A new curve fitting based rating prediction algorithm for recommender systems

Ar, Yilmaz ; Emrah Amrahov, Şahin ; Gasilov, Nizami A. ; Yigit-Sert, Sevgi

Kybernetika, Tome 58 (2022) no. 3, pp. 440-455

Abstract (VO)

The most algorithms for Recommender Systems (RSs) are based on a Collaborative Filtering (CF) approach, in particular on the Probabilistic Matrix Factorization (PMF) method. It is known that the PMF method is quite successful for the rating prediction. In this study, we consider the problem of rating prediction in RSs. We propose a new algorithm which is also in the CF framework; however, it is completely different from the PMF-based algorithms. There are studies in the literature that can increase the accuracy of rating prediction by using additional information. However, we seek the answer to the question that if the input data does not contain additional information, how we can increase the accuracy of rating prediction. In the proposed algorithm, we construct a curve (a low-degree polynomial) for each user using the sparse input data and by this curve, we predict the unknown ratings of items. The proposed algorithm is easy to implement. The main advantage of the algorithm is that the running time is polynomial, namely it is $\theta(n^2)$, for sparse matrices. Moreover, in the experiments we get slightly more accurate results compared to the known rating prediction algorithms.

Détail

DOI : 10.14736/kyb-2022-3-0440

Classification : 65D10, 68Q25, 68T01
Keywords: recommender systems; collaborative filtering; curve fitting

Bibliographie

[1] Acilar, A. M., Arslan, A.: A collaborative filtering method based on artificial immune network. Expert Systems with Applications 36 (2009), 8324-8332. | DOI

[2] Alhijawi, B., Awajan, A.: Prediction of movie success using Twitter temporal mining. In: 6th International Congress on Information and Communication Technology, Singapore 2022, pp. 105-116. | DOI

[3] Al-Shamri, M. Y. H.: Power coefficient as a similarity measure for memory-based collaborative recommender systems. Expert Systems Appl. 41 (2014), 5680-5688. | DOI

[4] Ar, Y.: An initialization method for the latent vectors in probabilistic matrix factorization for sparse datasets. Evolution. Intell. 13 (2020), 269-281. | DOI

[5] Ar, Y.: A genetic algorithm solution to the collaborative filtering problem. Expert Systems Appl. 41 (2016), 122-128. | DOI

[6] Bobadilla, J., Ortega, F., Hernando, A., Bernal, J.: Generalization of recommender systems: Collaborative filtering extended to groups of users and restricted to groups of items. Expert Systems Appl. 39 (2012), 172-186. | DOI

[7] Bokde, D., Girase, S., Mukhopadhyay, D.: Matrix factorization model in collaborative filtering algorithms: A survey. Procedia Computer Sci. 49 (2015), 136-146. | DOI

[8] Chen, J., Zhao, C., Chen, L.: Collaborative filtering recommendation algorithm based on user correlation and evolutionary clustering. Complex Intell. Systems 6 (2020), 147-156. | DOI

[9] Christakopoulou, E., Karypis, G.: HOSLIM: Higher-order sparse linear method for top-N recommender systems. In: Advances in Knowledge Discovery and Data Mining, Taiwan 2014, pp. 38-49. | DOI

[10] Cornelis, C., Lu, J., Guo, X., Zhang, G.: One-and-only item recommendation with fuzzy logic techniques. Inform. Sci. 177 (2007), 4906-4921. | DOI

[11] Maio, C. De, Fenza, G., Gaeta, M., Loia, V., Orciuoli, F., Senatore, S.: RSS-based e-learning recommendations exploiting fuzzy FCA for knowledge modeling. Applied Soft Computing 12 (2012), 1, 113-124. | DOI

[12] Meo, P. De, Ferrara, E., Fiumara, G., Provetti, A.: Improving recommendation quality by merging collaborative filtering and social relationships. In: 11th International Conference on Intelligent Systems Design and Applications 2011, pp. 587-592. | DOI

[13] Demir, G. N., Uyar, A. S., Ögüdücü, S. G.: Graph-based sequence clustering through multiobjective evolutionary algorithms for web recommender systems. In: 9th Annual Conference on Genetic and Evolutionary Computation (GECCO'07), London 2007, pp. 1943-1950. | DOI

[14] Devi, M. K., Venkatesh, P.: Smoothing approach to alleviate the meager rating problem in collaborative recommender systems. Future Generation Computer Systems 29 (2013), 262-270. | DOI

[15] Eirinaki, M., Gao, J., Varlamis, I., Tserpes, K.: Recommender systems for large-scale social networks: A review of challenges and solutions. Future Generation Computer Systems 78 (2018), 413-418. | DOI

