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Approaches and algorithms to mitigate cold start problems in recommender systems: a systematic literature review

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Abstract

Cold Start problems in recommender systems pose various challenges in the adoption and use of recommender systems, especially for new item uptake and new user engagement. This restricts organizations to realize the business value of recommender systems as they have to incur marketing and operations costs to engage new users and promote new items. Owing to this, several studies have been done by recommender systems researchers to address the cold start problems. However, there has been very limited recent research done on collating these approaches and algorithms. To address this gap, the paper conducts a systematic literature review of various strategies and approaches proposed by researchers in the last decade, from January 2010 to December 2021, and synthesizes the same into two categories: data-driven strategies and approach-driven strategies. Furthermore, the approach-driven strategies are categorized into five main clusters based on deep learning, matrix factorization, hybrid approaches, or other novel approaches in collaborative filtering and content-based algorithms. The scope of this study is limited to a systematic literature review and it does not include an experimental study to benchmark and recommend the best approaches and their context of use in cold start scenarios.

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Data Availability Statement

Data sharing is not applicable to this article as no data-sets were generated or analysed during the current study.

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References

  • Abdullah, N.A., Rasheed, R.A., Nizam, M.H., & Rahman, M.M. (2021). Eliciting auxiliary information for cold start user recommendation: A survey. Applied Sciences (Switzerland), 11.

  • Abel, F., Herder, E., Houben, G.J., Henze, N., & Krause, D. (2013). Cross-system user modeling and personalization on the social web. User Modeling and User-Adapted Interaction, 23, 169–209. https://doi.org/10.1007/s11257-012-9131-2.

    Article  Google Scholar 

  • Ahmadian, S., Afsharchi, M., & Meghdadi, M. (2019). An effective social recommendation method based on user reputation model and rating profile enhancement. Journal of Information Science, 45, 607–642. https://doi.org/10.1177/0165551518808191.

    Article  Google Scholar 

  • Aksnes, D.W., Langfeldt, L., & Wouters, P. (2019). Citations, citation indicators, and research quality: An overview of basic concepts and theories. SAGE Open, 9.

  • Alhijawi, B., & Kilani, Y. (2016). Using genetic algorithms for measuring the similarity values between users in collaborative filtering recommender systems. In Proceedings of the IEEE/ ACIS 15th international conference on computer and information science (ICIS). IEEE. https://doi.org/10.1109/icis.2016.7550751.

  • Alhijawi, B., & Kilani, Y. (2020). A collaborative filtering recommender system using genetic algorithm. Information Processing and Management, 57, 102310. https://doi.org/10.1016/j.ipm.2020.102310.

    Article  Google Scholar 

  • Anwaar, F., Iltaf, N., Afzal, H., & Nawaz, R. (2018). Hrs-ce: A hybrid framework to integrate content embeddings in recommender systems for cold start items. Journal of Computational Science, 29, 9–18. https://doi.org/10.1016/j.jocs.2018.09.008.

    Article  Google Scholar 

  • Bahrani, P., Minaei-Bidgoli, B., Parvin, H., Mirzarezaee, M., Keshavarz, A., & Alinejad-Rokny, H. (2020). User and item profile expansion for dealing with cold start problem. Journal of Intelligent and Fuzzy Systems, 38, 4471–4483. https://doi.org/10.3233/jifs-191225.

    Article  Google Scholar 

  • Bi, X., Qu, A., Wang, J., & Shen, X. (2017). A group-specific recommender system. Journal of the American Statistical Association, 112, 1344–1353. https://doi.org/10.1080/01621459.2016.1219261.

    Article  MathSciNet  Google Scholar 

  • Bobadilla, J., Ortega, F., Hernando, A., & Alcal (2011). Improving collaborative filtering recommender system results and performance using genetic algorithms. Knowledge-Based Systems, 24(8), 1310–1316. https://doi.org/10.1016/j.knosys.2011.06.005.

