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Volumn , Issue , 2013, Pages 395-404

App recommendation: A contest between satisfaction and temptation

Author keywords

app recommendation; contest; smartphone apps

Indexed keywords

APP RECOMMENDATION; CONTENT-BASED RECOMMENDATION; CONTEST; DECISION PROCESS; MOBILE APP; MOBILE APPLICATIONS; PRIMARY FACTORS; RECOMMENDATION PERFORMANCE; RECOMMENDATION TECHNIQUES; SMARTPHONE APPS; USER INTERESTS;

EID: 84874246055     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2433396.2433446     Document Type: Conference Paper
Times cited : (86)

References (41)
  • 1
    • 77950940282 scopus 로고    scopus 로고
    • Flda: Matrix factorization through latent dirichlet allocation
    • D. Agarwal and B.-C. Chen. flda: matrix factorization through latent dirichlet allocation. In WSDM, pages 91-100, 2010.
    • (2010) WSDM , pp. 91-100
    • Agarwal, D.1    Chen, B.-C.2
  • 2
    • 80052419365 scopus 로고    scopus 로고
    • An analysis of probabilistic methods for top-n recommendation in collaborative filtering
    • N. Barbieri and G. Manco. An analysis of probabilistic methods for top-n recommendation in collaborative filtering. In ECML/PKDD (1), pages 172-187, 2011.
    • (2011) ECML/PKDD , Issue.1 , pp. 172-187
    • Barbieri, N.1    Manco, G.2
  • 3
    • 84976680153 scopus 로고
    • Information filtering and information retrieval: Two sides of the same coin?
    • N. J. Belkin and W. B. Croft. Information filtering and information retrieval: two sides of the same coin? Commun. ACM, 35(12):29-38, 1992.
    • (1992) Commun. ACM , vol.35 , Issue.12 , pp. 29-38
    • Belkin, N.J.1    Croft, W.B.2
  • 6
    • 27144549260 scopus 로고    scopus 로고
    • Editorial: Special issue on learning from imbalanced data sets
    • N. V. Chawla, N. Japkowicz, and A. Kotcz. Editorial: special issue on learning from imbalanced data sets. SIGKDD Explor. Newsl., 6(1):1-6, 2004.
    • (2004) SIGKDD Explor. Newsl. , vol.6 , Issue.1 , pp. 1-6
    • Chawla, N.V.1    Japkowicz, N.2    Kotcz, A.3
  • 7
    • 0035747351 scopus 로고    scopus 로고
    • A music recommendation system based on music data grouping and user interests
    • H.-C. Chen and A. L. P. Chen. A music recommendation system based on music data grouping and user interests. In CIKM, pages 231-238, 2001.
    • (2001) CIKM , pp. 231-238
    • Chen, H.-C.1    Chen, A.L.P.2
  • 8
    • 12244294767 scopus 로고    scopus 로고
    • Mining customer product ratings for personalized marketing
    • K.-W. Cheung, J. T. Kwok, M. H. Law, and K.-C. Tsui. Mining customer product ratings for personalized marketing. Decis. Support Syst., 35(2):231-243, 2003.
    • (2003) Decis. Support Syst. , vol.35 , Issue.2 , pp. 231-243
    • Cheung, K.-W.1    Kwok, J.T.2    Law, M.H.3    Tsui, K.-C.4
  • 9
    • 84859209606 scopus 로고    scopus 로고
    • Which app? a recommender system of applications in markets: Implementation of the service for monitoring users' interaction
    • E. Costa-Montenegro, A. B. Barragáns-Martínez, and M. Rey-López. Which app? a recommender system of applications in markets: Implementation of the service for monitoring users' interaction. Expert Syst. Appl., 39(10):9367-9375, 2012.
    • (2012) Expert Syst. Appl. , vol.39 , Issue.10 , pp. 9367-9375
    • Costa-Montenegro, E.1    Barragáns-Martínez, A.B.2    Rey-López, M.3
  • 10
    • 78649914423 scopus 로고    scopus 로고
    • Performance of recommender algorithms on top-n recommendation tasks
    • P. Cremonesi, Y. Koren, and R. Turrin. Performance of recommender algorithms on top-n recommendation tasks. In RecSys, pages 39-46, 2010.
    • (2010) RecSys , pp. 39-46
    • Cremonesi, P.1    Koren, Y.2    Turrin, R.3
  • 11
