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A survey on recommendation systems for financial services

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Abstract

Recently, there is difficulty in extracting useful information from huge online information due to the rapid growth of the internet. Therefore, the Recommendation system (RS) is needed for the improvement of many services such as entertainment, e-commerce, healthcare, and financial services. It is an effective tool in the service industry, as it is used for guiding users to an interesting thing from the large space of random things. A recommendation system can discover patterns in input movements and generating system recommendations based on the patterns, thus it can significantly supplement the decision-making process of a stock trader. So, there are many methods for the recommendation process such as collaborative filtering, content-based and hybrid recommendations. Recommendation algorithm can be selected based on the existing research problem. This paper presents a review of the recommendation system, its types, and its applications. Then, this paper concentrated on the finance recommendation system, its operation, and its different finance sectors.

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References

  1. Agarwal V, Vijayalakshmi A (2019) Recommender system for surplus stock clearance. Int J Electrical Comput Eng 9(5):3813

    Google Scholar 

  2. Al-Bashiri H, Abdulgabber MA, Romli A, Kahtan H (2018) An improved memory-based collaborative filtering method based on the TOPSIS technique. PLoS One 13(10):e0204434

    Article  Google Scholar 

  3. Alhijawi B, Kilani Y (2020) A collaborative filtering recommender system using genetic algorithm. Inf Process Manag 57(6):102310

    Article  Google Scholar 

  4. Ali F, Islam SR, Kwak D, Khan P, Ullah N, Yoo SJ, Kwak KS (2018) Type-2 fuzzy ontology–aided recommendation systems for IoT–based healthcare. Comput Commun 119:138–155

    Article  Google Scholar 

  5. Alshammari G, Jorro-Aragoneses JL, Kapetanakis S, Polatidis N, Petridis M (2019) A switching approach that improves prediction accuracy for long tail recommendations. In proceedings of SAI intelligent systems conference (pp. 18-28). Springer, Cham.

  6. Atauchi PD, Nedel L, Galante R (2019) Broker-insights: An interactive and visual recommendation system for insurance brokerage. In computer graphics international conference (pp. 155-166). Springer, Cham.

  7. Babu MSP, Kumar BRS (2011) An implementation of the user-based collaborative filtering algorithm. IJCSIT) Int J Comp Sci Inform Technol 2(3):1283–1286

  8. Barathy R, Chitra P (2020) Applying matrix factorization in collaborative filtering recommender systems. In 2020 6th international conference on advanced computing and communication systems (ICACCS) (pp. 635-639). IEEE.

  9. Barros M, Moitinho A, Couto FM (2020) Hybrid semantic recommender system for chemical compounds. In European conference on information retrieval (pp. 94-101). Springer, Cham.

  10. Barua U, Hossain M, Arefin MS (2019) Real estate recommendation using historical data and surrounding environments. Int J Inform Eng Electron Bus 11(5):33–39

    Google Scholar 

  11. Benzarti I, Mili H, Paillard A (2019) A content based e-commerce recommendation approach under the Veristic framework. In international conference on e-business engineering (pp. 495-514). Springer, Cham.

  12. Civan Z, Simsek GG, Akay EC (2020) Identifying the systemically important banks of Turkey with the CoVaR method. Heliyon 6(9):e04790

    Article  Google Scholar 

  13. Dara S, Chowdary CR, Kumar C (2020) A survey on group recommender systems. J Intell Inf Syst 54(2):271–295

    Article  Google Scholar 

  14. Da'u A, Salim N, Rabiu I, Osman A (2020) Recommendation system exploiting aspect-based opinion mining with deep learning method. Inf Sci 512:1279–1292

    Article  Google Scholar 

  15. De Campos LM, Fernández-Luna JM, Huete JF, Rueda-Morales MA (2010) Combining content-based and collaborative recommendations: a hybrid approach based on Bayesian networks. Int J Approx Reason 51(7):785–799

    Article  Google Scholar 

  16. Desirena G, Diaz A, Desirena J, Moreno I, Garcia D (2019) Maximizing customer lifetime value using stacked neural networks: An insurance industry application. In 2019 18th IEEE international conference on machine learning and applications (ICMLA) (pp. 541-544). IEEE.

  17. Ding X, Yu W, Xie Y, Liu S (2020) Efficient model-based collaborative filtering with fast adaptive PCA. arXiv preprint arXiv:2009.02251.

