A new uncertainty-aware similarity for user-based collaborative filtering

Belmessous, Khadidja ; Sebbak, Faouzi ; Mataoui, M'hamed ; Cherifi, Walid

Kybernetika, Tome 60 (2024) no. 4, pp. 446-474

Abstract (VO)

User-based Collaborative Filtering (UBCF) is a common approach in Recommender Systems (RS). Essentially, UBCF predicts unprovided entries for the target user by selecting similar neighbors. The effectiveness of UBCF greatly depends on the selected similarity measure and the subsequent choice of neighbors. This paper presents a new Uncertainty-Aware Similarity measure "UASim" which enhances CF by accurately calculating how similar, dissimilar, and uncertain users' preferences are. Uncertainty is a key factor of "UASim" that is managed in the neighborhood selection step of CF. Extensive experimental evaluation, conducted on Flixter, Movielens-100K, and Movielens-1M datasets, indicates that "UASim" shows better performance compared to many representative predefined similarity measures. The proposed measure demonstrates enhancements across various performance indicators, namely: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), coverage, and the F-score.

Détail

DOI : 10.14736/kyb-2024-4-0446

Classification : 68P10, 68P20, 68T37
Keywords: collaborative filtering; similarity; subjective logic; uncertainty

Bibliographie

[1] al., Ch. C. Aggarwal et: Recommender systems, volume 1. 2016.

[2] Aherne, F. J., Thacker, N. A., Rockett, P. I.: The bhattacharyya metric as an absolute similarity measure for frequency coded data. Kybernetika 34 (1998), 4, 363-368. | DOI | MR

[3] Ahn, H. J.: A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inform. Sci. 178 (2008), 1, 37-51. | DOI

[4] Al-Bashiri, H., Abdulgabber, M. A., Romli, A., Kahtan, H.: An improved memory-based collaborative filtering method based on the topsis technique. PloS one 13 (2018), 10. e0204434. | DOI

[5] Amer, A. A, Abdalla, H. I., Nguyen, L.: Enhancing recommendation systems performance using highly-effective similarity measures. Knowledge-Based Systems 217 (2021), 106842. | DOI

[6] Anand, P. B., Nath, R.: Content-based recommender systems. In: Recommender System with Machine Learning and Artificial Intelligence: Practical Tools and Applications in Medical, Agricultural and Other Industries. 2020, pp. 165-195.

[7] Ar, Y., Amrahov, Ş. E., Gasilov, N. A., Y.-Sert, S.: A new curve fitting based rating prediction algorithm for recommender systems. Kybernetika 58 (2022), 3, 440-455. | DOI

[8] Belmessous, K., Sebbak, F., Batouche, A., al., et: Co-rating aware evidential user-based collaborative filtering recommender system. In: International Conference on Computing Systems and Applications, Springer 2022, 51-60. | DOI

[9] Chen, M., Liu, P.: Performance evaluation of recommender systems. Int. J. Performability Engrg. 13 (2017), 8, 1246. | DOI

[10] Dewi, R. K., Widodo, A. W., Sari, Y. A., Aziz, N. I. M.: Rank consistency of topsis in mobile based recommendation system. In: Proc. 5th International Conference on Sustainable Information Engineering and Technology, ACM Digital Library 2020, pp. 107-112.

[11] Feng, J., Fengs, X., Zhang, N., Peng, J.: An improved collaborative filtering method based on similarity. PLoS One 13 (2018), 9, e0204003. | DOI

[12] Fkih, Fethi: Similarity measures for collaborative filtering-based recommender systems: Review and experimental comparison. Journal of King Saud University-Computer and Information Sciences, 2021.

[13] Forouzandeh, S., Rostami, M., Berahmand, K.: A hybrid method for recommendation systems based on tourism with an evolutionary algorithm and topsis model. Fuzzy Inform. Engrg. 14 (2022), 1, 26-50. | DOI

[14] Gavalas, D., Konstantopoulos, Ch., Mastakas, K., Pantziou, G.: Mobile recommender systems in tourism. J. Network Computer Appl. 39 (2014), 319-333. | DOI

[15] Gazdar, A., Hidri, L.: A new similarity measure for collaborative filtering based recommender systems. Knowledge-Based Syst. 188 (2020), 105058. | DOI

[16] Guo, G., Zhang, J., Yorke-Smith, N.: A novel bayesian similarity measure for recommender systems. In ACM, editor, Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI), pages 2619-2625, 2013.

[17] Harper, F. M., Konstan, J. A.: The movielens datasets: History and context. ACM Trans. Int. Intell. Systems (TIIS) 5 (2015), 4, 1-19.

