Abstract

To overcome the new user cold-start problems in collaborative filtering, an innovative framework has been propsed that used entropy for item inclination in sub-community-based recommender system (EISR). It administered demographic filtering on user and item attributes for finding similar users and applied collaborative filtering on rating preferences. The proposed framework leveraged the advantages of traditional group aggregation strategies for delivering good quality recommendations using item preferences of the members of a refined group detected using two-tier approach. At Tier-I, user communities were detected using demographic attributes, which were decomposed into discernible sub-communities by exploiting the item preferences of users. A novel entropy-based hybrid group aggregation method called pragmatic propensity was used to combine the item preferences of members of these sub-communities. Also, experiments conducted using the MovieLens and Book-crossing datasets revealed the better quality of recommendations and the comparison with other algorithms confirmed the effectiveness of the proposed framework.

Keyword

Group recommender systems, Cold start problem, Community detection, Social network, Entropy, Item inclination, collaborative filtering, Demographic filtering, Group aggregation strategies.

Cite this article

Ahuja H, Narang S, Kaur S, Saxena R

Refference

[1][1]Qiu J, Wu Q, Ding G, Xu Y, Feng S. A survey of machine learning for big data processing. EURASIP Journal on Advances in Signal Processing. 2016; 2016(1):1-6.

[2][2]Enríquez JG, Morales-trujillo L, Calle-alonso F, Domínguez-mayo FJ, Lucas-rodríguez JM. Recommendation and classification systems: a systematic mapping study. Scientific Programming. 2019.

[3][3]Rela M, Rao SN, Patil RR. Performance analysis of liver tumor classification using machine learning algorithms. International Journal of Advanced Technology and Engineering Exploration. 2022; 9(86):143-54.

[4][4]Thakur B, Kumar N, Gupta G. Machine learning techniques with ANOVA for the prediction of breast cancer. International Journal of Advanced Technology and Engineering Exploration. 2022; 9(87):232-45.

[5][5]Nawang H, Makhtar M, Hamzah WM. Comparative analysis of classification algorithm evaluations to predict secondary school students’ achievement in core and elective subjects. International Journal of Advanced Technology and Engineering Exploration. 2022; 9(89):430-45.

[6][6]Ankalaki S, Thippeswamy MN. A customized 1D-CNN approach for sensor-based human activity recognition. International Journal of Advanced Technology and Engineering Exploration. 2022; 9(87):216-31.

[7][7]Beheshti A, Yakhchi S, Mousaeirad S, Ghafari SM, Goluguri SR, Edrisi MA. Towards cognitive recommender systems. Algorithms. 2020; 13(8):1-27.

[8][8]Yassine AF, Mohamed LA, Al AM. Intelligent recommender system based on unsupervised machine learning and demographic attributes. Simulation Modelling Practice and Theory. 2021.

[9][9]Walek B, Fojtik V. A hybrid recommender system for recommending relevant movies using an expert system. Expert Systems with Applications. 2020.

[10][10][10] Su JH, Chin CY, Liao YW, Yang HC, Tseng VS, Hsieh SY. A personalized music recommender system using user contents, music contents and preference ratings. Vietnam Journal of Computer Science. 2020; 7(1):77-92.

[11][11]Bazinin S, Shani G. Investigating recommendation algorithms for escape rooms. Vietnam Journal of Computer Science. 2019; 6(4):377-88.

[12][12]Alshammari G, Kapetanakis S, Alshammari A, Polatidis N, Petridis M. Improved movie recommendations based on a hybrid feature combination method. Vietnam Journal of Computer Science. 2019; 6(3):363-76.

[13][13]Liao M, Sundar SS, B. Walther J. User trust in recommendation systems: a comparison of content-based, collaborative and demographic filtering. In CHI conference on human factors in computing systems 2022 (pp. 1-14).

[14][14]Xinchang K, Vilakone P, Park DS. Movie recommendation algorithm using social network analysis to alleviate cold-start problem. Journal of Information Processing Systems. 2019; 15(3):616-31.

[15][15]González Á, Ortega F, Pérez-lópez D, Alonso S. Bias and unfairness of collaborative filtering based recommender systems in movielens dataset. IEEE Access. 2022; 10:68429-39.

[16][16]Homann L, Martins DM, Vossen G, Kraume K. Enhancing traditional recommender systems via social communities. Vietnam Journal of Computer Science. 2019; 6(1):3-16.

[17][17]Gorripati SK, Vatsavayi VK. Community-based collaborative filtering to alleviate the cold-start and sparsity problems. International Journal of Applied Engineering Research. 2017; 12(15):5022-30.

[18][18]Ahmadian S, Joorabloo N, Jalili M, Ahmadian M. Alleviating data sparsity problem in time-aware recommender systems using a reliable rating profile enrichment approach. Expert Systems with Applications. 2022.

[19][19]Sánchez P, Bellogín A. Point-of-interest recommender systems based on location-based social networks: a survey from an experimental perspective. ACM Computing Surveys (CSUR). 2022; 54(115):1-37.

[20][20]Bedi P, Gautam A, Bansal S, Bhatia D. Weighted bipartite graph model for recommender system using entropy based similarity measure. In the international symposium on intelligent systems technologies and applications 2017 (pp. 163-73). Springer, Cham.

[21][21]Gasparetti F, Sansonetti G, Micarelli A. Community detection in social recommender systems: a survey. Applied Intelligence. 2021; 51(6):3975-95.

[22][22]Vilakone P, Park DS, Xinchang K, Hao F. An efficient movie recommendation algorithm based on improved k-clique. Human-Centric Computing and Information Sciences. 2018; 8(1):1-15.

