ABSTRACT
Users of software applications generate vast amounts of unstructured log-trace data. These traces contain clues to the intentions and interests of those users, but service providers may find it difficult to uncover and exploit those clues. In this paper, we propose a framework for personalizing software and web services by leveraging such unstructured traces. We use 6 months of Photoshop usage history and 7 years of interaction records from 67K Behance users to design, develop, and validate a user-modeling technique that discovers highly discriminative representations of Photoshop users; we refer to the model as cutilization-to-vector, util2vec. We demonstrate the promise of this approach for three sample applications: (1) a practical user-tagging system that automatically predicts areas of focus for millions of Photoshop users; (2) a two-phase recommendation model that enables cold-start personalized recommendations for many new Behance users who have Photoshop usage data, improving recommendation quality (Recall@100) by 21.2% over a popularity-based recommender; and (3) a novel inspiration engine that provides real-time personalized inspirations to artists. We believe that this work demonstrates the potential impact of unstructured usage-log data for personalization.
- E. Adar, M. Dontcheva, and G. Laput. Commandspace: modeling the relationships between tasks, descriptions and features. In Proceedings of the 27th annual ACM symposium on User interface software and technology, pages 167--176. ACM, 2014. Google ScholarDigital Library
- D. Agarwal, B.-C. Chen, Q. He, Z. Hua, G. Lebanon, Y. Ma, P. Shivaswamy, H.-P. Tseng, J. Yang, and L. Zhang. Personalizing linkedin feed. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1651--1660. ACM, 2015. Google ScholarDigital Library
- J. Bennett and S. Lanning. The netflix prize. In Proceedings of KDD cup and workshop, volume 2007, page 35, 2007.Google Scholar
- E. Choi, M. T. Bahadori, E. Searles, C. Coffey, and J. Sun. Multi-layer representation learning for medical concepts. arXiv preprint arXiv:1602.05568, 2016.Google Scholar
- J. Duchi, E. Hazan, and Y. Singer. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research, 12(Jul):2121--2159, 2011. Google ScholarDigital Library
- M. Ekstrand, W. Li, T. Grossman, J. Matejka, and G. Fitzmaurice. Searching for software learning resources using application context. In Proceedings of the 24th annual ACM symposium on User interface software and technology, pages 195--204. ACM, 2011. Google ScholarDigital Library
- C. A. Fraser, M. Dontcheva, H. Winnemoeller, and S. Klemmer. Discoveryspace: Crowdsourced suggestions onboard novices in complex software. In Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social Computing Companion, pages 29--32. ACM, 2016. Google ScholarDigital Library
- M. Grbovic, V. Radosavljevic, N. Djuric, N. Bhamidipati, J. Savla, V. Bhagwan, and D. Sharp. E-commerce in your inbox: Product recommendations at scale. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 1809--1818. ACM, 2015. Google ScholarDigital Library
- I. Guy, N. Zwerdling, D. Carmel, I. Ronen, E. Uziel, S. Yogev, and S. Ofek-Koifman. Personalized recommendation of social software items based on social relations. In Proceedings of the third ACM conference on Recommender systems, pages 53--60. ACM, 2009. Google ScholarDigital Library
- I. Guy, N. Zwerdling, I. Ronen, D. Carmel, and E. Uziel. Social media recommendation based on people and tags. In Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval, pages 194--201. ACM, 2010. Google ScholarDigital Library
- K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385, 2015.Google Scholar
- R. He and J. McAuley. Vbpr: visual bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1510.01784, 2015. Google ScholarDigital Library
- R. He and J. McAuley. Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering. In Proceedings of the 25th International Conference on World Wide Web, pages 507--517. International World Wide Web Conferences Steering Committee, 2016. Google ScholarDigital Library
- C.-K. Hsieh, L. Yang, H. Wei, M. Naaman, and D. Estrin. Immersive recommendation: News and event recommendations using personal digital traces. In Proceedings of the 25th International Conference on World Wide Web, pages 51--62. International World Wide Web Conferences Steering Committee, 2016. Google ScholarDigital Library
- Y. Hu, Y. Koren, and C. Volinsky. Collaborative filtering for implicit feedback datasets. In 2008 Eighth IEEE International Conference on Data Mining, pages 263--272. Ieee, 2008. Google ScholarDigital Library
- Y. Koren, R. Bell, C. Volinsky, et al. Matrix factorization techniques for recommender systems. Computer, 42(8):30--37, 2009. Google ScholarDigital Library
- Q. V. Le and T. Mikolov. Distributed representations of sentences and documents. In ICML, volume 14, pages 1188--1196, 2014. Google ScholarDigital Library
- W. Li, J. Matejka, T. Grossman, J. A. Konstan, and G. Fitzmaurice. Design and evaluation of a command recommendation system for software applications. ACM Transactions on Computer-Human Interaction (TOCHI), 18(2):6, 2011. Google ScholarDigital Library
- D. C. Liu and J. Nocedal. On the limited memory bfgs method for large scale optimization. Mathematical programming, 45(1--3):503--528, 1989.Google Scholar
- J. Matejka, W. Li, T. Grossman, and G. Fitzmaurice. Communitycommands: command recommendations for software applications. In Proceedings of the 22nd annual ACM symposium on User interface software and technology, pages 193--202. ACM, 2009. Google ScholarDigital Library
- T. Mikolov and J. Dean. Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 2013. Google ScholarDigital Library
- S.-T. Park and W. Chu. Pairwise preference regression for cold-start recommendation. In Proceedings of the third ACM conference on Recommender systems, pages 21--28. ACM, 2009. Google ScholarDigital Library
- D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning representations by back-propagating errors. Cognitive modeling, 5(3):1, 1988.Google Scholar
- L. Tang, B.-C. Chen, D. Agarwal, and B. Long. An empirical study on recommendation with multiple types of feedback. In Proceedings of the 22th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2016. Google ScholarDigital Library
- A. Van den Oord, S. Dieleman, and B. Schrauwen. Deep content-based music recommendation. In Advances in Neural Information Processing Systems, pages 2643--2651, 2013. Google ScholarDigital Library
- C. Wang and D. M. Blei. Collaborative topic modeling for recommending scientific articles. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 448--456. ACM, 2011. Google ScholarDigital Library
- J. Weston, S. Bengio, and N. Usunier. Wsabie: Scaling up to large vocabulary image annotation. 2011.Google Scholar
- M. Yan, J. Sang, and C. Xu. Mining cross-network association for youtube video promotion. In Proceedings of the 22nd ACM international conference on Multimedia, pages 557--566. ACM, 2014. Google ScholarDigital Library
- F. Zhang, N. J. Yuan, K. Zheng, D. Lian, X. Xie, and Y. Rui. Exploiting dining preference for restaurant recommendation. In Proceedings of the 25th International Conference on World Wide Web, pages 725--735. International World Wide Web Conferences Steering Committee, 2016. Google ScholarDigital Library
Index Terms
- Personalizing Software and Web Services by Integrating Unstructured Application Usage Traces
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