A Novel Method for Improving Cold Start Challenge in Recommender Systems through Users Demographics Information

Subject Areas : International Journal of Mathematical Modelling & Computations

Taravat Abedini ¹ , Alireza Hedayati ² , Ali Harounabadi ³

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Received: 2022-07-05 Accepted : 2022-12-20 Published : 2022-12-01

Keywords: Clustering, Recommender Systems, user cold start, hybrid similarity measure, asymmetric Pearson,

Abstract :

The user cold start challenge, occurs when a user joins the system which used recommender systems, for the first time. Since the recommender system has no knowledge of the user preferences at first, it will be difficult to make appropriate recommendations. In this paper, users’ demographics information are used for clustering to find the users with similar preferences in order to improve the cold start challenge by employing the kmeans, k-medoids, and k-prototypes algorithms. The target user’s neighbors are determined by using a hybrid similarity measure including a combination of users’ demographics information similarity and users rating similarity. The asymmetric Pearson correlation coefficient utilized to calculate the user rating similarity, whereas GMR (i.e., global most rated) and GUC(i.e., global user local clustering) strategies are adopted to make recommendations. The proposed method was implemented on MovieLens dataset. The results of this research shows that the MAE of the proposed method has improved the accuracy of the proposals up to about 26% compared to the GMR method and up to about 34% compared to the GUC method. Also, the results show about 60% improvement in terms of rating coverage compared to the GMR and GUC methods.

References:

