Paolo Tomeo

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Papers17 papers

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Addressing the user cold start with cross-domain collaborative filtering: exploiting item metadata in matrix factorization

Ignacio Fernández-Tobías,Iván Cantador,Paolo Tomeo, Vito Walter Anelli,Tommaso Di Noia

User Modeling and User-Adapted Interaction, pp.1-44, (2019)

Cited by1EIBibtex

Neighbor Selection for Cold Users in Collaborative Filtering with Positive-Only Feedback.

Alejandro Bellogín,Ignacio Fernández-Tobías,Iván Cantador,Paolo Tomeo

CAEPIA, pp.3-12, (2018)

EIBibtex

Schema-summarization in linked-data-based feature selection for recommender systems.

Azzurra Ragone,Paolo Tomeo, Corrado Magarelli,Tommaso Di Noia,Matteo Palmonari,Andrea Maurino,Eugenio Di Sciascio

SAC, pp.330-335, (2017)

Cited by9EIBibtex

Addressing the Cold Start with Positive-Only Feedback Through Semantic-Based Recommendations.

Paolo Tomeo,Ignacio Fernández-Tobías,Iván Cantador,Tommaso Di Noia

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, no. Supplement-2 (2017): 57-78

Cited by4EIBibtex

On the Role of Time and Sessions in Diversifying Recommendation Results.

Vito Walter Anelli, Vito Bellini,Tommaso Di Noia, Wanda La Bruna,Paolo Tomeo,Eugenio Di Sciascio

IIR, pp.92-96, (2017)

EIBibtex

Schema-aware Feature Selection in Linked Data-based Recommender Systems.

Corrado Magarelli,Azzurra Ragone,Paolo Tomeo,Tommaso Di Noia,Matteo Palmonari,Andrea Maurino,Eugenio Di Sciascio

IIR, pp.67-71, (2017)

EIBibtex

An Analysis on Time- and Session-aware Diversification in Recommender Systems.

Vito Walter Anelli, Vito Bellini,Tommaso Di Noia, Wanda La Bruna,Paolo Tomeo,Eugenio Di Sciascio

UMAP, pp.270-274, (2017)

EIBibtex

Adaptive multi-attribute diversity for recommender systems.

Tommaso Di Noia,Jessica Rosati,Paolo Tomeo,Eugenio Di Sciascio

Inf. Sci., (2017): 234-253

Cited by21EIBibtex

Exploiting Linked Open Data in Cold-start Recommendations with Positive-only Feedback.

Paolo Tomeo,Ignacio Fernández-Tobías,Tommaso Di Noia,Iván Cantador

CERI, (2016)

Cited by3EIBibtex

Rating Prediction with Contextual Conditional Preferences.

Aleksandra Karpus,Tommaso Di Noia,Paolo Tomeo,Krzysztof Goczyla

KDIR, pp.419-424, (2016)

Cited by1EIBibtex

Accuracy and Diversity in Cross-domain Recommendations for Cold-start Users with Positive-only Feedback.

Ignacio Fernández-Tobías,Paolo Tomeo,Iván Cantador,Tommaso Di Noia,Eugenio Di Sciascio

RecSys, pp.119-122, (2016)

Cited by13EIBibtex

SPrank: Semantic Path-Based Ranking for Top-N Recommendations Using Linked Open Data.

Tommaso Di Noia,Vito Claudio Ostuni,Paolo Tomeo,Eugenio Di Sciascio

ACM TIST, no. 1 (2016)

Cited by59EIBibtex

Tommaso Di Noia,Vito Claudio Ostuni,Jessica Rosati,Paolo Tomeo,Eugenio Di Sciascio,Roberto Mirizzi,Claudio Bartolini

Expert Systems with Applications, (2016)

Cited by9EIBibtex

Tommaso Di Noia,Vito Claudio Ostuni,Jessica Rosati,Paolo Tomeo,Eugenio Di Sciascio,Roberto Mirizzi,Claudio Bartolini

Expert Systems with Applications: An International Journal, no. C (2016): 354-366

Cited by3EIBibtex

Content-Based Recommendations via DBpedia and Freebase: A Case Study in the Music Domain.

Phuong T. Nguyen,Paolo Tomeo,Tommaso Di Noia,Eugenio Di Sciascio

International Semantic Web Conference, pp.605-621, (2015)

Cited by21EIBibtex

Adaptive Diversity in Recommender Systems.

Tommaso Di Noia,Vito Claudio Ostuni,Jessica Rosati,Paolo Tomeo,Eugenio Di Sciascio

IIR, (2015)

Cited by1EIBibtex

An analysis of users' propensity toward diversity in recommendations

Tommaso Di Noia,Vito Claudio Ostuni,Jessica Rosati,Paolo Tomeo,Eugenio Di Sciascio

RecSys, pp.285-288, (2014)

Cited by38EIBibtex

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