    Article  Google Scholar 

  • Bobadilla, J., Ortega, F., Hernando, A., & Gutiérrez, A. (2013). Recommender systems survey. Knowledge-Based Systems, 46, 109–132. https://doi.org/10.1016/j.knosys.2013.03.012.

    Article  Google Scholar 

  • Cai, M., Gong, Z., & Li, Y. (2017). Fuzzy prototype classifier based on items and its application in recommender system. International Journal of Computational Intelligence Systems, 10, 1016. https://doi.org/10.2991/ijcis.2017.10.1.68.

    Article  Google Scholar 

  • Camacho, L.A.G., & Alves-Souza, S.N. (2018). Social network data to alleviate cold-start in recommender system: A systematic review. Information Processing and Management, 54, 529–544. https://doi.org/10.1016/j.ipm.2018.03.004.

    Article  Google Scholar 

  • Chalyi, S., Leshchynskyi, V., & Leshchynska, I. (2019). Method of forming recommendations using temporal constraints in a situation of cyclic cold start of the recommender system. EUREKA Physics and Engineering, 2019, 34–40. https://doi.org/10.21303/2461-4262.2019.00952.

    Article  Google Scholar 

  • Chen, C.C., Wan, Y.H., Chung, M.C., & Sun, Y.C. (2013). An effective recommendation method for cold start new users using trust and distrust networks. Information Sciences, 224, 19–36. https://doi.org/10.1016/j.ins.2012.10.037.

    Article  MathSciNet  Google Scholar 

  • Chen, H.H., & Chen, P. (2019). Differentiating regularization weights - a simple mechanism to alleviate cold start in recommender systems. ACM Transactions on Knowledge Discovery from Data, 13.

  • Chen, L., & Pu, P. (2012). Critiquing-based recommenders: Survey and emerging trends. User Modeling and User-Adapted Interaction, 22, 125–150. https://doi.org/10.1007/s11257-011-9108-6.

    Article  Google Scholar 

  • Choi, S.M., Jang, K., Lee, T.D., Khreishah, A., & Noh, W. (2020). Alleviating item-side cold-start problems in recommender systems using weak supervision. IEEE Access, 8, 167747–167756. https://doi.org/10.1109/ACCESS.2020.3019464.

    Article  Google Scholar 

  • Deldjoo, Y., Dacrema, M.F., Constantin, M.G., Eghbal-zadeh, H., Cereda, S., Schedl, M., Ionescu, B., & Cremonesi, P. (2019). Movie genome: Alleviating new item cold start in movie recommendation. User Modeling and User-Adapted Interaction, 29, 291–343. https://doi.org/10.1007/s11257-019-09221-y.

    Article  Google Scholar 

  • Ebesu, T., & Fang, Y. (2017). Neural semantic personalized ranking for item cold-start recommendation. Information Retrieval Journal, 20, 109–131. https://doi.org/10.1007/s10791-017-9295-9.

    Article  Google Scholar 

  • Felfernig, A., & Burke, R. (2008). Constraint-based recommender systems: Technologies and research issues. In 10th Int. Conf. on Electronic Commerce (ICEC) ’08 Innsbruck. Austria: ACM Press.

  • Feng, J., Xia, Z., Feng, X., & Peng, J. (2021). Rbpr: A hybrid model for the new user cold start problem in recommender systems. Knowledge-Based Systems, 214.

  • Ferdaous, H., Bouchra, F., Brahim, O., Imad-eddine, M., & Asmaa, B. (2018). Recommendation using a clustering algorithm based on a hybrid features selection method. Journal of Intelligent Information Systems, 51, 183–205. https://doi.org/10.1007/s10844-017-0493-0.

    Article  Google Scholar 

  • Fernández, D., Formoso, V., Cacheda, F., & Carneiro, V. (2019). High order profile expansion to tackle the new user problem on recommender systems. PLoS ONE, 14, 1–16. https://doi.org/10.1371/journal.pone.0224555.

