Xiangmin Zhou
ABSTRACT
With the explosive growth of online service platforms, increasing number of people and enterprises are doing everything online. In order for organizations, governments, and individuals to understand their users, and promote their products or services, it is necessary for them to analyse big data and recommend the media or online services in real time. Effective recommendation of items of interest to consumers has become critical for enterprises in domains such as retail, e-commerce, and online media. Driven by the business successes, academic research in this field has also been active for many years. Through many scientific breakthroughs have been achieved, there are still tremendous challenges in developing effective and scalable recommendation systems for real-world industrial applications. Existing solutions focus on recommending items based on pre-set contexts, such as time, location, weather etc. The big data sizes and complex contextual information add further challenges to the deployment of advanced recommender systems. This workshop aims to bring together researchers with wide-ranging backgrounds to identify important research questions, to exchange ideas from different research disciplines, and, more generally, to facilitate discussion and innovation in the area of context-aware recommender systems and big data analytics.
- M. Jiang, P. Cui, R. Liu, Q. Yang, F.Wang,W. Zhu, and S. Yang, "Social contextual recommendation," in CIKM. ACM, 2012, pp. 45--54. Google Scholar
Digital Library
- Y. Huang, B. Cui, J. Jiang, K. Hong, W. Zhang, and Y. Xie, "Real-time video recommendation exploration," in SIGMOD, 2016, pp. 35--46. Google ScholarDigital Library
- R. Kumar, B. K. Verma, and S. S. Rastogi, "Context-aware social popularity based recommender system," IJCA, vol. 92, no. 2, pp. 37--42, April 2014.Google ScholarCross Ref
- X. Yang, H. Steck, and Y. Liu, "Circle-based recommendation in online social networks," in KDD, 2012, pp. 1267--1275. Google ScholarDigital Library
- H. Yin, B. Cui, L. Chen, Z. Hu, and Z. Huang, "A temporal context-aware model for user behavior modeling in social media systems," in SIGMOD, 2014, pp. 1543-- 1554. Google ScholarDigital Library
- A. Akther, K. M. Alam, H. N. Kim, and A. E. Saddik, "Social network and user context assisted personalization for recommender systems," in IIT, 2012, pp. 95--100.Google Scholar
- X. Zhou, L. Chen, Y. Zhang, L. Cao, G. Huang, and C. Wang, "Online video recommendation in sharing community," in SIGMOD. ACM, 2015, pp. 1645-- 1656. Google ScholarDigital Library
- X. Zhou, L. Chen, Y. Zhang, D. Qin, L. Cao, G. Huang, and C. Wang, "Enhancing online video recommendation using social user interactions," VLDB J., vol. 26, no. 5, pp.637--656, 2017. Google ScholarDigital Library
- X. Zhou, D. Qin, L. Chen, and Y. Zhang, "Real-time context-aware social media recommendation," VLDB J., 2018.Google Scholar
- D. Qin, X. Zhou, L. Chen, G. Huang, and Y. Zhang, "Dynamic Connection-based Social Group Recommendation," TKDE, 2018.Google Scholar
- H. Luo, J. Fan, and D. A. Keim, "Personalized news video recommendation," in ACM MM, 2008, pp. 1001--1002. Google ScholarDigital Library
- L. Li, D.Wang, T. Li, D. Knox, and B. Padmanabhan, "SCENE: a scalable two-stage personalized news recommendation system," in SIGIR, 2011, pp. 125--134. Google ScholarDigital Library
- http://storm.apache.org/releases/1.0.0/Concepts.htmlGoogle Scholar
- http://www.ruizhang.info/Google Scholar
- G. Adomavicius, L. Baltrunas, E. William de Luca, T. Hussein, and A. Tuzhilin. 4th Workshop on Context-aware Recommender Systems (CARS 2012). In RecSys'12. 349--350. Google ScholarDigital Library
- G. Adomavicius, L. Baltrunas, T. Hussein, F. Ricci, and A. Tuzhilin. 2011. 3rd Workshop on Context-aware Recommender Systems (CARS 2011). In RecSys '11. 379--380. Google ScholarDigital Library
- G. Adomavicius and F. Ricci. 2009. RecSys'09 workshop 3: workshop on contextaware recommender systems (CARS-2009). In RecSys 2009. 423--424. Google ScholarDigital Library
- G. Adomavicius, A. Tuzhilin, S. Berkovsky, E. W. De Luca, and A. Said. 2010. Context-awareness in Recommender Systems: Research Workshop and Movie Recommendation Challenge. In RecSys '10. 385--386. Google ScholarDigital Library
Index Terms
- The 1st International Workshop on Context-Aware Recommendation Systems with Big Data Analytics (CARS-BDA)
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