Abstract
Recommender system (RS) has grown widely in various communities over the last few years. It creates curiosity among the researchers due to the recent growth of various commerce companies, especially Flipkart and Amazon. In collaborative filtering-based RS, the system aims to provide the users with their personalized items, which is based on the users’ past history. In general, these observations are represented in the form of rating matrix. However, these ratings are not uniform as some user ratings are stringent and others are lenient. As a result, the RS is incompetent to suggest the personalized items to the stringent users. In this manuscript, we design a normalization-based collaborative filtering recommender to overcome the above problem. The proposed algorithm consists of two phases, namely designing and evaluating. In the first phase, the proposed algorithm finds the average user rating per item and counts the number of users purchased each item. Then it uses min–max normalization to find the normalized user count per item and scale the average ratings of users in a specified range. In the latter phase, the proposed algorithm divides the rating matrix into training and testing rating matrix, and predicts the users’ ratings. We perform rigorous simulations using a large variety of users and items, and compare the results with a collaborative filtering-based RS using ten performance metrics to illustrate the efficacy of the proposed algorithm. Moreover, we evaluate the results through a statistical test, t-test and 95% confidence interval.
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References
Afridi A, Yasar A, Shakshuki E (2019) Facilitating research through serendipity of recommendations. J Ambient Intell Hum Comput Springer.https://doi.org/10.1007/s12652-019-01354-7
Ahn H (2008) A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Inf Sci 178(1):37–51. https://doi.org/10.1016/j.ins.2007.07.024
Bobadilla J, Serradilla F, Hernando A (2009) Collaborative filtering adapted to recommender systems of e-Learning. Knowl-Based Syst Elsevier 22(4):265. https://doi.org/10.1016/j.knosys.2009.01.008
Bobadilla J, Ortega F, Hernando A, Gutierrez A (2013) Recommender systems survey. Knowl-Based Syst Elsevier 46:132. https://doi.org/10.1016/j.knosys.2013.03.012
Boslaugh S (2012) Statistics in a Nutshell, 2nd edn. O’Reilly Media Inc, Massachusetts
Burke R (2000) Knowledge-based recommender systems. Encycl Libr Inf Syst 69(32):186
Chang W, Jung C (2017) A hybrid approach for personalized service staff recommendation. Inf Syst Front Springer 19(1):163. https://doi.org/10.1007/s10796-015-9597-7
Diaby M, Viennet E, Launay T (2014) Exploration of methodologies to improve job recommender systems on social networks. Soc Netw Anal Min Springer 4:17. https://doi.org/10.1007/s13278-014-0227-z
Han J, Kamber M (2012) Data mining: concepts and techniques. Morgan Kaufmann Series, Elsevier, Amsterdam 3rd Edition, pp 111–119. https://doi.org/10.1016/C2009-0-61819-5
Hernando A, Bobadilla J, Ortega F (2016) A non negative matrix factorization for collaborative filtering recommender systems based on a bayesian probabilistic model. Knowl-Based Syst Elsevier 97(C):202. https://doi.org/10.1016/j.knosys.2015.12.018
Herlocker J, Konstan J, Riedl J (2002) An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Inf Retr Springer 5(4):310. https://doi.org/10.1023/A:1020443909834
Herlocker J, Konstan J, Terveen L, Riedl J (2004) Evaluating collaborative filtering recommender systems. ACM Trans Inf Syst 22(1):53. https://doi.org/10.1145/963770.963772
Hong M, An S, Akerkar R, Camacho D, Jung J (2019) Cross-cultural contextualization for recommender systems. J Ambient Intell Hum Comput Springer.https://doi.org/10.1007/s12652-019-01479-9
Hu J, Sharma S, Gao Z, Chang V (2018) Gene-based collaborative filtering using recommender system. Comput Electr Eng Elsevier 65:341. https://doi.org/10.1016/j.compeleceng.2017.04.010
Isinkaye F, Folajimi Y, Ojokoh B (2015) Recommendation systems: principles, methods and evaluation. Egypt Inf J Elsevier 16(3):273. https://doi.org/10.1016/j.eij.2015.06.005
Kala K, Nandhini M (2019) Context-category specific sequence aware point-of-interest recommender system with multi-gated recurrent unit. J Ambient Intell Hum Comput Springer.https://doi.org/10.1007/s12652-019-01583-w
Karidi D, Stavrakas Y, Vassiliou Y (2018) Tweet and followee personalized recommendations based on knowledge graphs. J Ambient Intell Hum Comput Springer 9(6):2049. https://doi.org/10.1007/s12652-017-0491-7
Kim S, Sung K, Park C, Kim S (2016) Improvement of collaborative filtering using rating normalization. Multimed Tools Appl Springer 75(9):4968. https://doi.org/10.1007/s11042-013-1814-0
Koren Y (2009) Collaborative filtering with temporal dynamics. In: 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp 447–456. https://doi.org/10.1145/1557019.1557072
Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Comput IEEE 42(8):37. https://doi.org/10.1109/MC.2009.263
Larose D, Larose C (2014) Discovering knowledge in data: an introduction to data mining, 2nd edn. John Wiley and Sons Inc, Boca Raton
Linden G, Smith B, York J (2003) Amazon.com recommendations: item-to-item collaborative filtering. IEEE Internet Comput.https://doi.org/10.1109/MIC.2003.1167344
Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recomender system application developments: a survey. Decis Support Syst Elsevier 74(C):32. https://doi.org/10.1016/j.dss.2015.03.008
