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An empirical comparison of social, collaborative filtering, and hybrid recommenders
- Alejandro Bellogín, Iván Cantador, Fernando Díez, P. Castells, Enrique Chavarriaga
- Computer ScienceTIST
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.
A study of heterogeneity in recommendations for a social music service
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.
A comparative study of heterogeneous item recommendations in social systems
Precision-oriented evaluation of recommender systems: an algorithmic comparison
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.
Statistical biases in Information Retrieval metrics for recommender systems
- Alejandro Bellogín, P. Castells, Iván Cantador
- Computer ScienceInformation Retrieval Journal
- 27 July 2017
This paper lays out an experimental configuration framework upon which to identify and analyse specific statistical biases arising in the adaptation of Information Retrieval metrics to recommendation tasks, namely sparsity and popularity biases.
Content-based recommendation in social tagging systems
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.
Ontology-Based Personalised and Context-Aware Recommendations of News Items
- Iván Cantador, Alejandro Bellogín, P. Castells
- Computer ScienceIEEE/WIC/ACM International Conference on Web…
- 9 December 2008
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.
Relating Personality Types with User Preferences in Multiple Entertainment Domains
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.
Comparative recommender system evaluation: benchmarking recommendation frameworks
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.
A multilayer ontology-based hybrid recommendation model
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.