Alexandros Karatzoglou
ABSTRACT
The explosive growth of the mobile application (app) market has made it difficult for users to find the most interesting and relevant apps from the hundreds of thousands that exist today. Context is key in the mobile space and so too are proactive services that ease user input and facilitate effective interaction. We believe that to enable truly novel mobile app recommendation and discovery, we need to support real context-aware recommendation that utilizes the diverse range of implicit mobile data available in a fast and scalable manner. In this paper we introduce the Djinn model, a novel context-aware collaborative filtering algorithm for implicit feedback data that is based on tensor factorization. We evaluate our approach using a dataset from an Android mobile app recommendation service called appazaar. Our results show that our approach compares favorably with state-of-the-art collaborative filtering methods.
- M. Böhmer, B. Hecht, J. Schöning, A. Krüger, and G. Bauer. Falling asleep with angry birds, facebook and kindle - a large scale study on mobile application usage. In Proc. of Mobile HCI '11). ACM, 8 2011. Google Scholar
Digital Library
- M. Böhmer, M. Prinz, and G. Bauer. Contextualizing mobile applications for context-aware recommendation. In Adj. Proc. of Pervasive 2010, 2010.Google Scholar
- Y. Chen, D. Pavlov, and J. F. Canny. Large-scale behavioral targeting. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '09, pages 209--218, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- K. Church and B. Smyth. Understanding the intent behind mobile information needs. In Proceedings of IUI '09, pages 247--256. ACM, 2009. Google ScholarDigital Library
- P. Cremonesi, Y. Koren, and R. Turrin. Performance of recommender algorithms on top-n recommendation tasks. In Proc. of RecSys '10, pages 39--46, 2010. Google ScholarDigital Library
- F. L. Hitchcock. The expression of a tensor or a polyadic as a sum of products. Journal of Math. and Physics, 6, 1927.Google Scholar
- Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In Proc. of ICDM '08, pages 263--272, Washington, DC, USA, 2008. IEEE Computer Society. Google ScholarDigital Library
- A. Karatzoglou, X. Amatriain, L. Baltrunas, and N. Oliver. Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In Proc. of RecSys '10, pages 79--86, 2010. Google ScholarDigital Library
- R. Pan and M. Scholz. Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering. In Proc. of KDD '09, pages 667--676, New York, NY, USA, 2009. ACM. Google ScholarDigital Library
- V. Sindhwani, S. S. Bucak, J. Hu, and A. Mojsilovic. One-class matrix completion with low-density factorizations. In Proceedings of the 2010 IEEE International Conference on Data Mining, ICDM '10, pages 1055--1060, Washington, DC, USA, 2010. IEEE Computer Society. Google ScholarDigital Library
- W. Woerndl, C. Schueller, and R. Wojtech. A hybrid recommender system for context-aware recommendations of mobile applications. In Proceedings of the IEEE ICDE, 2007. Google ScholarDigital Library
- B. Yan and G. Chen. Appjoy: personalized mobile application discovery. In Proceedings of the 9th international conference on Mobile systems, applications, and services, MobiSys '11, pages 113--126. ACM, 2011. Google ScholarDigital Library
Index Terms
- Climbing the app wall: enabling mobile app discovery through context-aware recommendations
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