(Publisher of Peer Reviewed Open Access Journals)

International Journal of Advanced Technology and Engineering Exploration (IJATEE)

ISSN (Print):2394-5443    ISSN (Online):2394-7454
Volume-9 Issue-93 August-2022
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Paper Title : Entropy for item inclination in sub-community based recommender system
Author Name : Harita Ahuja, Sunita Narang, Sharanjit Kaur and Rakhi Saxena
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.

Keywords : 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. Entropy for item inclination in sub-community based recommender system . International Journal of Advanced Technology and Engineering Exploration. 2022; 9(93):1111-1133. DOI:10.19101/IJATEE.2021.875768.
References :
[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.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[7]Beheshti A, Yakhchi S, Mousaeirad S, Ghafari SM, Goluguri SR, Edrisi MA. Towards cognitive recommender systems. Algorithms. 2020; 13(8):1-27.
[Crossref] [Google Scholar]
[8]Yassine AF, Mohamed LA, Al AM. Intelligent recommender system based on unsupervised machine learning and demographic attributes. Simulation Modelling Practice and Theory. 2021.
[Crossref] [Google Scholar]
[9]Walek B, Fojtik V. A hybrid recommender system for recommending relevant movies using an expert system. Expert Systems with Applications. 2020.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[11]Bazinin S, Shani G. Investigating recommendation algorithms for escape rooms. Vietnam Journal of Computer Science. 2019; 6(4):377-88.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[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).
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[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.
[Google Scholar]
[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.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[21]Gasparetti F, Sansonetti G, Micarelli A. Community detection in social recommender systems: a survey. Applied Intelligence. 2021; 51(6):3975-95.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[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).
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[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).
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[32]Yalcin E, Ismailoglu F, Bilge A. An entropy empowered hybridized aggregation technique for group recommender systems. Expert Systems with Applications. 2021.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[35]Masthoff J. Group recommender systems: aggregation, satisfaction and group attributes. In recommender systems handbook 2015 (pp. 743-76). Springer, Boston, MA.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[37]Agarwal A, Chakraborty M, Chowdary CR. Does order matter? effect of order in group recommendation. Expert Systems with Applications. 2017; 82:115-27.
[Crossref] [Google Scholar]
[38]Yargic A, Bilge A. Privacy-preserving multi-criteria collaborative filtering. Information Processing & Management. 2019; 56(3):994-1009.
[Crossref] [Google Scholar]
[39]Shannon CE. Prediction and entropy of printed English. Bell System Technical Journal. 1951; 30(1):50-64.
[Crossref] [Google Scholar]
[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.
[Google Scholar]
[41]Ismailoglu F. Aggregating user preferences in group recommender systems: a crowdsourcing approach. Decision Support Systems. 2022.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[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.
[Crossref] [Google Scholar]
[45]Wang J, Cheng J. Truss decomposition in massive networks. arXiv preprint arXiv:1205.6693. 2012.
[Google Scholar]
[46]https://grouplens.org/datasets/ movielens/. Accessed 10 April 2022.
[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).
[Crossref] [Google Scholar]
[48]https://bisg.org/page/BISACEdition. Accessed 10 April 2022.
[49]Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment. 2008.
[Crossref] [Google Scholar]
[50]Easley D, Kleinberg J. Networks, crowds, and markets: reasoning about a highly connected world. Cambridge University Press; 2010.
[Google Scholar]
[51]Newman ME. Assortative mixing in networks. Physical Review Letters. 2002; 89(20).
[Google Scholar]
[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).
[Crossref] [Google Scholar]
[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).
[Crossref] [Google Scholar]