Abstract
The recommendation system (RS) suffers badly from the cold start problem (CSP) that occurs due to the lack of sufficient information about the new customers, purchase history, and browsing data. Moreover, data sparsity problems also arise when the interaction is made among a limited number of items. These issues not only pose a negative impact on the recommendation but also significantly condense the diversity of choices available on the particular platform. To tackle these issues, a novel methodological approach called sparsity and cold start aware hybrid recommended system (SCSHRS) has been designed to suppress data sparsity and CSP in RS. The performance of the proposed SCSHRS method is tested on MovieLens-20 M, Last.FM and Book-Crossing data sets and compared with the prevailing techniques. Based on the evaluation reports with the standards, the proposed SCSHRS system gives Mean Absolute Percentage Error of 40%, and, precision (0.16), recall (0.08), F-measure (0.1), and Normalized Discounted Cumulative Gain of 0.65. This study completely describes the SCSHRS mechanism and its comparison with other pre-proposed historic and traditional processes based on collaborative filtering.
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References
Abualigah L, Diabat A, Mirjalili S et al (2021a) The arithmetic optimization algorithm. Comput Methods Appl Mech Eng. https://doi.org/10.1016/j.cma.2020.113609
Abualigah L, Yousri D, Abd Elaziz M et al (2021b) Aquila Optimizer: a novel meta-heuristic optimization algorithm. Comput Ind Eng 157:107250. https://doi.org/10.1016/j.cie.2021.107250
Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering
Ahamed MT, Afroge S (2019) A recommender system based on deep neural network and matrix factorization for collaborative filtering. In: 2nd Int conf electr comput commun eng ECCE 2019 1–5. https://doi.org/10.1109/ECACE.2019.8679125
Ahuja R, Solanki A, Nayyar A (2019) Movie recommender system using k-means clustering and k-nearest neighbor. In: Proc 9th Int conf cloud comput data sci eng conflu 2019 263–268. https://doi.org/10.1109/CONFLUENCE.2019.8776969
Anitha J, Kalaiarasu M (2021) Optimized machine learning based collaborative filtering (OMLCF) recommendation system in e-commerce. J Ambient Intell Humaniz Comput 12:6387–6398. https://doi.org/10.1007/s12652-020-02234-1
Barzegar Nozari R, Koohi H (2020) A novel group recommender system based on members’ influence and leader impact. Knowl Based Syst 205:106296. https://doi.org/10.1016/j.knosys.2020.106296
Cami BR, Hassanpour H, Mashayekhi H (2019) User preferences modeling using dirichlet process mixture model for a content-based recommender system. Knowl Based Syst 163:644–655. https://doi.org/10.1016/j.knosys.2018.09.028
Cheng J, Zhang L (2019) Jaccard coefficient-based bi-clustering and fusion recommender system for solving data sparsity. Springer, Cham
Gonzalez Camacho LA, Alves-Souza SN (2018) Social network data to alleviate cold-start in recommender system: a systematic review. Inf Process Manag 54:529–544. https://doi.org/10.1016/j.ipm.2018.03.004
Greenstein-Messica A, Rokach L (2018) Personal price aware multi-seller recommender system: evidence from eBay. Knowledge-Based Syst 150:14–26. https://doi.org/10.1016/j.knosys.2018.02.026
Guo J, Deng J, Wang Y (2019a) An intuitionistic fuzzy set based hybrid similarity model for recommender system. Expert Syst Appl 135:153–163. https://doi.org/10.1016/j.eswa.2019.06.008
Guo X, Yin SC, Zhang YW et al (2019b) Cold start recommendation based on attribute-fused singular value decomposition. IEEE Access 7:11349–11359. https://doi.org/10.1109/ACCESS.2019.2891544
Herce-Zelaya J, Porcel C, Bernabé-Moreno J et al (2020) New technique to alleviate the cold start problem in recommender systems using information from social media and random decision forests. Inf Sci 536:156–170. https://doi.org/10.1016/j.ins.2020.05.071
Hwangbo H, Kim YS, Cha KJ (2018) Recommendation system development for fashion retail e-commerce. Electron Commer Res Appl 28:94–101. https://doi.org/10.1016/j.elerap.2018.01.012
Idrissi N, Zellou A (2020) A systematic literature review of sparsity issues in recommender systems. Soc Netw Anal Min. https://doi.org/10.1007/s13278-020-0626-2
Ioannidis VN, Zamzam AS, Giannakis GB, Sidiropoulos ND (2021) Coupled graphs and tensor factorization for recommender systems and community detection. IEEE Trans Knowl Data Eng 33:909–920. https://doi.org/10.1109/TKDE.2019.2941716
Isinkaye FO, Folajimi YO, Ojokoh BA (2015) Recommendation systems: principles, methods and evaluation. Egypt Inform J 16:261–273. https://doi.org/10.1016/j.eij.2015.06.005
Jain A, Gupta C (2018) Fuzzy logic in recommender systems. Stud Comput Intell 749:255–273. https://doi.org/10.1007/978-3-319-71008-2_20
Jiang L, Cheng Y, Yang L et al (2019) A trust-based collaborative filtering algorithm for E-commerce recommendation system. J Ambient Intell Humaniz Comput 10:3023–3034. https://doi.org/10.1007/s12652-018-0928-7
Katarya R, Verma OP (2017) An effective collaborative movie recommender system with cuckoo search. Egypt Informatics J 18:105–112. https://doi.org/10.1016/j.eij.2016.10.002