[16] Göksedef, M., Gündüz-Öğüdücü, Ş.: Combination of web page recommender systems. Expert Systems Appl. 37 (2010), 2911-2922. | DOI

[17] Golbeck, J.: Trust and nuanced profile similarity in online social networks. ACM Trans. Web 3 (2009), 12:1-12:33. | DOI

[18] Hasanzadeh, S., Fakhrahmad, S. M., Taheri, M.: Review based recommender systems: A proposed rating prediction scheme using word embedding representation of reviews. The Computer J. 65 (2022), 2, 345-354. | DOI

[19] Hofmann, T.: Probabilistic latent semantic indexing. In: 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR'99), pp. 50-57. | DOI

[20] Kaur, H., Kumar, N., Batra, S.: An efficient multi-party scheme for privacy preserving collaborative filtering for healthcare recommender system. Future Generation Computer Systems 86 (2018), 297-307. | DOI

[21] Kilani, Y., Otoom, A. F., Alsarhan, A., Almaayah, M.: A genetic algorithms-based hybrid recommender system of matrix factorization and neighborhood-based techniques. J. Comput. Sci. 28 (2018), 78-93. | DOI

[22] Koren, Y., Bell, R.: Advances in collaborative filtering. In: Recommender Systems Handbook 2011, pp. 77-118. | DOI

[23] Koren, Y., R, Bell, Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42 (2009), 30-37. | DOI

[24] Leskovec, J.: New directions in recommender systems. In: 8th ACM International Conference on Web Search and Data Mining 2015, pp. 3-4. | DOI

[25] Li, Q., Kim, B. M.: Constructing user profiles for collaborative recommender system. Advanced Web Technol. Appl. Lect. Notes Computer Sci. 3007 (2004), 100-110. | DOI

[26] Liu, F., Lee, H. J.: Use of social network information to enhance collaborative filtering performance. Expert Systems Appl. 37 (2010), 4772-4778. | DOI

[27] Liu, J., Wu, C., Liu, W.: Bayesian probabilistic matrix factorization with social relations and item contents for recommendation. Decision Support Systems 55 (2013), 838-850. | DOI

[28] Najafi, S., Salam, Z.: Evaluating prediction accuracy for collaborative filtering algorithms in recommender systems.

[29] Nilashi, M., Ibrahim, O., Bagherifard, K.: A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Systems Appl. 92 (2018), 507-520. | DOI

[30] Qian, Y., Zhang, Y., Ma, X., Yu, H., Peng, L.: EARS: Emotion-aware recommender system based on hybrid information fusion. Inform. Fusion 46 (2019), 141-146. | DOI

[31] Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: An open architecture for collaborative filtering of netnews. In: 1994 ACM Conference on Computer Supported Cooperative Work (CSCW'94), pp. 175-186. | DOI

[32] Resnick, P., Varian, H. R.: Recommender systems. Commun. ACM 40 (1997), 56-58. | DOI

[33] Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: 10th International Conference on World Wide Web (WWW'01), Hong Kong 2001, pp. 285-295. | DOI

[34] Sert, S. Y., Ar, Y., Bostancı, G. E.: Evolutionary approaches for weight optimization in collaborative filtering-based recommender systems. Turkish J. Electr. Engrg. Comput. Sci. 27 (2019), 3, 2121-2136. | DOI

[35] Singh, P. K., Sinha, S., Choudhury, P.: An improved item-based collaborative filtering using a modified Bhattacharyya coefficient and user–user similarity as weight. Knowledge Inform. Systems 64 (2022), 665-701. | DOI

[36] Tarus, J. K., Niu, Z., Yousif, A.: A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Future Generation Computer Systems 72 (2017), 37-48. | DOI

[37] Tu, Z., Li, W.: Multi-agent solver for non-negative matrix factorization based on optimization. Kybernetika 57 (2021), 60-77. | DOI | MR

[38] Victor, P., Cornelis, C., Cock, M. D., Silva, P. P. da: Gradual trust and distrust in recommender systems. Fuzzy Sets Systems 160 (2009), 1367-1382. | DOI | MR

[39] Yu, W., S.Li: Recommender systems based on multiple social networks correlation. Future Generation Computer Systems 87 (2018), 312-327. | DOI

[40] Zhang, Q., Lu, J., Jin, Y.: Artificial intelligence in recommender systems. Complex Intell. Systems 7 (2021), 1, 439-457. | DOI

[41] Zhu, J., He, Y., Zhao, G., Bo, X., Qian, X.: Joint reason generation and rating prediction for explainable recommendation. IEEE Trans. Knowledge Data Engrg. (2022). | DOI