    Article  Google Scholar 

  • Fernández-Tobías, I., Cantador, I., Tomeo, P., Anelli, V.W., & Noia, T.D. (2019). Addressing the user cold start with cross-domain collaborative filtering: exploiting item metadata in matrix factorization. User Modeling and User-Adapted Interaction, 29, 443–486. https://doi.org/10.1007/s11257-018-9217-6.

    Article  Google Scholar 

  • Ghavipour, M., & Meybodi, M.R. (2019). Stochastic trust network enriched by similarity relations to enhance trust-aware recommendations. Applied Intelligence, 49, 435–448. https://doi.org/10.1007/s10489-018-1289-9.

    Article  Google Scholar 

  • Guan, Y., Wei, Q., & Chen, G. (2019). Deep learning based personalized recommendation with multi-view information integration. Decision Support Systems, 118, 58–69. https://doi.org/10.1016/j.dss.2019.01.003https://doi.org/0.1016/j.dss.2019.01.003.

    Article  Google Scholar 

  • Guo, G., Zhang, J., & Thalmann, D. (2014). Merging trust in collaborative filtering to alleviate data sparsity and cold start. Knowledge-Based Systems, 57, 57–68. https://doi.org/10.1016/j.knosys.2013.12.007.

    Article  Google Scholar 

  • Guo, X., Yin, S.C., Zhang, Y.W., Li, W., & He, Q. (2019). Cold start recommendation based on attribute-fused singular value decomposition. IEEE Access, 7, 11349–11359. https://doi.org/10.1109/ACCESS.2019.2891544.

    Article  Google Scholar 

  • Han, D., Li, J., Yang, L., & Zeng, Z. (2019). A recommender system to address the cold start problem for app usage prediction. International Journal of Machine Learning and Cybernetics, 10, 2257–2268. https://doi.org/10.1007/s13042-018-0864-z.

    Article  Google Scholar 

  • Hasan, M., & Roy, F. (2019). An item–item collaborative filtering recommender system using trust and genre to address the cold-start problem. Big Data and Cognitive Computing, 3, 1–15. https://doi.org/10.3390/bdcc3030039.

    Article  Google Scholar 

  • Herce-Zelaya, J., Porcel, C., Bernabé-Moreno, J., Tejeda-Lorente, A., & Herrera-Viedma, E. (2020). New technique to alleviate the cold start problem in recommender systems using information from social media and random decision forests. Information Sciences, 536, 156–170. https://doi.org/10.1016/j.ins.2020.05.071.

    Article  MathSciNet  Google Scholar 

  • Hernando, A., Bobadilla, J., Ortega, F., & Gutiérrez, A. (2017). A probabilistic model for recommending to new cold-start non-registered users. Information Sciences, 376, 216–232. https://doi.org/10.1016/j.ins.2016.10.009.

    Article  Google Scholar 

  • Hong, D.G., Lee, Y.C., Lee, J., & Kim, S.W. (2019). Crowdstart: Warming up cold-start items using crowdsourcing. Expert Systems with Applications, 138.

  • Hu, L., Cao, L., Cao, J., Gu, Z., Xu, G., & Yang, D. (2016). Learning informative priors from heterogeneous domains to improve recommendation in cold-start user domains. ACM Transactions on Information Systems, 35.

  • Jeevamol, J., & Renumol, V.G. (2021). An ontology-based hybrid e-learning content recommender system for alleviating the cold-start problem. Education and Information Technologies, 26, 4993–5022. https://doi.org/10.1007/s10639-021-10508-0.

    Article  Google Scholar 

  • Ji, K., & Shen, H. (2015). Addressing cold-start: Scalable recommendation with tags and keywords. Knowledge-Based Systems, 83, 42–50. https://doi.org/10.1016/j.knosys.2015.03.008.

    Article  Google Scholar 

  • Kim, H.N., El-Saddik, A., & Jo, G.S. (2011). Collaborative error-reflected models for cold-start recommender systems. Decision Support Systems, 51, 519–531. https://doi.org/10.1016/j.dss.2011.02.015.