  18. Dooms S, De Pessemier T, Martens L (2015) Offline optimization for user-specific hybrid recommender systems. Multimed Tools Appl 74(9):3053–3076

    Article  Google Scholar 

  19. Erkek M, Çayırlı K, Taş H, Hepsen A, Aytekin T (2020) Recommendation systems applied in Turkish real estate market. J Comput Modell 10(1):1–10

    Google Scholar 

  20. Fu Z, Xian Y, Zhang Y, Zhang Y (2020) Tutorial on conversational recommendation systems. In fourteenth ACM conference on recommender systems (pp. 751-753).

  21. Fu Z, Xian Y, Zhang Y, Zhang Y (2020) Tutorial on conversational recommendation systems. In fourteenth ACM conference on recommender systems (pp. 751-753).

  22. Ghazanfar M, Prugel-Bennett A (2010) An improved switching hybrid recommender system using naive Bayes classifier and collaborative filtering

  23. Ghazanfar M, Prugel-Bennett A (2010) Building switching hybrid recommender system using machine learning classifiers and collaborative filtering. IAENG Int J Comput Sci 37(3)

  24. Guo Y, Zhou Y, Hu X, Cheng W (2019) Research on recommendation of insurance products based on random Forest. In 2019 international conference on machine learning, big data and business intelligence (MLBDBI) (pp. 308-311). IEEE.

  25. Henrique BM, Sobreiro VA, Kimura H (2019) Literature review: machine learning techniques applied to financial market prediction. Expert Syst Appl 124:226–251

    Article  Google Scholar 

  26. Isinkaye FO, Folajimi YO, Ojokoh BA (2015) Recommendation systems: principles, methods and evaluation. Egyptian Inform J 16(3):261–273

    Article  Google Scholar 

  27. Kaššák O, Kompan M, Bieliková M (2016) Personalized hybrid recommendation for group of users: top-N multimedia recommender. Inf Process Manag 52(3):459–477

    Article  Google Scholar 

  28. Katarya R, Verma OP (2017) An effective collaborative movie recommender system with cuckoo search. Egyptian Inform J 18(2):105–112

    Article  Google Scholar 

  29. Khan Z, Iltaf N, Afzal H, Abbas H (2020) DST-HRS: a topic driven hybrid recommender system based on deep semantics. Comput Commun 156:183–191

    Article  Google Scholar 

  30. Kiran R, Kumar P, Bhasker B (2020) DNNRec: a novel deep learning based hybrid recommender system. Expert Syst Appl 144:113054

    Article  Google Scholar 

  31. Kowsher M, Prottasha NJ, Tahabilder A, Islam MB (2020) Machine learning based recommendation Systems for the Mode of childbirth. In international conference on cyber security and computer science (pp. 295-306). Springer, Cham.

  32. Kumar NP, Fan Z (2015) Hybrid user-item based collaborative filtering. Procedia Comput Sci 60:1453–1461

    Article  Google Scholar 

  33. Kumar PV, Reddy VR (2014) A survey on recommender systems (RSS) and its applications. Int J Innov Res Comput Commun Eng 2(8):5254–5260

    Google Scholar 

  34. Lampropoulos AS, Lampropoulou PS, Tsihrintzis GA (2012) A cascade-hybrid music recommender system for mobile services based on musical genre classification and personality diagnosis. Multimed Tools Appl 59(1):241–258

    Article  Google Scholar 

  35. Lesage L, Deaconu M, Lejay A, Meira JA, Nichil G (2020) A recommendation system for car insurance. Eur Actuar J 10:1–22

    Article  MathSciNet  MATH  Google Scholar 

  36. Levinas CA (2014) An analysis of memory based collaborative filtering recommender systems with improvement proposals. Master's thesis, Universitat Politècnica de Catalunya

  37. Li C, Wang Z, Cao S, He L (2018) WLRRS: a new recommendation system based on weighted linear regression models. Comput Electrical Eng 66:40–47

    Article  Google Scholar 

  38. Lian M, Li J (2020) Financial product recommendation system based on transformer. In 2020 IEEE 4th information technology, networking, electronic and automation control conference (ITNEC) (Vol. 1, pp. 2547-2551). IEEE.

  39. Liu C, Wang H (2019) Stock recommendation based on depth BRNN and bi-LSTM. In 2019 4th international conference on mechanical, control and computer engineering (ICMCCE) (pp. 751-7514). IEEE.

  40. Logesh R, Subramaniyaswamy V, Malathi D, Sivaramakrishnan N, Vijayakumar V (2020) Enhancing recommendation stability of collaborative filtering recommender system through bio-inspired clustering ensemble method. Neural Comput amp; Applic 32(7):2141–2164

    Article  Google Scholar 

  41. Lops P, Jannach D, Musto C, Bogers T, Koolen M (2019) Trends in content-based recommendation. User Model User-Adap Inter 29(2):239–249

    Article  Google Scholar 

  42. Mathew PS, Pillai AS (2015) Big data solutions in healthcare: problems and perspectives. In 2015 international conference on innovations in information, embedded and communication systems (ICIIECS) (pp. 1-6). IEEE.