[18] Hwang, Ch. L., Yoon, K.: Multiple Attribute Decision Making: Methods and Applications A State-Of-The-Art Survey, volume 186. Springer Science Business Media, 2012. | MR

[19] Idrissi, N., Zellou, A.: A systematic literature review of sparsity issues in recommender systems. Social Network Anal. Mining 10 (2020), 1, 1-23.

[20] Jøsang, A.: A logic for uncertain probabilities. Int. J. Uncertainty, Fuzziness Knowledge-Based Syst. 9 (2001), 3, 279-311. | DOI | MR

[21] Jøsang, A.: Subjective Logic, volume 4. 2016.

[22] Karimi, M., Jannach, D., Jugovac, M.: News recommender systems-survey and roads ahead. Inform. Process. Management 54 (2018), 6, 1203-1227. | DOI

[23] Khojamli, H., Razmara, J.: Survey of similarity functions on neighborhood-based collaborative filtering. Expert Syst. Appl. 185 (2021), 115482. | DOI

[24] Kim, K.: A new similarity measure to increase coverage of rating predictions for collaborative filtering. Appl. Intell. 53 (2023), 23, 28804-28818. | DOI

[25] Chetana, V. L., Seetha, H.: Handling massive sparse data in recommendation systems. J. Inform. Knowledge Management (2024), 2450021. | DOI

[26] Liu, H., Hu, Z., Mian, A., Tian, H., Zhu, X.: A new user similarity model to improve the accuracy of collaborative filtering. Knowledge-Based Syst. 56 (2014), 156-166. | DOI

[27] Manochandar, S., Punniyamoorthy, M.: A new user similarity measure in a new prediction model for collaborative filtering. Appl. Intell. 51 (2021), 1, 586-615. | DOI

[28] Mataoui, M., Sebbak, F., Sidhoum, A. H., Harbi, T. E., Senouci, M. R., Belmessous, K.: A hybrid recommendation system for researchgate academic social network. Social Network Anal. Mining 13 (2023), 1. 53. | DOI

[29] Olson, D. L.: Comparison of weights in topsis models. Math. Comput. Modell. 40 (2004), 7-8, 721-727. | DOI | MR

[30] Papadakis, H., Papagrigoriou, An., Panagiotakis, C., Kosmas, E., Fragopoulou, P.: Collaborative filtering recommender systems taxonomy. Knowledge Inform. Syst. 64 (2022), 1, 35-74. | DOI

[31] Patra, B. K., Launonen, R., Ollikainen, V., Nandi, S.: A new similarity measure using bhattacharyya coefficient for collaborative filtering in sparse data. Knowledge-Based Syst. 82 (2015), 163-177. | DOI

[32] Ricci, F., Rokach, L., Shapira, B.: Context-aware recommender systems: recommender systems handbook. In: Recommender Systems Handbook, Springer, 2011, pp. 217-253.

[33] Roy, D., Dutta, M.: A systematic review and research perspective on recommender systems. J. Big Data 9 (2022), 1, 59. | DOI

[34] Sánchez, P., Bellogín, A.: Building user profiles based on sequences for content and collaborative filtering. Inform. Process. Management 56 (2019), 1, 192-211. | DOI

[35] Seth, R., Sharaff, A.: A comparative overview of hybrid recommender systems: Review, challenges, and prospects. Data Mining Machine Learning Appl. (2022), 57-98. | DOI

[36] Shojaei, M., Saneifar, H.: MFSR: A novel multi-level fuzzy similarity measure for recommender systems. Expert Systems Appl. 177 (2021), 114969. | DOI

[37] Valcarce, D., Parapar, J., Barreiro, Á.: Finding and analysing good neighbourhoods to improve collaborative filtering. Knowledge-Based Syst. 159 (2018), 193-202. | DOI

[38] Wang, D., Yih, Y., Ventresca, M.: Improving neighbor-based collaborative filtering by using a hybrid similarity measurement. Expert Syst. Appl. 160 (2020), 113651. | DOI

[39] Wang, Y., Deng, J., Gao, J., Zhang, P.: A hybrid user similarity model for collaborative filtering. Inform. Sci. 418 (2017), 102-118. | DOI

[40] Wang, Y., Wang, P., Liu, Z., Zhang, L. Y.: A new item similarity based on $\alpha$-divergence for collaborative filtering in sparse data. Expert Syst. Appl. 166 (2021), 114074. | DOI

[41] Wu, X., Cheng, B., Chen, J.: Collaborative filtering service recommendation based on a novel similarity computation method. IEEE Trans. Services Comput. 10 (2015), 3, 352-365. | DOI

[42] Wu, X., Huang, Y., Wang, S.: A new similarity computation method in collaborative filtering-based recommendation system. In: 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), IEEE, 2017, pp. 1-5. | DOI