    Article  Google Scholar 

  • Kolahkaj, M., Harounabadi, A., Nikravanshalmani, A., & Chinipardaz, R. (2020). A hybrid context-aware approach for e-tourism package recommendation based on asymmetric similarity measurement and sequential pattern mining. Electronic Commerce Research and Applications, 42.

  • Leung, C.W.K., Chan, S.C.F., & Chung, F.L. (2008). An empirical study of a cross-level association rule mining approach to cold-start recommendations. Knowledge-Based System, 21(7), 515–529.

    Article  Google Scholar 

  • Li, C.T., Hsu, C.T., & Shan, M.K. (2018). A cross-domain recommendation mechanism for cold-start users based on partial least squares regression. ACM Transactions on Intelligent Systems and Technology, 9.

  • Li, S., Lei, W., Wu, Q., He, X., Jiang, P., & Chua, T.S. (2021). Seamlessly unifying attributes and items: Conversational recommendation for cold-start users. ACM Transactions on Information Systems, 39.

  • Liu, H., Hu, Z., Mian, A., Tian, H., & Zhu, X. (2014a). A new user similarity model to improve the accuracy of collaborative filtering. Knowledge-Based System, 56, 156–166.

    Article  Google Scholar 

  • Liu, J.H., Zhou, T., Zhang, Z.K., Yang, Z., Liu, C., & Li, W.M. (2014b). Promoting cold-start items in recommender systems. PLoS ONE, 9, 1–14. https://doi.org/10.1371/journal.pone.0113457.

    Article  Google Scholar 

  • Liu, S., & Meng, X. (2015). A location-based business information recommendation algorithm. Mathematical Problems in Engineering, 2015.

  • Ma, Y., Geng, X., & Wang, J. (2021). A deep neural network with multiplex interactions for cold-start service recommendation. IEEE Transactions on Engineering Management, 68, 105–119. https://doi.org/10.1109/TEM.2019.2961376.

    Article  Google Scholar 

  • Mansoury, M., & Shajari, M. (2016). Improving recommender systems’ performance on cold-start users and controversial items by a new similarity model. International Journal of Web Information Systems, 12, 126–149. https://doi.org/10.1108/IJWIS-07-2015-0024.

    Article  Google Scholar 

  • Masood, M.A., Abbasi, R.A., Maqbool, O., Mushtaq, M., Aljohani, N.R., Daud, A., Aslam, M.A., & Alowibdi, J.S. (2017). Mfs-lda: a multi-feature space tag recommendation model for cold start problem. Program, 51, 218–234. https://doi.org/10.1108/PROG-01-2017-0002.

    Article  Google Scholar 

  • Mazumdar, P., Patra, B.K., & Babu, K.S. (2020). Cold-start point-of-interest recommendation through crowdsourcing. ACM Transactions on the Web, 14.

  • Mirbakhsh, N., & Ling, C.X. (2015). Improving top-n recommendation for cold-start users via cross-domain information. ACM Transactions on Knowledge Discovery from Data, 9, 1–19. https://doi.org/10.1145/2724720.

    Article  Google Scholar 

  • Misztal-Radecka, J., Indurkhya, B., & Smywiński-Pohl, A. (2021). Meta-user2vec model for addressing the user and item cold-start problem in recommender systems. User Modeling and User-Adapted Interaction, 31, 261–286. https://doi.org/10.1007/s11257-020-09282-4.

    Article  Google Scholar 

  • Mobasher, B., Burke, R., Bhaumik, R., & Williams, C. (2007). Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Transactions on Internet Technology, 7.

  • Movahedian, H., & Khayyambashi, M.R. (2014). Folksonomy-based user interest and disinterest profiling for improved recommendations: An ontological approach. Journal of Information Science, 40, 594–610. https://doi.org/10.1177/0165551514539870.