Lu L, Medo M, Yeung C, Zhang Y, Zhang Z, Zhou T (2012) Recommender systems. Phys Rep Elsevier 519:49. https://doi.org/10.1016/j.physrep.2012.02.006
Majeed T, Stampfli A, Liebrich A, Meier R (2019) What is of interest for tourists in an alpine destination: personalized recommendations for daily activities based on view data. J Ambient Intell Hum Comput Springer.https://doi.org/10.1007/s12652-019-01619-1
McKinsey and Company: How Retailers Can Keep Up With Consumers (2019) https://www.mckinsey.com/industries/retail/our-insights/how-retailers-can-keep-up-with-consumers. Accessed 25 May 2019
Nayak S, Panda S (2018) A user-oriented collaborative filtering algorithm for recommender systems. In: 5th IEEE International conference on parallel, distributed and grid computing, pp 374–380. https://doi.org/10.1109/PDGC.2018.8745892
Netflix Prize (2019) https://www.netflixprize.com/leaderboard.html. Accessed 27 May 2019
Orciuoli F, Parente M (2017) An ontology-driven context-aware recommender system for indoor shopping based on cellular automata. J Ambient Intell Hum Comput Springer 8:955. https://doi.org/10.1007/s12652-016-0411-2
Ott R, Longnecker M (2010) An introduction to statistical methods and data analysis, 6th edn. Duxbury Press, California
Panda S, Senapati M, Sahu S (2019) An item-oriented collaborative filtering algorithm for recommender systems. In: 60th Annual technical session, the institute of engineers (india), pp 228–236
Park J (2019) Resource recommender system based on psychological user type indicator. J Ambient Intell Hum Comput Springer 10(1):39. https://doi.org/10.1007/s12652-017-0583-4
Pelanek R (2017) Measuring predictive performance of user models: the details matter. In: 25th Conference on User Modeling, Adaptation and Personalization, ACM, pp 197–201. https://doi.org/10.1145/3099023.3099042
Porteous I, Asuncion A, Welling M (2010) Bayesian matrix factorization with side information and dirichlet process mixtures. In: Twenty-Fourth AAAI Conference on Artificial Intelligence, pp 563–568
Rong W, Peng B, Ouyang Y, Liu K, Xiong Z (2015) Collaborative personal profiling for web service ranking and recommendation. Inf Syst Front Springer 17(6):1282. https://doi.org/10.1007/s10796-014-9495-4
Salas Julian (2019) Sanitizing and measuring privacy of large sparse datasets for recommender systems. J Ambient Intell Hum Comput Springer. https://doi.org/10.1007/s12652-019-01391-2
Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algoritms. In: 10th International Conference on World Wide Web, ACM, pp 285–295. https://doi.org/10.1145/371920.372071
Tan Z, He L (2017) An efficient similarity measure for user-based collaborative filtering recommender systems inspired by the physical resonance principle. IEEE Access 5:27211–27228. https://doi.org/10.1109/ACCESS.2017.2778424
Wang J, Vries A, Reinders M (2006) Unifying user-based and item-based collaborative filtering approaches by similarity fusion. In: 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM, pp 501–508. https://doi.org/10.1145/1148170.1148257
Wei C, Khoury R, Fong S (2013) Web 2.0 recommendation service by multi-collaborative filtering trust network algorithm. Inf Syst Front Springer 15(4):551. https://doi.org/10.1007/s10796-012-9377-6
Xie F, Xu M, Chen Z (2012) RBRA: a simple and efficient rating-based recommender algorithm to cope with sparsity in recommender systems. In: 26th International Conference on Advanced Information Networking and Applications Workshops, IEEE, pp 306–311. https://doi.org/10.1109/WAINA.2012.11
Yang Y, Hooshyar D, Jo J, Lim H (2018) A group preference-based item similarity model: comparison of clustering techniques in ambient and context-aware recommender systems. J Ambient Intell Hum Comput Springer.https://doi.org/10.1007/s12652-018-1039-1
Yang Z, Wu B, Zheng K, Wang X, Lei L (2016) A survey of collaborative filtering-based recommender systems for mobile internet applications. IEEE Access 4:3273–3287. https://doi.org/10.1109/ACCESS.2016.2573314
Yongping D, Xiaoyan D, Liang H (2016) Improve the collaborative filtering recommender system performance by trust network construction. Chin J Electron 25(3):418–423. https://doi.org/10.1049/cje.2016.05.005
Yu Y, Gao Y, Wang H, Wang R (2018) Joint user knowledge and matrix factorization for recommender systems. World Wide Web Springer 21(4):1141–1163. https://doi.org/10.1007/s11280-017-0476-7
Zarzour H, Al-Sharif Z, Al-Ayyoub M, Jararweh Y (2018) A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. In: 9th International conference on information and communication systems, IEEE, pp 102–106. https://doi.org/10.1109/IACS.2018.8355449
Zhang H, Ji Y, Li J, Ye Y (2016) A triple wing harmonium model for movie recommendation. IEEE Trans Ind Inf 12(1):239. https://doi.org/10.1109/TII.2015.2475218
Zhang F, Lee V, Jin R, Garg S, Choo K, Maasberg M, Dong L, Cheng C (2018) Privacy-aware smart city: a case study in collaborative filtering recommender systems. J Parallel Distrib Comput Elsevier 127:159. https://doi.org/10.1016/j.jpdc.2017.12.015
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Panda, S.K., Bhoi, S.K. & Singh, M. A collaborative filtering recommendation algorithm based on normalization approach. J Ambient Intell Human Comput 11, 4643–4665 (2020). https://doi.org/10.1007/s12652-020-01711-x
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DOI: https://doi.org/10.1007/s12652-020-01711-x