[1] C. C. Aggarwal and C. K. Reddy, Algorithms and applications, Chapman&Hall/CRC Data Mining
and Knowledge Discovery Series, CRC Press, Londra, (2014).
[2] M. Ahmed, M. T. Imtiaz and R. Khan, Movie recommendation system using clustering and pattern recognition network, 2018 IEEE 8th Annual Computing and Communication Workshop and
Conference (CCWC), Las Vegas, NV, USA, (2018) 143–147.
[3] R. Ahuja, A. Solanki and A. Nayyar, Movie recommender system using K-means clustering and
K-nearest neighbor, 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, (2019) 263–268.
[4] N. F. AL-Bakri and S. Hassan, A Proposed Model to Solve Cold Start Problem using Fuzzy UserBased Clustering, 2019 2nd Scientific Conference of Computer Sciences (SCCS), Baghdad, Iraq,
(2019) 121–125.
[5] M. Y. H. Al-Shamri, User profiling approaches for demographic recommender systems, KnowledgeBased Systems, 100 (2016) 175–187.
[6] M. Ayub, M. A. Ghazanfar, Z. Mehmood, K. H. Alyoubi and A. S. Alfakeeh, Unifying user similarity
and social trust to generate powerful recommendations for smart cities using collaborating filteringbased recommender systems, Soft Computing, 24 (15) (2020) 11071–11094.
[7] R. Chen, Q. Hua, Y. S. Chang, B. Wang, L. Zhang and X. Kong, A survey of collaborative filteringbased recommender systems: From traditional methods to hybrid methods based on social networks,
IEEE Access, 6 (2018) 64301–64320.
[8] J. Chen, C. Zhao and L. Chen, Collaborative filtering recommendation algorithm based on user
correlation and evolutionary clustering, Complex & Intelligent Systems, 6 (1) (2020) 147–156.
[9] L. C. de Carvalho, F. Rodrigues and P. Oliveira, A hybrid recommendation algorithm to address the
cold start problem, International Conference on Hybrid Intelligent Systems, Cham, (2018) 260–271.
[10] S. B. U. Duja, B. Niu, B. Ahmed, M. U. F. Alvi, M. Amjad, U. Ali, Z. U. Rehman and W. Hussain, A
proposed method to solve cold start problem using fuzzy user-based clustering, International Journal
of Advanced Computer Science and Applications, 11 (2) (2020) 529–536.
[11] L. Feng, Q. Zhao and C. Zhou, Improving performances of Top-N recommendations with co-clustering
method, Expert Systems with Applications, 143 (2020) 113078.
[12] X. Guo, S. C. Yin, Y. W. Zhang, W. Li and Q. He, Cold start recommendation based on attributefused singular value decomposition, IEEE Access, 7 (2019) 11349–11359.
[13] G. Guo, J. Zhang and D. Thalmann, Merging trust in collaborative filtering to alleviate data sparsity
and cold start, Knowledge-Based Systems, 57 (2014) 57–68.
[14] J. Han, J. Pei, M. Kamber, Data Mining: Concepts and Techniques, The Morgan Kaufmann Series
in Data Management Systems, Elsevier, (2011).
[15] J. Herce-Zelaya, C. Porcel, J. Bernab-Moreno, A. Tejeda-Lorente and E. Herrera-Viedma, New technique to alleviate the cold start problem in recommender systems using information from social
media and random decision forests, Information Sciences, 536 (2020) 156–170.
[16] S. Jain, A. Grover, P. S. Thakur and S. K. Choudhary, Trends, problems and solutions of recommender system, InNoida, India, (2015) 955–958.
[17] S. Khusro, Z. Ali and I. Ullah, Recommender systems: issues, challenges, and research opportunities,
In: K. Kim and N. Joukov (eds), Information Science and Applications (ICISA), Lecture Notes in
Electrical Engineering, Springer, Singapore, 376 (2016) 1179–1189.
[18] Z. Li and L. Zhang, Subspace ensemble-based neighbor user searching for neighborhood-based collaborative filtering, In: G. Li, J. Yang, J. Gama, J. Natwichai and Y. Tong (eds), Database Systems
for Advanced Applications, Lecture Notes in Computer Science, Springer, Cham, 11447 (2019)
449–463.
[19] B. Lika, K. Kolomvatsos and S. Hadjiefthymiades, Facing the cold start problem in recommender
systems, Expert Systems with Applications, 41 (4) (2014) 2065–2073.
[20] V. Maihami, D. Zand and K. Naderi, Proposing a novel method for improving the performance of
collaborative filtering systems regarding the priority of similar users, Physica A: Statistical Mechanics
and its Applications, 536 (2019) 121021.
[21] M. H. Mohamed, M. H. Khafagy and M. H. Ibrahim, Recommender systems challenges and solutions
survey, International Conference on Innovative Trends in Computer Engineering (ITCE), (2019) 149–
155.
[22] A. Nair and R. Mathews, Challenges and solutions in recommender systems, International Conference
on Computer Networks, Big data and IoT, Springer, Cham, (2019) 188–194.
[23] S. Natarajan,S. Vairavasundaram, S. Natarajan and A. H. Gandomi, Resolving data sparsity and
cold start problem in collaborative filtering recommender system using linked open data, Expert
Systems with Applications, 149 (2020) 113248.
[24] J. Niu, L. Wang, X. Liu and S. Yu , FUIR: Fusing user and item information to deal with data
sparsity by using side information in recommendation systems, Journal of Network and Computer
Applications, 70 (2016) 41–50.
[25] B. Patel, P. Desai and U. Panchal, 2017 International Conference on Innovations in Information,
Embedded and Communication Systems (ICIIECS), Coimbatore, India, (2017) 1–4.
[26] P. Pirasteh, D. Hwang and J. J. Jung, Exploiting matrix factorization to asymmetric user similarities
in recommendation systems, Knowledge-Based Systems, 83 (2015) 51–57.
[27] P. Pirasteh, J. J. Jung and D. Hwang, An asymmetric weighting schema for collaborative filtering, In:
D. Camacho, S. W. Kim and B. Trawiski (eds), New Trends in Computational Collective Intelligence,
Studies in Computational Intelligence, Springer, Cham, 572 (2015) 77–82.
[28] S. K. Raghuwanshi and R. K. Pateriya, Recommendation systems: techniques, challenges, application, and evaluation, In: J. Bansal, K. Das, A. Nagar, K. Deep and A. Ojha (eds), Soft Computing for
Problem Solving, Advances in Intelligent Systems and Computing, Springer, Singapore, 817 (2019)
151–164.
[29] F. Ricci, L. Rokach and B. Shapira, Recommender systems: introduction and challenges, In: F. Ricci,
L. Rokach and B. Shapira (eds), Recommender Systems Handbook, Springer, Boston, MA, (2015)
1–34.
[30] K. V. Rodpysh, S. J. Mirabedini and T. Banirostam, Employing singular value decomposition and
similarity criteria for alleviating cold start and sparse data in context-aware recommender systems,
Electronic Commerce Research, (2021), doi:10.1007/s10660-021-09488-7.
[31] K. V. Rodpysh, S. J. Mirabedini and T. Banirostam, Resolving cold start and sparse data challenge
in recommender systems using multi-level singular value decomposition, Computers & Electrical
Engineering, 94 (2021) 107361.
[32] L. Safoury and A. Salah, Exploiting user demographic attributes for solving cold-start problem in
recommender system, Lecture Notes on Software Engineering, 1 (3) (2013) 303–307.
[33] A. Sattar, M. Ghazanfar and M. Iqbal, Building accurate and practical recommender system algo-
rithms using machine learning classifier and collaborative filtering, Arabian Journal for Science &
Engineering, 42 (8) (2017) 3229–3247.
[34] R. Sharma, D. Gopalani and Y. Meena, Collaborative filtering-based recommender system: Approaches and research challenges, 2017 3rd International Conference on Computational Intelligence
& Communication Technology (CICT), Ghaziabad, India, (2017) 1–6.
[35] F. Tahmasebi, M. Meghdadi, S. Ahmadian and K. Valiallahi, A hybrid recommendation system based
on profile expansion technique to alleviate cold start problem, Multimedia Tools and Applications,
80 (2) (2021) 2339–2354.
[36] B. Walek and V. Fojtik, A hybrid recommender system for recommending relevant movies using an
expert system, Expert Systems with Applications, 158 (2020) 113452.
[37] D. Wang, Y. Yih and M. Ventresca, Improving neighbor-based collaborative filtering by using a
hybrid similarity measurement, Expert Systems with Applications, 160 (2020) 113651