Cold start (Italian Wikipedia)

Analysis of information sources in references of the Wikipedia article "Cold start" in Italian language version.

refsWebsite

Global rank Italian rank

22doi.org

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21oadoi.org

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3springer.com

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2psu.edu

207^th place

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1researchgate.net

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1web.archive.org

1^st place

doi.org

dx.doi.org

Jesús Bobadilla, Fernando Ortega, Antonio Hernando e Jesús Bernal, A collaborative filtering approach to mitigate the new user cold start problem, in Knowledge-Based Systems, vol. 26, febbraio 2012, pp. 225-238, DOI:10.1016/j.knosys.2011.07.021.
Blerina Lika, Kostas Kolomvatsos e Stathes Hadjiefthymiades, Facing the cold start problem in recommender systems, in Expert Systems with Applications, vol. 41, n. 4, marzo 2014, pp. 2065-2073, DOI:10.1016/j.eswa.2013.09.005.
Lei Hou, Xue Pan e Kecheng Liu, Balancing the popularity bias of object similarities for personalised recommendation, in The European Physical Journal B, vol. 91, n. 3, 7 marzo 2018, DOI:10.1140/epjb/e2018-80374-8.
Himan Abdollahpouri, Robin Burke e Bamshad Mobasher, Controlling Popularity Bias in Learning-to-Rank Recommendation, ACM, 27 agosto 2017, pp. 42-46, DOI:10.1145/3109859.3109912.
Yoon-Joo Park e Alexander Tuzhilin, The long tail of recommender systems and how to leverage it, ACM, 23 ottobre 2008, pp. 11-18, DOI:10.1145/1454008.1454012.
(EN) Michael J. Pazzani e Daniel Billsus, Content-Based Recommendation Systems, in The Adaptive Web, Springer Berlin Heidelberg, 2007, pp. 325-341, DOI:10.1007/978-3-540-72079-9_10.
Li Chen, Guanliang Chen e Feng Wang, Recommender systems based on user reviews: the state of the art, in User Modeling and User-Adapted Interaction, vol. 25, n. 2, 22 gennaio 2015, pp. 99-154, DOI:10.1007/s11257-015-9155-5.
Al Mamunur Rashid, George Karypis e John Riedl, Learning preferences of new users in recommender systems, in ACM SIGKDD Explorations Newsletter, vol. 10, n. 2, 20 dicembre 2008, p. 90, DOI:10.1145/1540276.1540302.
J. Bobadilla, F. Ortega, A. Hernando e A. Gutiérrez, Recommender systems survey, in Knowledge-Based Systems, vol. 46, luglio 2013, pp. 109-132, DOI:10.1016/j.knosys.2013.03.012.
Zi-Ke Zhang, Chuang Liu, Yi-Cheng Zhang e Tao Zhou, Solving the cold-start problem in recommender systems with social tags, in EPL (Europhysics Letters), vol. 92, n. 2, 1º ottobre 2010, p. 28002, DOI:10.1209/0295-5075/92/28002.
Zan Huang, Hsinchun Chen e Daniel Zeng, Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering, in ACM Transactions on Information Systems, vol. 22, n. 1, 1º gennaio 2004, pp. 116-142, DOI:10.1145/963770.963775.
J. Salter e N. Antonopoulos, CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering, in IEEE Intelligent Systems, vol. 21, n. 1, gennaio 2006, pp. 35-41, DOI:10.1109/MIS.2006.4.
(EN) Robin Burke, Hybrid Web Recommender Systems, in The Adaptive Web, Springer Berlin Heidelberg, 2007, pp. 377-408, DOI:10.1007/978-3-540-72079-9_12.
Ignacio Fernández-Tobías, Matthias Braunhofer, Mehdi Elahi, Francesco Ricci e Iván Cantador, Alleviating the new user problem in collaborative filtering by exploiting personality information, in User Modeling and User-Adapted Interaction, 2016, DOI:10.1007/s11257-016-9172-z.
Zeno Gantner, Lucas Drumond e Cristoph Freudenthaler, Learning Attribute-to-Feature Mappings for Cold-Start Recommendations, 20 gennaio 2011, DOI:10.1109/ICDM.2010.129.
Yehuda Koren, Robert Bell e Chris Volinsky, Matrix Factorization Techniques for Recommender Systems, in Computer, vol. 42, n. 8, agosto 2009, pp. 30-37, DOI:10.1109/MC.2009.263.
Deepak Agarwal e Bee-Chung Chen, Regression-based latent factor models, ACM, 28 giugno 2009, pp. 19-28, DOI:10.1145/1557019.1557029.
Leonardo Cella, Stefano Cereda, Massimo Quadrana e Paolo Cremonesi, Deriving Item Features Relevance from Past User Interactions, in UMAP '17 Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, 2017, pp. 275-279, DOI:10.1145/3079628.3079695.
Mohit Sharma, Jiayu Zhou, Junling Hu e George Karypis, Feature-based factorized Bilinear Similarity Model for Cold-Start Top-n Item Recommendation, in Proceedings of the 2015 SIAM International Conference on Data Mining, 2015, DOI:10.1137/1.9781611974010.22.
(EN) Panagiotis Symeonidis, Alexandros Nanopoulos e Yannis Manolopoulos, Feature-Weighted User Model for Recommender Systems, in User Modeling 2007, Springer Berlin Heidelberg, 25 luglio 2007, pp. 97-106, DOI:10.1007/978-3-540-73078-1_13.
Steffen Rendle, Factorization Machines with libFM, in ACM Transactions on Intelligent Systems and Technology, vol. 3, n. 3, 1º maggio 2012, pp. 1-22, DOI:10.1145/2168752.2168771.
Steffen Rendle, Factorization Machines, IEEE, 2010, ISBN 978-1-4244-9131-5.