    • 79955153810 scopus 로고    scopus 로고
    • Utilizing implicit feedback and context to recommend mobile applications from first use
    • C. Davidsson and S. Moritz. Utilizing implicit feedback and context to recommend mobile applications from first use. In CaRR, pages 19-22, 2011.
    • (2011) CaRR , pp. 19-22
    • Davidsson, C.1    Moritz, S.2
  • 13
    • 85015559680 scopus 로고    scopus 로고
    • An algorithmic framework for performing collaborative filtering
    • J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In SIGIR, pages 230-237, 1999.
    • (1999) SIGIR , pp. 230-237
    • Herlocker, J.L.1    Konstan, J.A.2    Borchers, A.3    Riedl, J.4
  • 14
    • 85008044987 scopus 로고    scopus 로고
    • Matrix factorization techniques for recommender systems
    • Y. Koren, R. M. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, 42(8):30-37, 2009.
    • (2009) IEEE Computer , vol.42 , Issue.8 , pp. 30-37
    • Koren, Y.1    Bell, R.M.2    Volinsky, C.3
  • 15
    • 37249036549 scopus 로고    scopus 로고
    • A hybrid approach for movie recommendation
    • DOI 10.1007/s11042-006-0082-7, Special Issue on Personalized and Mobile Digital TV Applications; Guest Editor: Konstantinos Chorianopoulos
    • G. Lekakos and P. Caravelas. A hybrid approach for movie recommendation. Multimedia Tools Appl., 36(1-2):55-70, 2008. (Pubitemid 350274737)
    • (2008) Multimedia Tools and Applications , vol.36 , Issue.1-2 , pp. 55-70
    • Lekakos, G.1    Caravelas, P.2
  • 16
    • 80052881372 scopus 로고    scopus 로고
    • Content-based recommender systems: State of the art and trends
    • P. Lops, M. de Gemmis, and G. Semeraro. Content-based recommender systems: State of the art and trends. In Recommender Systems Handbook, pages 73-105. 2011.
    • (2011) Recommender Systems Handbook , pp. 73-105
    • Lops, P.1    De Gemmis, M.2    Semeraro, G.3
  • 17
    • 79952399179 scopus 로고    scopus 로고
    • Recommender systems with social regularization
    • H. Ma, D. Zhou, C. Liu, M. R. Lyu, and I. King. Recommender systems with social regularization. In WSDM, pages 287-296, 2011.
    • (2011) WSDM , pp. 287-296
    • Ma, H.1    Zhou, D.2    Liu, C.3    Lyu, M.R.4    King, I.5
  • 18
    • 0033653019 scopus 로고    scopus 로고
    • Content-based book recommending using learning for text categorization
    • R. J. Mooney and L. Roy. Content-based book recommending using learning for text categorization. In DL, pages 195-204, 2000.
    • (2000) DL , pp. 195-204
    • Mooney, R.J.1    Roy, L.2
  • 21
    • 31844451557 scopus 로고    scopus 로고
    • Fast maximum margin matrix factorization for collaborative prediction
    • J. D. M. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. In ICML, pages 713-719, 2005.
    • (2005) ICML , pp. 713-719
    • Rennie, J.D.M.1    Srebro, N.2
  • 22
    • 48349135120 scopus 로고    scopus 로고
    • Probabilistic matrix factorization
    • R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In NIPS, 2007.
    • (2007) NIPS
    • Salakhutdinov, R.1    Mnih, A.2
  • 23
    • 56449131205 scopus 로고    scopus 로고
    • Bayesian probabilistic matrix factorization using markov chain monte carlo
    • R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using markov chain monte carlo. In ICML, pages 880-887, 2008.
    • (2008) ICML , pp. 880-887
    • Salakhutdinov, R.1    Mnih, A.2
  • 25
    • 85052617391 scopus 로고    scopus 로고
    • Item-based collaborative filtering recommendation algorithms
    • B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW, pages 285-295, 2001.
    • (2001) WWW , pp. 285-295
    • Sarwar, B.1    Karypis, G.2    Konstan, J.3    Riedl, J.4
  • 27
    • 79951750366 scopus 로고    scopus 로고
