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Research on multi-context aware recommendation methods based on tensor factorization

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Abstract

Compared to the traditional recommender systems, context-aware recommender systems are more in line with actual application contexts. However, the existing researches are mostly focused on single context-aware recommendation, such as time-aware recommendation or location-aware recommendation, and lack of in-depth research on multi-context-aware recommendation. Therefore, we proposed a recommendation method of high-order tensor factorization based on multi-context-aware. First, on the basis of analyzing the influence of context on users’ interest preferences, the sensitivity of users to multiple contexts was detected using statistical methods. For context-sensitive users, four-dimensional tensors and feature matrices used to solve data sparsity were constructed based on rating matrix and situational information. And then the stochastic gradient descent algorithm was used for iterative calculation to fill in missing data values and carry out parameter optimization. For context-insensitive users, we used matrix factorization to predict users’ interest preferences. Finally, we tested and validated our method on a multi-context-aware movie dataset, and the experimental results show that the proposed method could effectively reduce the prediction error and improve the recommendation quality.

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References

  1. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions[J]. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005). https://doi.org/10.1109/TKDE.2005.99

    Article  Google Scholar 

  2. Guo, Y., Ling, Y., Chen, H.: A Time-Aware Graph Neural Network for Session-Based Recommendation[C], IEEE Access, 8, 167371–16738 (2020). https://doi.org/10.1109/ACCESS.2020.3023685

  3. Xian, J., Qin, Z., Sun, L.: An optimization of collaborative filtering personalized recommendation algorithm based on time context information. Springer International Publishing (2015). https://doi.org/10.1007/978-3-319-16274-4-15

  4. Yin, H., Cui, B., Sun, Y., et al.: LCARS: a spatial item recommender system. Acm Trans. Inf. Syst. 32(3), 11–11 (2014). https://doi.org/10.1145/2629461

    Article  Google Scholar 

  5. Wang, F., Li, D., Xu, M.: A location-aware TV show recommendation with localized sementaic analysis. Multimed. Syst. 22(4), 535–542 (2016). https://doi.org/10.1007/s00530-015-0451-z

    Article  Google Scholar 

  6. Lu, J., Indeche, M.A.: Multi-context-aware location recommendation using Tensor decomposition. IEEE Access (2020). https://doi.org/10.1109/ACCESS.2020.2983555

    Article  Google Scholar 

  7. Schilit, B.N., Theimer, M.M.: Disseminating active map information to mobile hosts. IEEE Netw. 8(5), 22–32 (1994). https://doi.org/10.1109/65.313011

    Article  Google Scholar 

  8. Brown, P.J., Bovey, J.D., Chen, X.: Context-aware applications: from the laboratory to the marketplace. IEEE Pers. Commun. 4(5), 58–64 (1997). https://doi.org/10.1109/98.626984

    Article  Google Scholar 

  9. Dey, AK.: Context-aware computing: the cyber desk project. AAAI Press, (1998)

  10. Abowd, G.D., Dey, A.K., Brown, P.J., et al.: Towards a better understanding of context and context-awareness. Springer, Berlin Heidelberg (1999)

    Book  Google Scholar 

  11. Liu, N.N., Cao, B., Zhao, M., et al.: Adapting neighborhood and matrix factorization models for context aware recommendation[M]. ACM, (2010)

  12. Shi, Y., Hanjalic, A., Larson, M.: Mining mood-specific movie similarity with matrix factorization for context-aware recommendation[J]. In Proceedings of the Workshop on Context-Aware Movie Recommendation (CAMRa'10). ACM, New York, NY, 34–40 (2010)

  13. Baltrunas, L., Ludwig, B., Ricci, F.: Matrix factorization techniques for context aware recommendation, in: Proceedings of the Fifth ACM conference on recommender systems, ACM,pp. 301–304 (2011)

  14. Yong, Z., Mobasher, B., Burke, R.: CSLIM: contextual SLIM recommendation algorithms[C]// ACM, conference on recommender systems. ACM, (2014)

  15. Kim, D., Park, C., Oh, J., Lee, S., Yu, H.: Convolutional matrix factorization for document context-aware recommendation, in: Proceedings of the 10th ACM conference on recommender systems, ACM, pp. 233–240 (2016)