oadoi.org

Jesús Bobadilla, Fernando Ortega, Antonio Hernando e Jesús Bernal, A collaborative filtering approach to mitigate the new user cold start problem, in Knowledge-Based Systems, vol. 26, febbraio 2012, pp. 225-238, DOI:10.1016/j.knosys.2011.07.021.
Blerina Lika, Kostas Kolomvatsos e Stathes Hadjiefthymiades, Facing the cold start problem in recommender systems, in Expert Systems with Applications, vol. 41, n. 4, marzo 2014, pp. 2065-2073, DOI:10.1016/j.eswa.2013.09.005.
Lei Hou, Xue Pan e Kecheng Liu, Balancing the popularity bias of object similarities for personalised recommendation, in The European Physical Journal B, vol. 91, n. 3, 7 marzo 2018, DOI:10.1140/epjb/e2018-80374-8.
Himan Abdollahpouri, Robin Burke e Bamshad Mobasher, Controlling Popularity Bias in Learning-to-Rank Recommendation, ACM, 27 agosto 2017, pp. 42-46, DOI:10.1145/3109859.3109912.
Yoon-Joo Park e Alexander Tuzhilin, The long tail of recommender systems and how to leverage it, ACM, 23 ottobre 2008, pp. 11-18, DOI:10.1145/1454008.1454012.
(EN) Michael J. Pazzani e Daniel Billsus, Content-Based Recommendation Systems, in The Adaptive Web, Springer Berlin Heidelberg, 2007, pp. 325-341, DOI:10.1007/978-3-540-72079-9_10.
Li Chen, Guanliang Chen e Feng Wang, Recommender systems based on user reviews: the state of the art, in User Modeling and User-Adapted Interaction, vol. 25, n. 2, 22 gennaio 2015, pp. 99-154, DOI:10.1007/s11257-015-9155-5.
Al Mamunur Rashid, George Karypis e John Riedl, Learning preferences of new users in recommender systems, in ACM SIGKDD Explorations Newsletter, vol. 10, n. 2, 20 dicembre 2008, p. 90, DOI:10.1145/1540276.1540302.
J. Bobadilla, F. Ortega, A. Hernando e A. Gutiérrez, Recommender systems survey, in Knowledge-Based Systems, vol. 46, luglio 2013, pp. 109-132, DOI:10.1016/j.knosys.2013.03.012.
Zi-Ke Zhang, Chuang Liu, Yi-Cheng Zhang e Tao Zhou, Solving the cold-start problem in recommender systems with social tags, in EPL (Europhysics Letters), vol. 92, n. 2, 1º ottobre 2010, p. 28002, DOI:10.1209/0295-5075/92/28002.
Zan Huang, Hsinchun Chen e Daniel Zeng, Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering, in ACM Transactions on Information Systems, vol. 22, n. 1, 1º gennaio 2004, pp. 116-142, DOI:10.1145/963770.963775.
J. Salter e N. Antonopoulos, CinemaScreen Recommender Agent: Combining Collaborative and Content-Based Filtering, in IEEE Intelligent Systems, vol. 21, n. 1, gennaio 2006, pp. 35-41, DOI:10.1109/MIS.2006.4.
(EN) Robin Burke, Hybrid Web Recommender Systems, in The Adaptive Web, Springer Berlin Heidelberg, 2007, pp. 377-408, DOI:10.1007/978-3-540-72079-9_12.
Ignacio Fernández-Tobías, Matthias Braunhofer, Mehdi Elahi, Francesco Ricci e Iván Cantador, Alleviating the new user problem in collaborative filtering by exploiting personality information, in User Modeling and User-Adapted Interaction, 2016, DOI:10.1007/s11257-016-9172-z.
Zeno Gantner, Lucas Drumond e Cristoph Freudenthaler, Learning Attribute-to-Feature Mappings for Cold-Start Recommendations, 20 gennaio 2011, DOI:10.1109/ICDM.2010.129.
Yehuda Koren, Robert Bell e Chris Volinsky, Matrix Factorization Techniques for Recommender Systems, in Computer, vol. 42, n. 8, agosto 2009, pp. 30-37, DOI:10.1109/MC.2009.263.
Deepak Agarwal e Bee-Chung Chen, Regression-based latent factor models, ACM, 28 giugno 2009, pp. 19-28, DOI:10.1145/1557019.1557029.
Leonardo Cella, Stefano Cereda, Massimo Quadrana e Paolo Cremonesi, Deriving Item Features Relevance from Past User Interactions, in UMAP '17 Proceedings of the 25th Conference on User Modeling, Adaptation and Personalization, 2017, pp. 275-279, DOI:10.1145/3079628.3079695.
Mohit Sharma, Jiayu Zhou, Junling Hu e George Karypis, Feature-based factorized Bilinear Similarity Model for Cold-Start Top-n Item Recommendation, in Proceedings of the 2015 SIAM International Conference on Data Mining, 2015, DOI:10.1137/1.9781611974010.22.
(EN) Panagiotis Symeonidis, Alexandros Nanopoulos e Yannis Manolopoulos, Feature-Weighted User Model for Recommender Systems, in User Modeling 2007, Springer Berlin Heidelberg, 25 luglio 2007, pp. 97-106, DOI:10.1007/978-3-540-73078-1_13.
Steffen Rendle, Factorization Machines with libFM, in ACM Transactions on Intelligent Systems and Technology, vol. 3, n. 3, 1º maggio 2012, pp. 1-22, DOI:10.1145/2168752.2168771.