  43. Mican D, Sitar-Tăut DA, Moisescu OI (2020) Perceived usefulness: a silver bullet to assure user data availability for online recommendation systems. Decision Support Syst 139:113420

    Article  Google Scholar 

  44. Mokarrama MJ, Khatun S, Arefin MS (2020) A content-based recommender system for choosing universities. Turkish J Electrical Eng Comput Sci 28(4):2128–2142

    Article  Google Scholar 

  45. Montesi G, Papiro G, Fazzini M, Ronga A (2020) Stochastic optimization system for Bank reverse stress testing. JRisk Financial Manag 13(8):174

    Article  Google Scholar 

  46. Narayanan M, Cherukuri AK (2016) A study and analysis of recommendation systems for location-based social network (LBSN) with big data. IIMB Manag Rev 28(1):25–30

    Article  Google Scholar 

  47. Nawi RM, Noah SM, Zakaria LQ (2020) Evaluation of group modelling strategy in model-based collaborative filtering recommendation. Int J Mach Learn Comput 10(2):330–338

    Article  Google Scholar 

  48. Nguyen NC, Duong HC, Nguyen HT, Chen SS, Le HQ, Ngo HH, … Bui XT (2020) Forward osmosis–membrane distillation hybrid system for desalination using mixed trivalent draw solution. J Membrane Sci 603:118029

    Article  Google Scholar 

  49. Nieves EH (2020) New approach to recommend banking products through a hybrid recommender system. In international symposium on ambient intelligence (pp. 262-266). Springer, Cham.

  50. Ortiz Viso B (2020) Evolutionary approach in recommendation Systems for Complex Structured Objects. In fourteenth ACM conference on recommender systems (pp. 776-781).

  51. Oyebode O, Orji R (2020) A hybrid recommender system for product sales in a banking environment. J Banking Financial Technol 4:1–11

    Article  Google Scholar 

  52. Pan Y, He F, Yu H (2019) A novel enhanced collaborative autoencoder with knowledge distillation for top-N recommender systems. Neurocomputing 332:137–148

    Article  Google Scholar 

  53. Pan Y, He F, Yu H (2020) Learning social representations with deep autoencoder for recommender system. World Wide Web 23(4):2259–2279

    Article  Google Scholar 

  54. Pan Y, He F, Yu H (2020) A correlative denoising autoencoder to model social influence for top-N recommender system. Front Comput Sci 14(3):1–13

    Article  Google Scholar 

  55. Rana SP, Dey M, Prieto J, Dudley S (2020) Content-based health recommender systems. Recommender system with machine learning and artificial intelligence: practical tools and applications in medical, agricultural and other industries, 215.

  56. Rehman F, Masood H, Ul-Hasan A, Nawaz R, Shafait F (2019) An intelligent context aware recommender system for real-estate. In Mediterranean conference on pattern recognition and artificial intelligence (pp. 177-191). Springer, Cham.

  57. Ricci F, Rokach L, Shapira B (2011) Introduction to recommender systems handbook, In recommender systems handbook (pp. 1–35). Springer, Boston, MA

  58. Riyahi M, Sohrabi MK (2020) Providing effective recommendations in discussion groups using a new hybrid recommender system based on implicit ratings and semantic similarity. Electron Commer Res Appl 40:100938

    Article  Google Scholar 

  59. Sabbah T, Selamat A, Selamat MH, Ibrahim R, Fujita H (2016) Hybridized term-weighting method for dark web classification. Neurocomputing 173:1908–1926

    Article  Google Scholar 

  60. Sezgin E, Özkan S (2013) A systematic literature review on health recommender systems. In 2013 E-health and bioengineering conference (EHB) (pp. 1-4). IEEE.

  61. Shah J, Sahu L (2014) A survey of various hybrid based recommendation method. Int J Advanc Res Comput Sci Software Eng 4:369–371

    Google Scholar 

  62. Shao B, Li X, Bian G (2020) A survey of research hotspots and frontier trends of recommendation systems from the perspective of knowledge graph. Expert Syst Appl 113764

  63. Sharifihosseini A (2019) A case study for presenting Bank recommender systems based on bon card transaction data. In 2019 9th international conference on computer and knowledge engineering (ICCKE) (pp. 72-77). IEEE.