  16. Kolda, T.: Tensor decompositions and applications. Siam Rev. (2009). https://doi.org/10.1137/07070111X

    Article  MathSciNet  MATH  Google Scholar 

  17. Hong, M., Jung, J.J.: Multi-Sided recommendation based on social tensor factorization. Inf. Sci. (2018). https://doi.org/10.1016/j.ins.2018.03.019

    Article  Google Scholar 

  18. CAI, Y., ZHANG, M., LUO, D., et al.: Low-order tensor decompositions for social tagging recommendation[C]//The forth ACM international conference on web search and web data mining. ACM, 695–704. (2011) https://doi.org/10.1145/1935826.1935920

  19. Luan, W., Liu, G., Jiang, C.: Collaborative tensor factorization and its application in POI recommendation, 2016 IEEE 13th international conference on networking, sensing, and control (ICNSC), pp. 1–6 (2016)

  20. Chen, H., Li, J.: Neural Tensor model for learning multi-aspect factors in recommender systems[C]// International joint conference on artificial intelligence. International joint conferences on artificial intelligence organization, (2020)

  21. Chen, H., Li, J.: Adversarial tensor factorization for context-aware recommendation[C]// The 13th ACM conference. ACM, (2019)

  22. Xian, W., Shi, B., Dong, Y., et al.: Neural Tensor factorization for temporal interaction learning[C]// The twelfth ACM international conference. ACM, (2019)

  23. Chen, SL., Zhang, BF., et al.: Valid context detection based on context filter in context-aware recommendation system, (2018)

  24. Kovsir, A., Odi’C, A., Kunaver, M., et al.: Database for contextual personalization. Electrotech. Rev. 78(5), 270–274 (2011)

    Google Scholar 

  25. Paterek, A.: Improving regularized singular value decomposition for collaborative filtering, pp. 39–42. ACM Press, Proc. KDD Cup and Workshop (2007)

  26. Xu, Y., Hao, R., Yin, W., et al.: Parallel matrix factorization for low-rank tensor completion. Inverse Probl. Imaging 9(2), 601–624 (2015). https://doi.org/10.1007/s11464-012-0194-5

    Article  MathSciNet  MATH  Google Scholar 

  27. Symeonidis, P.: ClustHOSVD: item recommendation by combining semantically enhanced Tag clustering with Tensor HOSVD. IEEE Trans. Syst. Man Cybern. Syst. 46(9), 1240–1251 (2016). https://doi.org/10.1109/TSMC.2015.2482458

    Article  Google Scholar 

  28. Lin, C.J.: On the convergence of multiplicative update algorithms for nonnegative matrix factorization. IEEE Trans. Neural Netw. 18(6), 1589–1596 (2007). https://doi.org/10.1109/TNN.2007.895831

    Article  Google Scholar 

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Acknowledgements

The work was supported by grants from the Nature Science Foundation of Anhui Province in China, No.2008085MF193, the Outstanding Young Talents Program of Anhui Province in China, No.gxyqZD2018060, the Major Science and Technology Project of Anhui Province, No.201903a06020006, the Provincial Quality Project of the Anhui Province Education Department in China, No.2019jyxm0285. And we thank all the anonymous reviewers for their hard work and valuable comments.

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Authors and Affiliations

  1. The University Key Laboratory of Intelligent Perception and Computing of Anhui Province, Anqing Normal University, Anqing, 246000, Anhui, China

    Shulin Cheng & Wanyan Wang

  2. School of Computer and Information, Anqing Normal University, Anqing, 246000, Anhui, China

    Huimin Jiang

  3. Anqing Data Resources Management Bureau, Anqing, 246000, Anhui, China

    Wei Jiang

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  1. Shulin Cheng
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  2. Huimin Jiang
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  4. Wei Jiang
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Correspondence to Shulin Cheng or Wei Jiang.

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Cheng, S., Jiang, H., Wang, W. et al. Research on multi-context aware recommendation methods based on tensor factorization. Multimedia Systems 29, 2253–2262 (2023). https://doi.org/10.1007/s00530-023-01103-z

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