Abstract
In recent years, the context-aware recommendation algorithm has become the main research direction in the field of recommendation systems. It becomes the main task of the context-aware recommendation system to use the context information to further improve the recommendation accuracy and user satisfaction. This paper studies and analyzes the context-aware recommendation system by context extraction and modeling. The key step is how to extract user preferences. At the same time, this article introduces the relevant context recommendation generation techniques. Finally, the full text is summarized and future work is proposed.
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References
Adomavivius G, Tuzhilin A (2005) Personalization technlogies: a process -oriented perspective. Commun ACM 48(10):83–90
Chen L, Li G et al (2015) Perceptual recommendation algorithm based on context extraction. Comput Sci 42(10):90–95
He M, Liu Y et al (2017) Collaborative filtering recommendation based on context item score splitting. Comput Sci 44(3):247–253. (in Chinese)
Liu Q, Wu S, Wang L (2015) COT: contextual operating tensor for context-aware recommender systems. Association for the Advancement of Articial Intelligence, pp 203–209 (2015). (in Chinese)
Karatzoglou A, Amatriain X, Baltrunas L et al (2010) Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In: Proceedings of the fourth ACM conference on recommender systems, pp 79–86
Koren Y, Bell R (2011) Advances in collaborative filtering. In: Recommender Systems Handbook. Springer, pp 145–186
Goldberg D, Nichols D, Oki BM et al (1992) Using collaboration filtering to weave an information tapestry. Commun ACM 35(12):61–70
Burke R (2002) Hybrid recommender systems: survey and experiments. User Model User-Adap Inter 12(4):331–370
Adomavicius G, Sankaranarayanan R et al (2005) Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans Inf Syst (TOIS) 23(1):103–145
Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng (TKDE) 17(6):734–749
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Ren, Y., Chi, C., Zhang, J. (2020). A Review of Context-Based Personalized Recommendation Research. In: Xu, Z., Choo, KK., Dehghantanha, A., Parizi, R., Hammoudeh, M. (eds) Cyber Security Intelligence and Analytics. CSIA 2019. Advances in Intelligent Systems and Computing, vol 928. Springer, Cham. https://doi.org/10.1007/978-3-030-15235-2_163
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DOI: https://doi.org/10.1007/978-3-030-15235-2_163
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