Kumar P, Kumar V, Thakur RS (2019) A new approach for rating prediction system using collaborative filtering. Iran J Comput Sci 2:81–87. https://doi.org/10.1007/s42044-018-00028-5
Li M, Wen L, Chen F (2021) A novel collaborative filtering recommendation approach based on soft co-clustering. Phys A Stat Mech Appl 561:125140. https://doi.org/10.1016/j.physa.2020.125140
Lv G, Hu C, Chen S (2016) Research on recommender system based on ontology and genetic algorithm. Neurocomputing 187:92–97. https://doi.org/10.1016/j.neucom.2015.09.113
Mao M, Lu J, Han J, Zhang G (2019) Multiobjective e-commerce recommendations based on hypergraph ranking. Inf Sci 471:269–287. https://doi.org/10.1016/j.ins.2018.07.029
Natarajan S, Vairavasundaram S, Natarajan S, Gandomi AH (2020) Resolving data sparsity and cold start problem in collaborative filtering recommender system using Linked Open Data. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.113248
Nilashi M, Ibrahim O, Bagherifard K (2018) A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques. Expert Syst Appl 92:507–520. https://doi.org/10.1016/j.eswa.2017.09.058
Pappas IO, Kourouthanassis PE, Giannakos MN, Lekakos G (2017) The interplay of online shopping motivations and experiential factors on personalized e-commerce: a complexity theory approach. Telemat Inform 34:730–742. https://doi.org/10.1016/j.tele.2016.08.021
Pujahari A, Sisodia DS (2020) Pair-wise preference relation based probabilistic matrix factorization for collaborative filtering in recommender system. Knowl Based Syst 196:105798. https://doi.org/10.1016/j.knosys.2020.105798
Qiu J, Lin Z, Li Y (2015) Predicting customer purchase behavior in the e-commerce context. Electron Commer Res 15:427–452. https://doi.org/10.1007/s10660-015-9191-6
Qiu L, Gao S, Cheng W, Guo J (2016) Aspect-based latent factor model by integrating ratings and reviews for recommender system. Knowl Based Syst 110:233–243. https://doi.org/10.1016/j.knosys.2016.07.033
Kiran R, Kumar P, Bhasker B (2020) DNNRec: a novel deep learning based hybrid recommender system. Expert Syst Appl 144:113054. https://doi.org/10.1016/j.eswa.2019.113054
Ranjbar Kermany N, Alizadeh SH (2017) A hybrid multi-criteria recommender system using ontology and neuro-fuzzy techniques. Electron Commer Res Appl 21:50–64. https://doi.org/10.1016/j.elerap.2016.12.005
Razia Sulthana A, Ramasamy S (2019) Ontology and context based recommendation system using Neuro-Fuzzy Classification. Comput Electr Eng 74:498–510. https://doi.org/10.1016/j.compeleceng.2018.01.034
Selvi C, Sivasankar E (2019) A novel Adaptive Genetic Neural Network (AGNN) model for recommender systems using modified k-means clustering approach. Multimed Tools Appl 78:14303–14330. https://doi.org/10.1007/s11042-018-6790-y
Silva N, Carvalho D, Pereira ACM et al (2019) The Pure Cold-Start Problem: A deep study about how to conquer first-time users in recommendations domains. Inf Syst 80:1–12. https://doi.org/10.1016/j.is.2018.09.001
Sneha V, Shrinidhi KR, Sunitha RS, Nair MK (2019) Collaborative filtering based recommender system using regression and grey wolf optimization algorithm for sparse data. In: Proc 4th Int conf commun electron syst ICCES 2019 436–441. https://doi.org/10.1109/ICCES45898.2019.9002394
Subramaniyaswamy V, Manogaran G, Logesh R et al (2019) An ontology-driven personalized food recommendation in IoT-based healthcare system. J Supercomput 75:3184–3216. https://doi.org/10.1007/s11227-018-2331-8
Walek B, Fojtik V (2020) A hybrid recommender system for recommending relevant movies using an expert system. Expert Syst Appl 158:113452. https://doi.org/10.1016/j.eswa.2020.113452
Wang CD, Deng ZH, Lai JH, Yu PS (2019a) Serendipitous recommendation in e-commerce using innovator-based collaborative filtering. IEEE Trans Cybern 49:2678–2692. https://doi.org/10.1109/TCYB.2018.2841924
Wang H, Amagata D, Makeawa T et al (2020a) A DNN-based cross-domain recommender system for alleviating cold-start problem in e-commerce. IEEE Open J Ind Electron Soc 1:194–206. https://doi.org/10.1109/OJIES.2020.3012627
Wang K, Zhang T, Xue T et al (2020b) E-commerce personalized recommendation analysis by deeply-learned clustering. J Vis Commun Image Represent. https://doi.org/10.1016/j.jvcir.2019.102735
Wang R, Cheng HK, Jiang Y, Lou J (2019b) A novel matrix factorization model for recommendation with LOD-based semantic similarity measure. Expert Syst Appl 123:70–81. https://doi.org/10.1016/j.eswa.2019.01.036
Wei J, He J, Chen K et al (2017) Collaborative filtering and deep learning based recommendation system for cold start items. Expert Syst Appl 69:1339–1351. https://doi.org/10.1016/j.eswa.2016.09.040
Yuan X, Han L, Qian S et al (2019) Singular value decomposition based recommendation using imputed data. Knowl Based Syst 163:485–494. https://doi.org/10.1016/j.knosys.2018.09.011
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Patro, S.G.K., Mishra, B.K., Panda, S.K. et al. Cold start aware hybrid recommender system approach for E-commerce users. Soft Comput 27, 2071–2091 (2023). https://doi.org/10.1007/s00500-022-07378-0
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DOI: https://doi.org/10.1007/s00500-022-07378-0