    Article  Google Scholar 

  • Movahedian, H., & Khayyambashi, M.R. (2014). A semantic recommender system based on frequent tag pattern. Intelligent Data Analysis, 19, 109–126. https://doi.org/10.3233/IDA-140699.

    Article  Google Scholar 

  • Natarajan, S., Vairavasundaram, S., Natarajan, S., & Gandomi, A.H. (2020). Resolving data sparsity and cold start problem in collaborative filtering recommender system using linked open data. Expert Systems with Applications, 149.

  • Nguyen, P., Wang, J., & Kalousis, A. (2016). Factorizing lambdamart for cold start recommendations. Machine Learning, 104, 223–242. https://doi.org/10.1007/s10994-016-5579-3.

    Article  MathSciNet  MATH  Google Scholar 

  • Nguyen, V.D., Sriboonchitta, S., & Huynh, V.N. (2017). Using community preference for overcoming sparsity and cold-start problems in collaborative filtering system offering soft ratings. Electronic Commerce Research and Applications, 26, 101–108. https://doi.org/10.1016/j.elerap.2017.10.002.

    Article  Google Scholar 

  • Nie, D.C., Zhang, Z.K., Dong, Q., Sun, C., & Fu, Y. (2014). Information filtering via biased random walk on coupled social network. The Scientific World Journal, 2014.

  • Nouh, R.M., Lee, H.H., Lee, W.J., & Lee, J.D. (2019). A smart recommender based on hybrid learning methods for personal well-being services. Sensors (Switzerland), 19.

  • Ojagh, S., Malek, M.R., & Saeedi, S. (2020). A social-aware recommender system based on user’s personal smart devices. ISPRS International Journal of Geo-Information, 9.

  • Okoli, C. (2015). A guide to conducting a standalone systematic literature review. Communications of the Association for Information Systems, 37, 879–910. https://doi.org/10.17705/1cais.03743.

    Article  Google Scholar 

  • Paleti, L., Krishna, P.R., & Murthy, J.V. (2021). Approaching the cold-start problem using community detection based alternating least square factorization in recommendation systems. Evolutionary Intelligence, 14, 835–849. https://doi.org/10.1007/s12065-020-00464-y.

    Article  Google Scholar 

  • Pan, R., Ge, C., Zhang, L., Zhao, W., & Shao, X. (2020). A new similarity model based on collaborative filtering for new user cold start recommendation. IEICE Transactions on Information and Systems E103D, 1388–1394.

  • Pappas, N., & Popescu-Belis, A. (2015). Combining content with user preferences for non-fiction multimedia recommendation: a study on ted lectures. Multimedia Tools and Applications, 74, 1175–1197. https://doi.org/10.1007/s11042-013-1840-y.

    Article  Google Scholar 

  • Peng, F., Lu, J., Wang, Y., Xu, R.Y.D., Ma, C., & Yang, J. (2016). N-dimensional Markov random field prior for cold-start recommendation. Neurocomputing, 191, 187–199. https://doi.org/10.1016/j.neucom.2015.12.099.

    Article  Google Scholar 

  • Pereira, A.L.V., & Hruschka, E.R. (2015). Simultaneous co-clustering and learning to address the cold start problem in recommender systems. Knowledge-Based Systems, 82, 11–19. https://doi.org/10.1016/j.knosys.2015.02.016.

    Article  Google Scholar 

  • Polohakul, J., Chuangsuwanich, E., Suchato, A., & Punyabukkana, P. (2021). Real estate recommendation approach for solving the item cold-start problem. IEEE Access, 9, 68139–68150.

    Article  Google Scholar 

  • Pradhan, T., & Pal, S. (2020). A hybrid personalized scholarly venue recommender system integrating social network analysis and contextual similarity. Future Generation Computer Systems, 110, 1139–1166. https://doi.org/10.1016/j.future.2019.11.017.

