Intelligent and Adaptive Web Page Recommender System

Rani, Geeta; Dhaka, Vijaypal Singh; Sonam, -; Pandey, Upasana; Tiwari, Pradeep Kumar

doi:10.4018/ijwsr.2021100102

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Consumers need more detailed face-to-face professional advice [11][12]. The combination of artificial intelligence technology and financial services to establish a personalized recommendation system, which can provide customized services for individual consumer needs, to develop a personalized recommendation program [13][14]. Moreover, the customized recommendation system of financial services can improve the quality of service, thereby increasing the loyalty and trust of consumers to the relevant financial institutions and financial products [15].…”

Section: Introductionmentioning

confidence: 99%

Application of AI technology in personalized recommendation system for financial services

Yue

2024

Applied Mathematics and Nonlinear Sciences

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How to quickly and accurately retrieve the products that users are interested in from a huge amount of financial products has become a business pain point that financial institutions must solve. In this study, an interpretable EPRSA model for personalized financial service recommendations based on self-attention mechanisms is proposed by combining an LSTM model and an LDA topic model with AI technology support. A customized recommendation system for financial services is constructed by introducing the Nginx server into the Flask framework, and the design of the database and personalized recommendation module is interpreted. For the financial service personalized recommendation system proposed in this paper, its recommendation performance and system performance are tested, and the stock financial products are selected as the recommendation objects to explore its recommendation effect. It is found that the DNCG index of personalized recommendation of financial products of the EPRSA model is improved by 40.18%, the average response time of the system when the number of concurrent users is 1,000 is 1.96 s. The average quality of the personalized recommendation of the collection of stocks reaches 0.153. The customized recommendation of financial services using AI technology can select financial products based on the investor’s preference, help investors better understand the product returns, and improve the service quality of the financial industry.

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Section: Introductionmentioning

confidence: 99%

Application of AI technology in personalized recommendation system for financial services

Yue

2024

Applied Mathematics and Nonlinear Sciences

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“…A set of recommendation algorithms for producing Web-page candidates has been provided as a result of these enquiries. The outcomes of the proposal were evaluated and contrasted with those of a more sophisticated earlier Web Usage Mining (WUM) technique [5][6].…”

Section: Iintroductionmentioning

confidence: 99%

Web Page Recommendation System by Integrating Ontology and Stemming Algorithm

Ayoobkhan¹,

Ali²

2022

IJASIS

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In this research, we offer a customized-recommendation system that uses item representations and user profiles based on the ontologies that provide personalized services to semantic applications. To develop and implement the personalized-recommendation system, a system that uses the representations of the items and the user profiles based on the ontologies to provide the semantic applications with personalized services. Recommendation systems can use semantic reasoning capabilities to overcome present system limits and increase the quality of recommendations. The recommender makes use of domain ontologies to improve personalization: on the one hand, a domain-based inference method is used to model user interests more effectively and accurately; on the other hand, a semantic similarity method is used to improve the stemmer algorithm, which is used by our content-based filtering approach, which provides a measure of the affinity between an item and the user. In recommender systems and web personalization, Web Usage Mining is crucial. This study presents an effective recommender system based on ontology and web usage mining. The approach's first step is to extract features from online documents and build on related ideas. Then, they create an ontology for the website using the concepts and relevant terms retrieved from the records. The semantic similarity of web documents is used to group them into multiple semantic themes, each with its own set of preferences. The suggested solution incorporates ontology and semantic knowledge into Web Usage Mining and personalization procedures, as well as a stemming algorithm, and gets an overall accuracy of 90%.

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Intelligent and Adaptive Web Page Recommender System

Cited by 2 publications

References 16 publications

Application of AI technology in personalized recommendation system for financial services

Application of AI technology in personalized recommendation system for financial services

Web Page Recommendation System by Integrating Ontology and Stemming Algorithm

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