A survey on recommendation systems for financial services

Sharaf, M.M.; Hemdan, Ezz El‐Din; El‐Sayed, Ayman; El‐Bahnasawy, Nirmeen A.

doi:10.1007/s11042-022-12564-1

Cited by 20 publications

(10 citation statements)

References 72 publications

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“…By combining the digital number of 32 criteria bits from the example in Figure 4, the binary number 01000000000101110000000010011100 represents the integer value 107,524,930,81010. Conversion from binary to base-10 is: 010000000001011100000000100111002 = (0 × 2 31 ) + (1 × 2 30 ) + (0 × 2 29 ) + (0 × 2 28 ) + (0 × 2 27 ) + (0 × 2 26 ) + (0 × 2 25 ) + (0 × 2 24 ) + (0 × 2 23 ) + (0 × 2 22 ) + (0 × 2 21 ) + (1 × 2 20 ) + (0 × 2 19 ) + (1 × 2 18 ) + (1 × 2 17 ) + (1 × 2 16 ) + (0 × 2 15 ) + (0 × 2 14 ) + (0 × 2 13 ) + (0 × 2 12 ) + (0 × 2 11 ) + (0 × 2 10 ) + (0 × 2 9 ) + (0 × 2 8 ) + (1 × 2 7 ) + (0 × 2 6 ) + (0 × 2 5 ) + (1 × 2 4 ) + (1 × 2 3 ) + (1 × 2 2 ) + (0 × 2 1 ) + (0 × 2 0 ) = 107524930810.…”

Section: Samplesmentioning

confidence: 99%

“…A recommender system contributes to the development of an original application and offers users subsequent actions based on a variety of factors and choices, such as user interests or preferences and user history [11,12]. The recommender system has become a valuable tool for resolving the problem of information overload, enabling users to make difficult decisions and meeting their needs in a variety of solutions, including digital commerce [13], finance [14], entertainment (film [15], music [16], and television shows [17]), travel [18], healthcare [19], energy [20], and education [5,21].…”

Section: Introductionmentioning

confidence: 99%

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Dynamic Multi-Criteria Decision Making of Graduate Admission Recommender System: AHP and Fuzzy AHP Approaches

2023

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The optimal management of personal resources impacts everyone’s quality of life. An investment in graduate education is a sustainable opportunity for improved outcomes in human life, including cognition, behavior, life opportunities, salary, and career. Advanced technology dramatically reduces the risk of personal resources in graduate program admission recommendations that depend on multiple individual needs and preferences. In the digital age, a dynamic recommender system enhances the suitably effective solution for students’ university selections. This study focused on designing, developing, and testing a recommender system for graduate admission using a dynamic multi-criteria AHP and fuzzy AHP approach. The explicit multi-criteria recommender system was a platform as a service (PaaS) web application created to aid in graduate admissions management and decision-making. The design proposed that the bit representation store a dynamic explicit multi-criteria data structure. The recommendations adopting dynamic multi-criteria were validated by comparing them to the programs to which the students were actually admitted and enrolled. They individually ranked the evaluation outcomes of dynamic explicit multi-criteria and alternative preferences to provide graduate admission recommendations. Eighty graduate students in information technology evaluated the recommender system. Using top-1, top-2, and F1-score accuracy, the effective system accuracy performance on the dynamic multi-criteria recommender system was evaluated using AHP and fuzzy AHP approaches. The fuzzy AHP demonstrated marginally greater practical accuracy than the AHP method.

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Section: Samplesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic Multi-Criteria Decision Making of Graduate Admission Recommender System: AHP and Fuzzy AHP Approaches

2023

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“…It is a concept, associated with Customer Relationship Management (CRM), aimed to establish a direct relationship (and specific conduct) between an organization and a specific customer. Such a relationship can ultimately make the customer loyal for a longer period [6][7][8]. Recommender Systems (RSs) are well-known examples of personalization in the e-commerce world, automatically offering proper products that match their characteristics and behaviors.…”

Section: Introductionmentioning

confidence: 99%

Explainable AI in big data intelligence of community detection for digitalization e-healthcare services

Sangaiah

Rezaei

Javadpour

et al. 2023

Applied Soft Computing

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“…Recently, most recommendations and decision-making systems are based on the prediction outcome, especially in the financial field. So, it is necessary to provide accurate predictions in the decision-making stage to help investors from saving their stocks [ 20 ]. Financial specialists assess the execution of a company sometime recently choosing to buy its stock to dodge buying unsafe stocks [ 12 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

“…So, it is necessary to provide accurate predictions in the decision-making stage to help investors from saving their stocks [ 20 ]. Financial specialists assess the execution of a company sometime recently choosing to buy its stock to dodge buying unsafe stocks [ 12 , 19 , 20 ]. This assessment incorporates an examination of the company’s execution on social media and budgetary news websites.…”

Section: Introductionmentioning

confidence: 99%

An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis

Sharaf

Hemdan

El‐Sayed

et al. 2022

Multimed Tools Appl

Self Cite

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The coronavirus is an irresistible virus that generally influences the respiratory framework. It has an effective impact on the global economy specifically, on the financial movement of stock markets. Recently, an accurate stock market prediction has been of great interest to investors. A sudden change in the stock movement due to COVID -19 appearance causes some problems for investors. From this point, we propose an efficient system that applies sentiment analysis of COVID-19 news and articles to extract the final impact of COVID-19 on the financial stock market. In this paper, we propose a stock market prediction system that extracts the stock movement with the COVID spread. It is important to predict the effect of these diseases on the economy to be ready for any disease change and protect our economy. In this paper, we apply sentimental analysis to stock news headlines to predict the daily future trend of stock in the COVID-19 period. Also, we use machine learning classifiers to predict the final impact of COVID-19 on some stocks such as TSLA, AMZ, and GOOG stock. For improving the performance and quality of future trend predictions, feature selection and spam tweet reduction are performed on the data sets. Finally, our proposed system is a hybrid system that applies text mining on social media data mining on the historical stock dataset to improve the whole prediction performance. The proposed system predicts stock movement for TSLA, AMZ, and GOOG with average prediction accuracy of 90%, 91.6%, and 92.3% respectively.

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A survey on recommendation systems for financial services

Cited by 20 publications

References 72 publications

Dynamic Multi-Criteria Decision Making of Graduate Admission Recommender System: AHP and Fuzzy AHP Approaches

Dynamic Multi-Criteria Decision Making of Graduate Admission Recommender System: AHP and Fuzzy AHP Approaches

Explainable AI in big data intelligence of community detection for digitalization e-healthcare services

An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis

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