    Article  Google Scholar 

  • Puthiya Parambath, S.A., & Chawla, S. (2020). Simple and effective neural-free soft-cluster embeddings for item cold-start recommendations. Data Mining and Knowledge Discovery, 34, 1560–1588. https://doi.org/10.1007/s10618-020-00708-6.

    Article  Google Scholar 

  • Ralph, D., Li, Y., Wills, G., & Green, N.G. (2020). Recommendations from cold starts in big data. Computing, 102, 1323–1344. https://doi.org/10.1007/s00607-020-00792-y.

    Article  MathSciNet  Google Scholar 

  • Richa, BP. (2020). Combining trust and reputation as user influence in cross domain group recommender system (cdgrs). Journal of Intelligent and Fuzzy Systems, 38, 6235–6246. https://doi.org/10.3233/JIFS-179705.

    Article  Google Scholar 

  • Rodpysh, K.V., Mirabedini, S.J., & Banirostam, T. (2021). Employing singular value decomposition and similarity criteria for alleviating cold start and sparse data in context-aware recommender systems. Electronic Commerce Research.

  • Rodríguez, R. M., Espinilla, M., Sánchez, P. J., & Martínez-López, L. (2010). Using linguistic incomplete preference relations to cold start recommendations. Internet Research, 20, 296–315. https://doi.org/10.1108/10662241011050722.

    Article  Google Scholar 

  • Rohani, V.A., Kasirun, Z.M., Kumar, S., & Shamshirband, S. (2014). An effective recommender algorithm for cold-start problem in academic social networks.Mathematical Problems in Engineering, 2014.

  • Rosli, A.N., You, T., Ha, I., Chung, K.Y., & Jo, G.S. (2015). Alleviating the cold-start problem by incorporating movies facebook pages. Cluster Computing, 18, 187–197. https://doi.org/10.1007/s10586-014-0355-2https://doi.org/10.1007/s10586-014-0355-2https://doi.org/10.1007/s10586-014-0355-2.

    Article  Google Scholar 

  • Sarwar, B., Karypis, G., & Konstan, J. (2001). Item-based collaborative filtering recommendation. GroupLens Research Group/Army HPC Research Center Department of Computer Science and Engineering, pp 286–295.

  • Shambour, Q., & Lu, J. (2011). A hybrid multi-criteria semantic-enhanced collaborative filtering approach for personalized recommendations. In Proceedings of the IEEE / WIC/ ACM international conferences on web intelligence and intelligent agent technology. IEEE. https://doi.org/10.1109/wi-iat.2011.109.

  • Shi, L., Zhao, W.X., & Shen, Y.D. (2017). Local representative-based matrix factorization for cold-start recommendation. ACM Transactions on Information Systems, 36.

  • Silva, N., Carvalho, D., Pereira, A.C., Mourão, F., & Rocha, L. (2019). The pure cold-start problem: A deep study about how to conquer first-time users in recommendations domains. Information Systems, 80, 1–12. https://doi.org/10.1016/j.is.2018.09.001.

    Article  Google Scholar 

  • Son, L.H. (2014a). HU-FCF: a hybrid user-based fuzzy collaborative filtering method in recommender systems. Expert Systems with Application, 41(15), 6861-6870.

    Article  Google Scholar 

  • Son, L.H. (2014b). Optimizing municipal solid waste collection using chaotic particle swarm optimization in GIS based environments: a case study at Danang City, Vietnam. Expert Systems with Applications, 41(18), 8062–8074.

    Article  Google Scholar 

  • Son, L.H. (2015). Hu-fcf++: A novel hybrid method for the new user cold-start problem in recommender systems. Engineering Applications of Artificial Intelligence, 41, 207–222. https://doi.org/10.1016/j.engappai.2015.02.003.

    Article  Google Scholar 

  • Son, L.H. (2016). Dealing with the new user cold-start problem in recommender systems: A comparative review. Information Systems, 58, 87–104. https://doi.org/10.1016/j.is.2014.10.001.

