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Volumn 9146, Issue , 2015, Pages 343-349

On the use of cross-domain user preferences and personality traits in collaborative filtering

Author keywords

Collaborative filtering; Cross domain recommendation; Personality

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTERS;

EID: 84937468211     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-20267-9_29     Document Type: Conference Paper
Times cited : (13)

References (12)
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  • 2
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  • 3
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    • Relating Personality Types with User Preferences in Multiple Entertainment Domains
    • Cantador, I., Fernández-Tobías, I., Bellogín, A.: Relating Personality Types with User Preferences in Multiple Entertainment Domains. EMPIRE 2013 (2013)
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    • Cantador, I.1    Fernández-Tobías, I.2    Bellogín, A.3
  • 4
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    • Cross-domain Recommender Systems: A Survey of the State of the Art
    • Fernández-Tobías, I., Cantador, I., Kaminskas, M., Ricci, F.: Cross-domain Recommender Systems: A Survey of the State of the Art. CERI 2012, 187-198 (2012)
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    • Fernández-Tobías, I.1    Cantador, I.2    Kaminskas, M.3    Ricci, F.4
  • 5
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    • Personality-aware collaborative filtering: An empirical study in multiple domains with facebook data
    • Hepp, M., Hoffner, Y. (eds.), Springer, Heidelberg
    • Fernández-Tobías, I., Cantador, I.: Personality-aware collaborative filtering: an empirical study in multiple domains with facebook data. In: Hepp, M., Hoffner, Y. (eds.) EC-Web 2014. LNBIP, vol. 188, pp. 125-137. Springer, Heidelberg (2014)
    • (2014) Ec-Web 2014. LNBIP , vol.188 , pp. 125-137
    • Fernández-Tobías, I.1    Cantador, I.2
  • 6
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    • Cross-domain recommendation via cluster-level latent factor model
    • Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.), Springer, Heidelberg
    • Gao, S., Luo, H., Chen, D., Li, S., Gallinari, P., Guo, J.: Cross-domain recommendation via cluster-level latent factor model. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013, Part II. LNCS, vol. 8189, pp. 161-176. Springer, Heidelberg (2013)
    • (2013) ECML PKDD 2013, Part II. LNCS , vol.8189 , pp. 161-176
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  • 7
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    • A study on user perception of personality-based recommender systems
    • De Bra, P., Kobsa, A., Chin, D. (eds.), Springer, Heidelberg
    • Hu, R., Pu, P.: A study on user perception of personality-based recommender systems. In: De Bra, P., Kobsa, A., Chin, D. (eds.) UMAP 2010. LNCS, vol. 6075, pp. 291-302. Springer, Heidelberg (2010)
    • (2010) UMAP 2010. LNCS , vol.6075 , pp. 291-302
    • Hu, R.1    Pu, P.2
  • 8
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    • Enhancing Collaborative Filtering Systems with Personality Information
    • Hu, R., Pu, P.: Enhancing Collaborative Filtering Systems with Personality Information. RecSys 2011, 197-204 (2011)
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    • Hu, R.1    Pu, P.2
  • 9
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    • Listening, Watching, and Reading: The Structure and Correlates of Entertainment Preferences
    • Rentfrow, P.J., Goldberg, L.R., Zilca, R.: Listening, Watching, and Reading: The Structure and Correlates of Entertainment Preferences. Journal of Personality 79(2), 223-258 (2011)
    • (2011) Journal of Personality , vol.79 , Issue.2 , pp. 223-258
    • Rentfrow, P.J.1    Goldberg, L.R.2    Zilca, R.3
  • 10
    • 84879690586 scopus 로고    scopus 로고
    • Facebook Single and Cross Domain Data for Recommendation Systems
    • Shapira, B., Rokach, L., Freilikhman, S.: Facebook Single and Cross Domain Data for Recommendation Systems. UMUAI 23(2-3), 211-247 (2013)
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  • 11
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    • Tags as bridges between domains: Improving recommendation with tag-induced cross-domain collaborative filtering
    • Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.), Springer, Heidelberg
    • Shi, Y., Larson, M., Hanjalic, A.: Tags as bridges between domains: improving recommendation with tag-induced cross-domain collaborative filtering. In: Konstan, J.A., Conejo, R., Marzo, J.L., Oliver, N. (eds.) UMAP 2011. LNCS, vol. 6787, pp. 305-316. Springer, Heidelberg (2011)
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    • Addressing the New User Problem with a Personality Based User Similarity Measure
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.