[23][23]Gonzalez-camacho LA, Faria JH, Machado LT, Alves-souza SN. Recommender system based on the friendship between social network users in a cold-start scenario. In world conference on information systems and technologies 2022 (pp. 234-52). Springer, Cham.

[24][24]Cao KY, Liu Y, Zhang HX. Improving the cold start problem in music recommender systems. In journal of physics: conference series 2020 (pp. 1-6). IOP Publishing.

[25][25]Anwaar F, Iltaf N, Afzal H, Nawaz R. HRS-CE: a hybrid framework to integrate content embeddings in recommender systems for cold start items. Journal of Computational Science. 2018; 29:9-18.

[26][26]Hawashin B, Mansour A, Kanan T, Fotouhi F. An efficient cold start solution based on group interests for recommender systems. In proceedings of the first international conference on data science, e-learning and information systems 2018 (pp. 1-5).

[27][27]Tey FJ, Wu TY, Lin CL, Chen JL. Accuracy improvements for cold-start recommendation problem using indirect relations in social networks. Journal of Big Data. 2021; 8(1):1-18.

[28][28]Ceh-varela E, Cao H, Lauw HW. Performance evaluation of aggregation-based group recommender systems for ephemeral groups. ACM Transactions on Intelligent Systems and Technology. 2022; 13(6):1-26.

[29][29]Boratto L, Carta S, Fenu G. Discovery and representation of the preferences of automatically detected groups: exploiting the link between group modeling and clustering. Future Generation Computer Systems. 2016; 64:165-74.

[30][30]Salehi-abari A, Boutilier C. Preference-oriented social networks: group recommendation and inference. In proceedings of the ACM conference on recommender systems 2015 (pp. 35-42).

[31][31]Seo YD, Kim YG, Lee E, Seol KS, Baik DK. An enhanced aggregation method considering deviations for a group recommendation. Expert Systems with Applications. 2018; 93:299-312.

[32][32]Yalcin E, Ismailoglu F, Bilge A. An entropy empowered hybridized aggregation technique for group recommender systems. Expert Systems with Applications. 2021.

[33][33]Kaššák O, Kompan M, Bieliková M. Personalized hybrid recommendation for group of users: Top-N multimedia recommender. Information Processing & Management. 2016; 52(3):459-77.

[34][34]Liu Y, Wang B, Wu B, Zeng X, Shi J, Zhang Y. Cogrec: a community-oriented group recommendation framework. In international conference of pioneering computer scientists, engineers and educators 2016 (pp. 258-71). Springer, Singapore.

[35][35]Masthoff J. Group recommender systems: aggregation, satisfaction and group attributes. In recommender systems handbook 2015 (pp. 743-76). Springer, Boston, MA.

[36][36]Ahmad HS, Nurjanah D, Rismala R. A combination of individual model on memory-based group recommender system to the books domain. In 5th international conference on information and communication technology 2017 (pp. 1-6). IEEE.

[37][37]Agarwal A, Chakraborty M, Chowdary CR. Does order matter? effect of order in group recommendation. Expert Systems with Applications. 2017; 82:115-27.

[38][38]Yargic A, Bilge A. Privacy-preserving multi-criteria collaborative filtering. Information Processing & Management. 2019; 56(3):994-1009.

[39][39]Shannon CE. Prediction and entropy of printed English. Bell System Technical Journal. 1951; 30(1):50-64.

[40][40]Mehta H, Bedi P, Dixit VS. Group recommendation for mitigating new user problem: a modified OCRG. Journal of Network and Innovative Computing. 2013; 1(1):99-108.

[41][41]Ismailoglu F. Aggregating user preferences in group recommender systems: a crowdsourcing approach. Decision Support Systems. 2022.

[42][42]Kitsak M, Gallos LK, Havlin S, Liljeros F, Muchnik L, Stanley HE, et al. Identification of influential spreaders in complex networks. Nature Physics. 2010; 6(11):888-93.

[43][43]Saxena R, Kaur S, Bhatnagar V. Social centrality using network hierarchy and community structure. Data Mining and Knowledge Discovery. 2018; 32(5):1421-43.

[44][44]Batagelj V, Zaveršnik M. Fast algorithms for determining (generalized) core groups in social networks. Advances in Data Analysis and Classification. 2011; 5(2):129-45.

[45][45]Wang J, Cheng J. Truss decomposition in massive networks. arXiv preprint arXiv:1205.6693. 2012.

[46][46]https://grouplens.org/datasets/ movielens/. Accessed 10 April 2022.

[47][47]Ziegler CN, Mcnee SM, Konstan JA, Lausen G. Improving recommendation lists through topic diversification. In proceedings of the 14th international conference on World Wide Web 2005 (pp. 22-32).

[48][48]https://bisg.org/page/BISACEdition. Accessed 10 April 2022.

[49][49]Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment. 2008.

[50][50]Easley D, Kleinberg J. Networks, crowds, and markets: reasoning about a highly connected world. Cambridge University Press; 2010.

[51][51]Newman ME. Assortative mixing in networks. Physical Review Letters. 2002; 89(20).

[52][52]Resnick P, Iacovou N, Suchak M, Bergstrom P, Riedl J. Grouplens: an open architecture for collaborative filtering of netnews. In proceedings of the 1994 ACM conference on computer supported cooperative work 1994 (pp. 175-86).

[53][53]Sarwar B, Karypis G, Konstan J, Riedl J. Item-based collaborative filtering recommendation algorithms. In proceedings of the international conference on World Wide Web 2001 (pp. 285-95).