App recommendation: a contest between satisfaction and temptation

ABSTRACT

Due to the huge and still rapidly growing number of mobile applications (apps), it becomes necessary to provide users an app recommendation service. Different from conventional item recommendation where the user interest is the primary factor, app recommendation also needs to consider factors that invoke a user to replace an old app (if she already has one) with a new app. In this work we propose an Actual- Tempting model that captures such factors in the decision process of mobile app adoption. The model assumes that each owned app has an actual satisfactory value and a new app under consideration has a tempting value. The former stands for the real satisfactory value the owned app brings to the user while the latter represents the estimated value the new app may seemingly have. We argue that the process of app adoption therefore is a contest between the owned apps' actual values and the candidate app's tempting value. Via the extensive experiments we show that the AT model performs significantly better than the conventional recommendation techniques such as collaborative filtering and content-based recommendation. Furthermore, the best recommendation performance is achieved when the AT model is combined with them.

References

1. D. Agarwal and B.-C. Chen. flda: matrix factorization through latent dirichlet allocation. In WSDM, pages 91--100, 2010.
2. 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.
3. 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.
4. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. J. Mach. Learn. Res., 3:993--1022, 2003.
5. C.-C. Chang and C.-J. Lin. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011.
6. 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.
7. 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.
8. 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.
9. E. Costa-Montenegro, A. B. Barragans-Mart1nez, and M. Rey-Lopez. 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.
10. P. Cremonesi, Y. Koren, and R. Turrin. Performance of recommender algorithms on top-n recommendation tasks. In RecSys, pages 39--46, 2010.
11. C. Davidsson and S. Moritz. Utilizing implicit feedback and context to recommend mobile applications from first use. In CaRR, pages 19--22, 2011.
12. D. Graziano. Apple's app store reaches 25 billion downloads. http://www.bgr.com/2012/03/05/apples-appstore-reaches-25-billion-downloads/, 2012.
13. J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl. An algorithmic framework for performing collaborative filtering. In SIGIR, pages 230--237, 1999.
14. Y. Koren, R. M. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. IEEE Computer, 42(8):30--37, 2009.
15. G. Lekakos and P. Caravelas. A hybrid approach for movie recommendation. Multimedia Tools Appl., 36(1-2):55--70, 2008.
16. 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.
17. H. Ma, D. Zhou, C. Liu, M. R. Lyu, and I. King. Recommender systems with social regularization. In WSDM, pages 287--296, 2011.
18. R. J. Mooney and L. Roy. Content-based book recommending using learning for text categorization. In DL, pages 195--204, 2000.
19. M. J. Pazzani and D. Billsus. Content-based recommendation systems. In The Adaptive Web: Methos and Strategies of Web Personalization, pages 325-341. Springer-Verlag, 2007.
20. J. C. Platt. Probabilities for SV Machines, pages 61--74. MIT Press, 2000.
21. J. D. M. Rennie and N. Srebro. Fast maximum margin matrix factorization for collaborative prediction. In ICML, pages 713--719, 2005.
22. R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In NIPS, 2007.
23. R. Salakhutdinov and A. Mnih. Bayesian probabilistic matrix factorization using markov chain monte carlo. In ICML, pages 880--887, 2008.
24. G. Salton. Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, 1989.
25. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW, pages 285--295, 2001.
26. H. Shan and A. Banerjee. Generalized probabilistic matrix factorizations for collaborative filtering. Technical report, University of Minnesota, Twin Cities, 2010.
27. H. Shan and A. Banerjee. Generalized probabilistic matrix factorizations for collaborative filtering. In ICDM, pages 1025--1030, 2010.
28. N. Srebro and T. Jaakkola. Weighted low-rank approximations. In ICML, pages 720--727, 2003.
29. N. Srebro, J. D. M. Rennie, and T. Jaakkola. Maximum-margin matrix factorization. In NIPS, 2004.
30. M. Steyvers and T. Griffiths. Handbook of Latent Semantic Analysis, chapter Probabilistic Topic Models.
31. J. T. Text categorization with support vector machines : Learning with many relevant features. ECML, pages 137--142, 1998.
32. V. N. Vapnik. The nature of statistical learning theory. Springer-Verlag New York, Inc., 1995.
33. J. Wang and Y. Zhang. Utilizing marginal net utility for recommendation in e-commerce. In SIGIR, pages 1003--1012, 2011.
34. G. M. Weiss. Mining with rarity: a unifying framework. SIGKDD Explor. Newsl., 6(1):7--19, 2004.
35. 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.
36. B. Yan and G. Chen. Appjoy: personalized mobile application discovery. In MobiSys, pages 113--126, 2011.
37. S.-H. Yang, B. Long, A. J. Smola, H. Zha, and Z. Zheng. Collaborative competitive filtering: learning recommender using context of user choice. In SIGIR, pages 295--304, 2011.
38. M. Ye, P. Yin, andW.-C. Lee. Location recommendation for location-based social networks. In GIS, pages 458--461, 2010.
39. 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.
40. 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.
41. T. Zhou, Z. Kuscsik, J.-G. Liu, M. Medo, J. R. Wakeling, and Y.-C. Zhang. Solving the apparent diversity-accuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences, 107:4511--4515, 2010.

Index Terms

• Information systems

  • Information retrieval