Cited By
View all- Cheng SJiang HWang WJiang W(2023)Research on multi-context aware recommendation methods based on tensor factorizationMultimedia Systems10.1007/s00530-023-01103-z29:4(2253-2262)Online publication date: 15-May-2023
Valid context detection based on context filter in context-aware recommendation system
Pages 149 - 153
Abstract
Context as a kind of quite important information plays a significant role in context-aware recommendation system (CARS). Many studies have been proved that context help promote to improve the effectiveness of recommendations. But a serious challenge has yet not been solved well, which is how to detect valid contexts for users in CARS, since different users have different sensitivity to contexts. Motivated by the observations, we proposed a method of valid context detection based on context filter. Context filter comprises two selection phases. In the first phase, context selection depends on the expert experiences, which is also called primary selection. We focus on the second selection phase named refinement selection based on one-way analysis of variance (ANOVA). By one-way ANOVA, a utility function is put forward to measure user's context sensitivity to detect valid contexts. We verified the effectiveness of detection method by the experiments on a small real film dataset.
References
[1]
Adomavicius, Gediminas, and Alexander Tuzhilin. 2005. "Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions." IEEE transactions on knowledge and data engineering 17(6): 734--749.
[2]
Abowd, G. D., Dey, A. K., Brown, P. J., Davies, N., Smith, M., & Steggles, P. 1999. Towards a better understanding of context and context-awareness. In International Symposium on Handheld and Ubiquitous Computing (pp. 304--307). Springer, Berlin, Heidelberg.
[3]
Dey, A. K. 2001. Understanding and using context. Personal and ubiquitous computing, 5(1), 4--7.
[4]
Huang, M., Zhang, B., Zou, G., Gu, C., Wu, P., Cheng, S., & Zhang, Z. 2016. Socialized User Modeling in Microblogging Scenarios for Interest Prediction. In Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing, 2nd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech), 2016 IEEE 14th Intl C, pp. 124--131.
[5]
Deng, S., Wang, D., Li, X., & Xu, G. 2015. Exploring user emotion in microblogs for music recommendation. Expert Systems with Applications, 42(23), 9284--9293.
[6]
Panniello, U., & Gorgoglione, M. 2011. A contextual modeling approach to context-aware recommender systems. In Proceedings of the 3rd Workshop on Context-Aware Recommender Systems.
[7]
Cheng, S., & Liu, Y. 2015. Time-Aware and Grey Incidence Theory Based User Interest Modeling for Document Recommendation. Cybernetics and Information Technologies, 15(2), 36--52.
[8]
Yin, H., Cui, B., Chen, L., Hu, Z., & Huang, Z. 2014. A temporal context-aware model for user behavior modeling in social media systems. In Proceedings of the 2014 ACM SIGMOD international conference on Management of data, pp. 1543--1554.
[9]
Yin, H., Cui, B., Chen, L., Hu, Z., & Zhou, X. 2015. Dynamic user modeling in social media systems. ACM Transactions on Information Systems (TOIS), 33(3), 10.
[10]
Ding, Y., & Li, X. 2005. Time weight collaborative filtering. In Proceedings of the 14th ACM international conference on Information and knowledge management, pp. 485--492.
Index Terms
- Valid context detection based on context filter in context-aware recommendation system
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July 2018
174 pages
ISBN:9781450365215
DOI:10.1145/3239283
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Published: 20 July 2018
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DSIT2018
DSIT2018: 2018 International Conference on Data Science and Information Technology
July 20 - 22, 2018
Singapore, Singapore
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DSIT '18 Paper Acceptance Rate 31 of 85 submissions, 36%;
Overall Acceptance Rate 114 of 277 submissions, 41%
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View all- Cheng SJiang HWang WJiang W(2023)Research on multi-context aware recommendation methods based on tensor factorizationMultimedia Systems10.1007/s00530-023-01103-z29:4(2253-2262)Online publication date: 15-May-2023
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