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A study of heterogeneity in recommendations for a social music service
TLDR
The obtained results show that, in Last.fm, social tagging and explicit social networking information provide effective and heterogeneous item recommendations, and give first insights on the feasibility of exploiting the above non performance recommendation characteristics by hybrid approaches. Expand
An empirical comparison of social, collaborative filtering, and hybrid recommenders
TLDR
A coverage metric is proposed that uncovers and compensates for the incompleteness of performance evaluations based only on precision and is used together with precision metrics in an empirical comparison of several social, collaborative filtering, and hybrid recommenders. Expand
Precision-oriented evaluation of recommender systems: an algorithmic comparison
TLDR
In three experiments with three state-of-the-art recommenders, four of the evaluation methodologies are consistent with each other and differ from error metrics, in terms of the comparative recommenders' performance measurements. Expand
Content-based recommendation in social tagging systems
TLDR
Various content-based recommendation models that make use of user and item profiles defined in terms of weighted lists of social tags, adaptations of the Vector Space and Okapi BM25 information retrieval models are presented. Expand
Comparative recommender system evaluation: benchmarking recommendation frameworks
TLDR
This work compares common recommendation algorithms as implemented in three popular recommendation frameworks and shows the necessity of clear guidelines when reporting evaluation of recommender systems to ensure reproducibility and comparison of results. Expand
Simple time-biased KNN-based recommendations
TLDR
Results show that the usage of information near to the recommendation date alone can help improving recommendation results, with the additional benefit of reducing the information overload of the recommender engine. Expand
A multilayer ontology-based hybrid recommendation model
TLDR
A novel hybrid recommendation model is proposed in which user preferences and item features are described in terms of semantic concepts defined in domain ontologies and the resulting clusters are used to find similarities among individuals at multiple semantic layers. Expand
Ontology-Based Personalised and Context-Aware Recommendations of News Items
TLDR
A model that personalizes the order in which news articles are shown to the user according to his long-term interest profile is evaluated, and other model that reorders the news items lists taking into account the current semantic context of interest of the user is investigated. Expand
Relating Personality Types with User Preferences in Multiple Entertainment Domains
TLDR
This preliminary study analyzes Facebook user profiles composed of both personality scores from the Five Factor model and explicit interests about 16 genres in multiple entertainment domains to extract personality-based user stereotypes and association rules for some of the considered domain genres. Expand
A comparative study of heterogeneous item recommendations in social systems
TLDR
Empiric observations showing that exploiting tagging information by content-based recommenders provides high coverage and novelty, and combining social networking and collaborative filtering information by hybrid recommenders results in high accuracy and diversity are reported. Expand
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