  64. Soares M, Viana P (2015) Tuning metadata for better movie content-based recommendation systems. Multimed Tools Appl 74(17):7015–7036

    Article  Google Scholar 

  65. Sun Y, Fang M, Wang X (2018) A novel stock recommendation system using Guba sentiment analysis. Pers Ubiquit Comput 22(3):575–587

    Article  Google Scholar 

  66. Tas H, Sumnu HE, Gokoz B, Aytekin T Dev Hybrid Real Estate Recomm Syst

  67. Vismayaa V, Pooja KR, Alekhya A, Malavika CN, Nair BB, Kumar PN (2020) Classifier based stock trading recommender systems for Indian stocks: An empirical evaluation. Comput Econ 55(3):901–923

    Article  Google Scholar 

  68. Wang H, Ma S, Dai HN, Imran M, Wang T (2020) Blockchain-based data privacy management with nudge theory in open banking. Futur Gener Comput Syst 110:812–823

    Article  Google Scholar 

  69. Wang W, Mishra KK (2018) A novel stock trading prediction and recommendation system. Multimed Tools Appl 77(4):4203–4215

    Article  Google Scholar 

  70. Wei S, Ye N, Zhang S, Huang X, Zhu J (2012) Item-based collaborative filtering recommendation algorithm combining item category with interestingness measure. In 2012 international conference on computer science and service system (pp. 2038-2041). IEEE.

  71. Wen H, Zhang J, Lin Q, Yang K, Huang P (2019) Multi-level deep cascade trees for conversion rate prediction in recommendation system. In proceedings of the AAAI conference on artificial intelligence (Vol. 33, pp. 338-345).

  72. Xia H, Wei X, An W, Zhang ZJ, Sun Z (2020) Design of electronic-commerce recommendation systems based on outlier mining. Electron Markets. 1-17.

  73. Xue J, Zhu E, Liu Q, Yin J (2018) Group recommendation based on financial social network for robo-advisor. IEEE Access 6:54527–54535

    Article  Google Scholar 

  74. Yadav S, Nagpal S (2018) An improved collaborative filtering based recommender system using bat algorithm. Procedia Comput Sci 132:1795–1803

    Article  Google Scholar 

  75. Yin C, Shi L, Sun R, Wang J (2020) Improved collaborative filtering recommendation algorithm based on differential privacy protection. J Supercomput 76(7):5161–5174

    Article  Google Scholar 

  76. Ying Y, Chen L, Chen G (2017) A temporal-aware POI recommendation system using context-aware tensor decomposition and weighted HITS. Neurocomputing 242:195–205

    Article  Google Scholar 

  77. Yu K, Xu X, Schwaighofer A, Tresp V, Kriegel HP (2002) Removing redundancy and inconsistency in memory-based collaborative filtering. In conference on information and knowledge management: proceedings of the eleventh international conference on information and knowledge management (Vol. 4, no. 09, pp. 52-59).

  78. Yu K, Schwaighofer A, Tresp V, Xu X, Kriegel HP (2004) Probabilistic memory-based collaborative filtering. IEEE Trans Knowl Data Eng 16(1):56–69

    Article  Google Scholar 

  79. Zhang Q, Zhang D, Lu J, Zhang G, Qu W, Cohen M (2019) A recommender system for cold-start items: a case study in the real estate industry. In 2019 IEEE 14th international conference on intelligent systems and knowledge engineering (ISKE) (pp. 1185-1192). IEEE.

  80. Zhang X, Zhou Y, Ma Y, Chen BC, Zhang L, Agarwal D (2016) Glmix: generalized linear mixed models for large-scale response prediction. In proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 363-372).

  81. Zhang Y, Qiu M, Tsai CW, Hassan MM, Alamri A (2015) Health-CPS: healthcare cyber-physical system assisted by cloud and big data. IEEE Syst J 11(1):88–95

    Article  Google Scholar 

  82. Zheng Z, Gao Y, Yin L, Rabarison MK (2019) Modeling and analysis of a stock-based collaborative filtering algorithm for the Chinese stock market. Expert Syst Appl 162:113006

    Article  Google Scholar 

  83. Zibriczky D (2016). Recommender systems meet finance: a literature review

  84. Zuo T, Zhu S, Lu J (2020) A hybrid recommender system combing singular value decomposition and linear mixed model. In science and information conference (pp. 347-362). Springer, Cham.

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  1. Computer Science and Engineering Department, Faculty of Electronic Engineering, Menoufia University, Menoufia, Egypt

    Marwa Sharaf, Ezz El-Din Hemdan, Ayman El-Sayed & Nirmeen A. El-Bahnasawy

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  1. Marwa Sharaf
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Sharaf, M., Hemdan, E.ED., El-Sayed, A. et al. A survey on recommendation systems for financial services. Multimed Tools Appl 81, 16761–16781 (2022). https://doi.org/10.1007/s11042-022-12564-1

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