    Article  Google Scholar 

  • Suryana, N., & Basari, A.S.B.H. (2018). An understanding and approach solution for cold start problem associated with recommender system: A literature review. Journal of Theoretical and Applied Information Technology, 15, 9. www.jatit.org.

    Google Scholar 

  • Tahmasebi, F., Meghdadi, M., Ahmadian, S., & Valiallahi, K. (2021). A hybrid recommendation system based on profile expansion technique to alleviate cold start problem. Multimedia Tools and Applications, 80, 2339–2354. https://doi.org/10.1007/s11042-020-09768-8.

    Article  Google Scholar 

  • Tarus, J.K., Niu, Z., & Yousif, A. (2017). A hybrid knowledge-based recommender system for e-learning based on ontology and sequential pattern mining. Future Generation Computer Systems, 72, 37–48. https://doi.org/10.1016/j.future.2017.02.049.

    Article  Google Scholar 

  • Torraco, R.J. (2016). Writing integrative reviews of the literature. International Journal of Adult Vocational Education and Technology, 7, 62–70. https://doi.org/10.4018/ijavet.2016070106.

    Article  Google Scholar 

  • Tsai, C.Y., Chiu, Y.F., & Chen, Y.J. (2021). A two-stage neural network-based cold start item recommender. Applied Sciences (Switzerland), 11.

  • Viktoratos, I., Tsadiras, A., & Bassiliades, N. (2018). Combining community-based knowledge with association rule mining to alleviate the cold start problem in context-aware recommender systems. Expert Systems with Applications, 101, 78–90. https://doi.org/10.1016/j.eswa.2018.01.044.

    Article  Google Scholar 

  • Wang, H., & Zhao, Y. (2020). Ml2e: Meta-learning embedding ensemble for cold-start recommendation. IEEE Access, 8, 165757–165768. https://doi.org/10.1109/ACCESS.2020.3022796.

    Article  Google Scholar 

  • Wang, X., Peng, Z., Wang, S., Yu, P.S., Fu, W., Xu, X., & Hong, X. (2020). Cdlfm: cross-domain recommendation for cold-start users via latent feature mapping. Knowledge and Information Systems, 62, 1723–1750. https://doi.org/10.1007/s10115-019-01396-5.

    Article  Google Scholar 

  • Wei, J., He, J., Chen, K., Zhou, Y., & Tang, Z. (2017). Collaborative filtering and deep learning based recommendation system for cold start items. Expert Systems with Applications, 69, 1339–1351. https://doi.org/10.1016/j.eswa.2016.09.040.

    Article  Google Scholar 

  • Wu, H., Wang, X., Peng, Z., & Li, Q. (2013). Div-clustering: Exploring active users for social collaborative recommendation. Journal of Network and Computer Applications, 36, 1642–1650. https://doi.org/10.1016/j.jnca.2013.02.016.

    Article  Google Scholar 

  • Wu, W., Chen, L., & Zhao, Y. (2018). Personalizing recommendation diversity based on user personality. User Modeling and User-Adapted Interaction, 28, 237–276. https://doi.org/10.1007/s11257-018-9205-x.

    Article  Google Scholar 

  • Xiao, J., Luo, M., Chen, J.-M., & Li, J.-J. (2015). An item based collaborative filtering system combined with genetic algorithms using rating behavior. Lecture notes in computer science (pp. 453–460). Springer International Publishing. https://doi.org/10.1007/978-3-319-22053-6_48.

  • Yadav, U., Duhan, N., & Bhatia, K.K. (2020). Dealing with pure new user cold-start problem in recommendation system based on linked open data and social network features. Mobile Information Systems, 2020.

  • Yu, Y., Wang, C., Wang, H., & Gao, Y. (2017). Attributes coupling based matrix factorization for item recommendation. Applied Intelligence, 46, 521–533. https://doi.org/10.1007/s10489-016-0841-8.

