Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
IEEE Transactions on Knowledge and Data Engineering
Web Page Recommender System based on Folksonomy Mining for ITNG '06 Submissions
ITNG '06 Proceedings of the Third International Conference on Information Technology: New Generations
Google news personalization: scalable online collaborative filtering
Proceedings of the 16th international conference on World Wide Web
A time-based approach to effective recommender systems using implicit feedback
Expert Systems with Applications: An International Journal
Exploring folksonomy for personalized search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic relevance ranking for collaborative filtering
Information Retrieval
Social ranking: uncovering relevant content using tag-based recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative Filtering for Implicit Feedback Datasets
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Tagommenders: connecting users to items through tags
Proceedings of the 18th international conference on World wide web
On social networks and collaborative recommendation
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Workshop on social recommender systems
Proceedings of the 15th international conference on Intelligent user interfaces
Use of social network information to enhance collaborative filtering performance
Expert Systems with Applications: An International Journal
Generalized distances between rankings
Proceedings of the 19th international conference on World wide web
Web search personalization via social bookmarking and tagging
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Recommender System for Online Social Network
Recommender System for Online Social Network
Content-based recommendation in social tagging systems
Proceedings of the fourth ACM conference on Recommender systems
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Learning multiple models for exploiting predictive heterogeneity in recommender systems
Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems
Rank and relevance in novelty and diversity metrics for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
A personalized recommendation system on scholarly publications
Proceedings of the 20th ACM international conference on Information and knowledge management
A comparative study of heterogeneous item recommendations in social systems
Information Sciences: an International Journal
An empirical comparison of social, collaborative filtering, and hybrid recommenders
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
A group recommender for movies based on content similarity and popularity
Information Processing and Management: an International Journal
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We present a preliminarily study on the influence of different sources of information in Web 2.0 systems on recommendation. Aiming to identify which are the sources of information (ratings, tags, social contacts, etc.) most valuable for recommendation, we evaluate a number of content-based, collaborative filtering and social recommenders on a heterogeneous dataset obtained from Last.fm. Moreover, aiming to investigate whether and how fusion of such information sources can benefit individual recommendation approaches, we propose various metrics to measure coverage, overlap, diversity and novelty between different sets of recommendations. The obtained results show that, in Last.fm, social tagging and explicit social networking information provide effective and heterogeneous item recommendations. Moreover, they give first insights on the feasibility of exploiting the above non performance recommendation characteristics by hybrid approaches.