{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T21:50:46Z","timestamp":1766181046905,"version":"3.41.0"},"publisher-location":"New York, New York, USA","reference-count":29,"publisher":"ACM Press","license":[{"start":{"date-parts":[[2017,1,1]],"date-time":"2017-01-01T00:00:00Z","timestamp":1483228800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"AOL","award":["AOL-funded Connected Experiences Lab"],"award-info":[{"award-number":["AOL-funded Connected Experiences Lab"]}]},{"name":"Adobe Research","award":["Gift funding"],"award-info":[{"award-number":["Gift funding"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017]]},"DOI":"10.1145\/3041021.3054183","type":"proceedings-article","created":{"date-parts":[[2018,1,11]],"date-time":"2018-01-11T18:39:25Z","timestamp":1515695965000},"page":"485-493","source":"Crossref","is-referenced-by-count":13,"title":["Personalizing Software and Web Services by Integrating Unstructured Application Usage Traces"],"prefix":"10.1145","author":[{"given":"Longqi","family":"Yang","sequence":"first","affiliation":[{"name":"Cornell University, New York City, NY, USA"}]},{"given":"Chen","family":"Fang","sequence":"additional","affiliation":[{"name":"Adobe Research, San Jose, CA, USA"}]},{"given":"Hailin","family":"Jin","sequence":"additional","affiliation":[{"name":"Adobe Research, San Jose, CA, USA"}]},{"given":"Matthew D.","family":"Hoffman","sequence":"additional","affiliation":[{"name":"Google, San Francisco, CA, USA"}]},{"given":"Deborah","family":"Estrin","sequence":"additional","affiliation":[{"name":"Cornell University, New York City, NY, USA"}]}],"member":"320","reference":[{"key":"key-10.1145\/3041021.3054183-1","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/2642918.2647395"},{"key":"key-10.1145\/3041021.3054183-2","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/2783258.2788614"},{"key":"key-10.1145\/3041021.3054183-3","doi-asserted-by":"crossref","unstructured":"J. Bennett and S. Lanning. The netflix prize. In Proceedings of KDD cup and workshop, volume 2007, page 35, 2007.","DOI":"10.1145\/1345448.1345459"},{"key":"key-10.1145\/3041021.3054183-4","unstructured":"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."},{"key":"key-10.1145\/3041021.3054183-5","unstructured":"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."},{"key":"key-10.1145\/3041021.3054183-6","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/2047196.2047220"},{"key":"key-10.1145\/3041021.3054183-7","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/2818052.2874317"},{"key":"key-10.1145\/3041021.3054183-8","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/2783258.2788627"},{"key":"key-10.1145\/3041021.3054183-9","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/1639714.1639725"},{"key":"key-10.1145\/3041021.3054183-10","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/1835449.1835484"},{"key":"key-10.1145\/3041021.3054183-11","unstructured":"K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. arXiv preprint arXiv:1512.03385, 2015."},{"key":"key-10.1145\/3041021.3054183-12","unstructured":"R. He and J. McAuley. Vbpr: visual bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1510.01784, 2015."},{"key":"key-10.1145\/3041021.3054183-13","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/2872427.2883037"},{"key":"key-10.1145\/3041021.3054183-14","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/2872427.2883006"},{"key":"key-10.1145\/3041021.3054183-15","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1109\/ICDM.2008.22"},{"key":"key-10.1145\/3041021.3054183-16","doi-asserted-by":"crossref","unstructured":"Y. Koren, R. Bell, C. Volinsky, et al. Matrix factorization techniques for recommender systems. Computer, 42(8):30--37, 2009.","DOI":"10.1109\/MC.2009.263"},{"key":"key-10.1145\/3041021.3054183-17","unstructured":"Q. V. Le and T. Mikolov. Distributed representations of sentences and documents. In ICML, volume 14, pages 1188--1196, 2014."},{"key":"key-10.1145\/3041021.3054183-18","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/1970378.1970380"},{"key":"key-10.1145\/3041021.3054183-19","doi-asserted-by":"crossref","unstructured":"D. C. Liu and J. Nocedal. On the limited memory bfgs method for large scale optimization. Mathematical programming, 45(1--3):503--528, 1989.","DOI":"10.1007\/BF01589116"},{"key":"key-10.1145\/3041021.3054183-20","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/1622176.1622214"},{"key":"key-10.1145\/3041021.3054183-21","unstructured":"T. Mikolov and J. Dean. Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems, 2013."},{"key":"key-10.1145\/3041021.3054183-22","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/1639714.1639720"},{"key":"key-10.1145\/3041021.3054183-23","unstructured":"D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning representations by back-propagating errors. Cognitive modeling, 5(3):1, 1988."},{"key":"key-10.1145\/3041021.3054183-24","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/2939672.2939690"},{"key":"key-10.1145\/3041021.3054183-25","unstructured":"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."},{"key":"key-10.1145\/3041021.3054183-26","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/2020408.2020480"},{"key":"key-10.1145\/3041021.3054183-27","unstructured":"J. Weston, S. Bengio, and N. Usunier. Wsabie: Scaling up to large vocabulary image annotation. 2011."},{"key":"key-10.1145\/3041021.3054183-28","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/2647868.2654920"},{"key":"key-10.1145\/3041021.3054183-29","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1145\/2872427.2882995"}],"event":{"number":"26","sponsor":["SIGWEB, ACM Special Interest Group on Hypertext, Hypermedia, and Web","IW3C2, International World Wide Web Conference Committee"],"acronym":"WWW '17 Companion","name":"the 26th International Conference","start":{"date-parts":[[2017,4,3]]},"location":"Perth, Australia","end":{"date-parts":[[2017,4,7]]}},"container-title":["Proceedings of the 26th International Conference on World Wide Web Companion - WWW '17 Companion"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3041021.3054183","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/dl.acm.org\/ft_gateway.cfm?id=3054183&ftid=1865157&dwn=1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T03:03:19Z","timestamp":1750215799000},"score":1,"resource":{"primary":{"URL":"http:\/\/dl.acm.org\/citation.cfm?doid=3041021.3054183"}},"subtitle":[],"proceedings-subject":"World Wide Web Companion","short-title":[],"issued":{"date-parts":[[2017]]},"references-count":29,"URL":"https:\/\/doi.org\/10.1145\/3041021.3054183","relation":{},"subject":[],"published":{"date-parts":[[2017]]}}}