psu.edu

citeseer.ist.psu.edu

Andrew I. Schein, Alexandrin Popescul, Lyle H. Ungar e David M. Pennock, Methods and Metrics for Cold-Start Recommendations, Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2002), New York City, New York, ACM, 2002, pp. 253-260, ISBN 1-58113-561-0. URL consultato il 2 febbraio 2008.
Yezdi Lashkari, Max Metral e Pattie Maes, Collaborative Interface Agents, Proceedings of the Twelfth National Conference on Artificial Intelligence, Seattle, Washington, AAAI Press, 1994, pp. 444-449, ISBN 0-262-61102-3. URL consultato il 2 febbraio 2008.

researchgate.net

Mehdi Elahi, Francesco Ricci e Neil Rubens, A survey of active learning in collaborative filtering recommender systems, Computer Science Review, 2016. Ospitato su Elsevier.

springer.com

rd.springer.com

Mehdi Elahi, Francesco Ricci e Neil Rubens, Active Learning in Collaborative Filtering Recommender Systems, Springer International Publishing, pp. 113-124, ISBN 978-3-319-10491-1.
Marko Tkalcic e Li Chen, Personality and Recommender Systems, in Francesco Ricci, Lior Rokach e Bracha Shapira (a cura di), Recommender Systems Handbook, 2nd, Springer US, 2016, ISBN 978-1-4899-7637-6.
Neil Rubens, Mehdi Elahi, Masashi Sugiyama e Dain Kaplan, Active Learning in Recommender Systems, in Francesco Ricci, Lior Rokach e Bracha Shapira (a cura di), Recommender Systems Handbook, 2nd, Springer US, 2016, ISBN 978-1-4899-7637-6.

web.archive.org

Vendor attempts to crack 'cold start' problem in content recommendations (PDF), in Mobile Media, United Kingdom, Informa Telecoms & Media, 29 giugno 2007, p. 18. URL consultato il 2 febbraio 2008 (archiviato dall'url originale il 1º agosto 2008).