Abstract
The recommendation systems have been investigating and applying in a vast of fields. The core of systems is the similarity measures and the dissimilarity measures. Many scientists have proposed various similarity measurements in different aspects, including the measures between the users and the users, the measures between the items and the items, the measures between users with the items. However, there are not much studies on the effects of statistical implicative in the recommendation system with subjective tendency. We mainly focus on showing the effects of the subjective tendency against the recommendation system’s model through the prism of statistics implicative. Three specific approaches, including Independence, Dependence, and Equilibrium combined with the fifteen measures of the statistical bias are considered in our work. The experimental results evaluated on the Jester5k dataset compare the similarity measures and the interestingness measures based on the subjective tendency in recommendation systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
https://rdrr.io/cran/recommenderlab/man/Jester5k.html, accessed on February 01, 2021.
- 2.
- 3.
- 4.
- 5.
- 6.
References
Felfernig, A., Jeran, M., Ninaus, G., Reinfrank, F., Reiterer, S., Stettinger, M.: Basic approaches in recommendation systems. In: Robillard, M.P., Maalej, W., Walker, R.J., Zimmermann, T. (eds.) Recommendation Systems in Software Engineering, pp. 15–37. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-45135-5_2
Aggarwal, C.C.: Recommender Systems. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-29659-3
Ricci, F., Rokach, L., Shapira, B. (eds.): Recommender Systems Handbook. Springer, New York (2015). https://doi.org/10.1007/978-1-4899-7637-6
Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)
Saqlain, M., Riaz, M., Saleem, M.A., Yang, M.: Distance and similarity measures for neutrosophic hypersoft set (NHSS) with construction of NHSS-TOPSIS and applications. IEEE Access 9, 30803–30816 (2021). https://doi.org/10.1109/ACCESS.2021.3059712
Yan, H., Tang, Y.: Collaborative filtering based on Gaussian mixture model and improved Jaccard similarity. IEEE Access 7, 118690–118701 (2019)
Huynh, H.X., et al.: Context-similarity collaborative filtering recommendation. IEEE Access 8, 33342–33351 (2020)
Mpela, M.D., Zuva, T.: A mobile proximity job employment recommender system. In: 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), pp. 1–6 (2020)
Phan, L.P., Huynh, H.H., Huynh, H.X.: Hybrid recommendation based on implicative rating measures. In: International Conference on Machine Learning and Soft Computing, ICMLSC 2018, New York, NY, USA, pp. 50–56. Association for Computing Machinery (2018). https://doi.org/10.1145/3184066.3184076
Chirico, R., et al.: Guidelines for reporting of phase equilibrium measurements (IUPAC recommendations 2012). Pure Appl. Chem. 84, 1785–1813 (2012)
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992). https://doi.org/10.1145/138859.138867
Huynh, H.X., Phan, N.Q., Duong-Trung, N., Nguyen, H.T.T.: Collaborative filtering recommendation based on statistical implicative analysis. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds.) ICCCI 2020. CCIS, vol. 1287, pp. 224–235. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63119-2_19
Banda, L., et al.: Recommender systems using collaborative tagging. Int. J. Data Wareh. Min. 16(3), 183–200 (2020)
Nguyen, H.T., Huynh, H.H., Phan, L.P., Huynh, H.X.: Improved collaborative filtering recommendations using quantitative implication rules mining in implication field. In: Proceedings of the 3rd International Conference on Machine Learning and Soft Computing, ICMLSC 2019, New York, NY, USA, pp. 110–116. Association for Computing Machinery (2019). https://doi.org/10.1145/3310986.3310996
Huynh, H.X., Cu, G.N., Huynh, T.M., Luong, H.H., et al.: Recommender systems based on resonance relationship of criteria with Choquet operation. Int. J. Data Wareh. Min. (IJDWM) 16(4), 44–62 (2020)
Berkani, L., Betit, L., Belarif, L.: A multi-view clustering approach for the recommendation of items in social networking context. In: Senouci, M.R., Boudaren, M.E.Y., Sebbak, F., Mataoui, M. (eds.) CSA 2020. LNNS, vol. 199, pp. 241–251. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-69418-0_22
Nilashi, M., Bagherifard, K., Ibrahim, O., Alizadeh, H., Nojeem, L., Roozegar, N.: Collaborative filtering recommender systems. Res. J. Appl. Sci. Eng. Technol. 5, 4168–4182 (2013)
Osadchiy, T., Poliakov, I., Olivier, P., Rowland, M., Foster, E.: Recommender system based on pairwise association rules. Expert Syst. Appl. 115, 535–542 (2019). https://www.sciencedirect.com/science/article/pii/S095741741830441X
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Application of dimensionality reduction in recommender system - a case study (2000)
Amatriain, X., Jaimes, A., Oliver, N., Pujol, J.M.: Data mining methods for recommender systems. In: Ricci, F., Rokach, L., Shapira, B., Kantor, P. (eds.) Recommender Systems Handbook, pp. 39–71. Springer, Boston (2011). https://doi.org/10.1007/978-0-387-85820-3_2
Gras, R., Kuntz, P.: An overview of the statistical implicative analysis (SIA) development. In: Gras, R., Suzuki, E., Guillet, F., Spagnolo, F. (eds.) Statistical Implicative Analysis. Studies in Computational Intelligence, vol. 127, pp. 11–40. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-78983-3_1
Nguyen, H.T., Phan, L.P., Huynh, H.H., Huynh, H.X.: Recommendation with quantitative implication rules. EAI Endorsed Trans. Context-Aware Syst. Appl. 6(16), e2 (2019)
Hills, J., Davis, L.M., Bagnall, A.: Interestingness measures for fixed consequent rules. In: Yin, H., Costa, J.A.F., Barreto, G. (eds.) IDEAL 2012. LNCS, vol. 7435, pp. 68–75. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32639-4_9
Phan, L.P., Phan, N.Q., Phan, V.C., Huynh, H.H., Huynh, H.X., Guillet, F.: Classification of objective interestingness measures. EAI Endorsed Trans. Context-Aware Syst. Appl. 3(10), e4 (2016)
Hills, J., Davis, L.M., Bagnall, A.: Preprint: Interestingness measures for fixed consequent rules (2012)
Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of recommendation algorithms for e-commerce. In: Proceedings of the 2nd ACM Conference on Electronic Commerce, New York, NY, USA, EC 2000. pp. 158–167. Association for Computing Machinery (2000). https://doi.org/10.1145/352871.352887
Mild, A., Reutterer, T.: Collaborative filtering methods for binary market basket data analysis. In: Liu, J., Yuen, P.C., Li, C., Ng, J., Ishida, T. (eds.) AMT 2001. LNCS, vol. 2252, pp. 302–313. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45336-9_35
Schafer, J.B., Frankowski, D., Herlocker, J., Sen, S.: Collaborative filtering recommender systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 291–324. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72079-9_9
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Huynh, H.X., Phan, C.A., Tran, T.C.T., Nguyen, H.T. (2021). Recommendation with Subjective Tendency Based on Statistical Implicative Analysis. In: Cong Vinh, P., Rakib, A. (eds) Context-Aware Systems and Applications. ICCASA 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 409. Springer, Cham. https://doi.org/10.1007/978-3-030-93179-7_22
Download citation
DOI: https://doi.org/10.1007/978-3-030-93179-7_22
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-93178-0
Online ISBN: 978-3-030-93179-7
eBook Packages: Computer ScienceComputer Science (R0)