    Article  Google Scholar 

  • Yue, L., Sun, X.X., Gao, W.Z., Feng, G.Z., & Zhang, B.Z. (2018). Multiple auxiliary information based deep model for collaborative filtering. Journal of Computer Science and Technology, 33, 668–681. https://doi.org/10.1007/s11390-018-1848-x.

    Article  Google Scholar 

  • Zahid, A., Sharef, N.M., & Mustapha, A. (2020). Normalization-based neighborhood model for cold start problem in recommendation system. International Arab Journal of Information Technology, 17, 281–290. https://doi.org/10.34028/iajit/17/3/1.

    Article  Google Scholar 

  • Zhang, Q., Wu, D., Lu, J., Liu, F., & Zhang, G. (2017). A cross-domain recommender system with consistent information transfer. Decision Support Systems, 104, 49–63. https://doi.org/10.1016/j.dss.2017.10.002.

    Article  Google Scholar 

  • Zhang, Y., Ma, X., Wan, S., Abbas, H., & Guizani, M. (2018). Crossrec: Cross-domain recommendations based on social big data and cognitive computing. Mobile Networks and Applications, 23, 1610–1623. https://doi.org/10.1007/s11036-018-1112-1.

    Article  Google Scholar 

  • Zhang, Y., Shi, Z., Zuo, W., Yue, L., Liang, S., & Li, X. (2020). Joint personalized markov chains with social network embedding for cold-start recommendation. Neurocomputing, 386, 208–220. https://doi.org/10.1016/j.neucom.2019.12.046.

    Article  Google Scholar 

  • Zhang, Z., Dong, M., Ota, K., & Kudo, Y. (2020). Alleviating new user cold-start in user-based collaborative filtering via bipartite network. IEEE Transactions on Computational Social Systems, 7, 672–685. https://doi.org/10.1109/TCSS.2020.2971942.

    Article  Google Scholar 

  • Zhang, Z., Kudo, Y., Murai, T., & Ren, Y. (2020). Improved covering-based collaborative filtering for new users’ personalized recommendations. Knowledge and Information Systems, 62, 3133–3154. https://doi.org/10.1007/s10115-020-01455-2.

    Article  Google Scholar 

  • Zhang, Z., & Liu, H. (2015). Social recommendation model combining trust propagation and sequential behaviors. Applied Intelligence, 43, 695–706. https://doi.org/10.1007/s10489-015-0681-y.

    Article  Google Scholar 

  • Zhang, Z., Zhang, Y., & Ren, Y. (2020). Employing neighborhood reduction for alleviating sparsity and cold start problems in user-based collaborative filtering. Information Retrieval Journal, 23, 449–472. https://doi.org/10.1007/s10791-020-09378-w.

    Article  Google Scholar 

  • Zhang, Z.K., Liu, C., Zhang, Y.C., & Zhou, T. (2010). Solving the cold-start problem in recommender systems with social tags. Europhysics Letters.

  • Zhang, Z.P., Kudo, Y., Murai, T., & Ren, Y.G. (2019). Addressing complete new item cold-start recommendation: A niche item-based collaborative filtering via interrelationship mining. Applied Sciences (Switzerland), 9.

  • Zheng, X., Luo, Y., Xu, Z., Yu, Q., & Lu, L. (2016). Tourism destination recommender system for the cold start problem. KSII Transactions on Internet and Information Systems, 10, 3192–3212. https://doi.org/10.3837/tiis.2016.07.018.

    Article  Google Scholar 

  • Zou, H., Gong, Z., Zhang, N., Zhao, W., & Guo, J. (2015). Trustrank: A cold-start tolerant recommender system. Enterprise Information Systems, 9, 117–138. https://doi.org/10.1080/17517575.2013.804587.

    Article  Google Scholar 

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Panda, D.K., Ray, S. Approaches and algorithms to mitigate cold start problems in recommender systems: a systematic literature review. J Intell Inf Syst 59, 341–366 (2022). https://doi.org/10.1007/s10844-022-00698-5

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