    • Generalized probabilistic matrix factorizations for collaborative filtering
    • H. Shan and A. Banerjee. Generalized probabilistic matrix factorizations for collaborative filtering. In ICDM, pages 1025-1030, 2010.
    • (2010) ICDM , pp. 1025-1030
    • Shan, H.1    Banerjee, A.2
  • 28
    • 1942516801 scopus 로고    scopus 로고
    • Weighted low-rank approximations
    • N. Srebro and T. Jaakkola. Weighted low-rank approximations. In ICML, pages 720-727, 2003.
    • (2003) ICML , pp. 720-727
    • Srebro, N.1    Jaakkola, T.2
  • 31
    • 84957069814 scopus 로고    scopus 로고
    • Text categorization with support vector machines: Learning with many relevant features
    • J. T. Text categorization with support vector machines : Learning with many relevant features. ECML, pages 137-142, 1998.
    • (1998) ECML , pp. 137-142
    • T, J.1
  • 33
    • 80052121603 scopus 로고    scopus 로고
    • Utilizing marginal net utility for recommendation in e-commerce
    • J. Wang and Y. Zhang. Utilizing marginal net utility for recommendation in e-commerce. In SIGIR, pages 1003-1012, 2011.
    • (2011) SIGIR , pp. 1003-1012
    • Wang, J.1    Zhang, Y.2
  • 34
    • 20844458491 scopus 로고    scopus 로고
    • Mining with rarity: A unifying framework
    • G. M. Weiss. Mining with rarity: a unifying framework. SIGKDD Explor. Newsl., 6(1):7-19, 2004.
    • (2004) SIGKDD Explor. Newsl. , vol.6 , Issue.1 , pp. 7-19
    • Weiss, G.M.1
  • 35
    • 82955197342 scopus 로고    scopus 로고
    • Identifying diverse usage behaviors of smartphone apps
    • Q. Xu, J. Erman, A. Gerber, Z. Mao, J. Pang, and S. Venkataraman. Identifying diverse usage behaviors of smartphone apps. In IMC, pages 329-344, 2011.
    • (2011) IMC , pp. 329-344
    • Xu, Q.1    Erman, J.2    Gerber, A.3    Mao, Z.4    Pang, J.5    Venkataraman, S.6
  • 36
    • 79961035462 scopus 로고    scopus 로고
    • Appjoy: Personalized mobile application discovery
    • B. Yan and G. Chen. Appjoy: personalized mobile application discovery. In MobiSys, pages 113-126, 2011.
    • (2011) MobiSys , pp. 113-126
    • Yan, B.1    Chen, G.2
  • 37
    • 80052129672 scopus 로고    scopus 로고
    • Collaborative competitive filtering: Learning recommender using context of user choice
    • S.-H. Yang, B. Long, A. J. Smola, H. Zha, and Z. Zheng. Collaborative competitive filtering: learning recommender using context of user choice. SIGIR, pages 295-304, 2011.
    • (2011) SIGIR , pp. 295-304
    • Yang, S.-H.1    Long, B.2    Smola, A.J.3    Zha, H.4    Zheng, Z.5
  • 38
    • 78650589837 scopus 로고    scopus 로고
    • Location recommendation for location-based social networks
    • M. Ye, P. Yin, and W.-C. Lee. Location recommendation for location-based social networks. In GIS, pages 458-461, 2010.
    • (2010) GIS , pp. 458-461
    • Ye, M.1    Yin, P.2    Lee, W.-C.3
  • 39
    • 80052134524 scopus 로고    scopus 로고
    • Exploiting geographical influence for collaborative point-of-interest recommendation
    • M. Ye, P. Yin, W.-C. Lee, and D. L. Lee. Exploiting geographical influence for collaborative point-of-interest recommendation. In SIGIR, pages 325-334, 2011.
    • (2011) SIGIR , pp. 325-334
    • Ye, M.1    Yin, P.2    Lee, W.-C.3    Lee, D.L.4
  • 40
    • 2342586046 scopus 로고    scopus 로고
    • Collaborative ensemble learning: Combining collaborative and content-based information filtering via hierarchical bayes
    • K. Yu, A. Schwaighofer, and V. Tresp. Collaborative ensemble learning: combining collaborative and content-based information filtering via hierarchical bayes. In UAI, pages 616-623, 2003.
    • (2003) UAI , pp. 616-623
    • Yu, K.1    Schwaighofer, A.2    Tresp, V.3


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