Personalized Intelligent Recommendation Algorithm Design for Book Services Based on Deep Learning

Yang, Weiwei

doi:10.1155/2022/9203665

Cited by 6 publications

(6 citation statements)

References 22 publications

(27 reference statements)

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“…It is a meaningful work to help the scholars focus on how to mine users' preferences and interests so as to satisfy them through recommendation services. Therefore, personalized digital library resource recommendation technology has gradually become a hot research point in the field of education and intelligence [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Personalized Book Recommendation Algorithm for University Library Based on Deep Learning Models

Hou

2022

Journal of Sensors

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Personalized recommendation is one of the important contents of personalized service in university libraries. Accurate and in-depth understanding of users is the premise of personalized recommendation. This paper proposes a personalized book recommendation algorithm based on deep learning models according to the characteristics and laws of user savings in university libraries. The method first uses the long short-term memory network (LSTM) to improve the deep autoencoder (DAE) so that the model can extract the temporal features of the data. Then, the Softmax function is used to obtain the book recommendation result of the current user. The proposed method is verified based on actual library lending data. The experimental results show that the proposed method has performance advantages compared with several existing recommendation methods.

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Section: Introductionmentioning

confidence: 99%

Personalized Book Recommendation Algorithm for University Library Based on Deep Learning Models

Hou

2022

Journal of Sensors

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“…Many libraries and information services already use AI in their operations (Alam, 2022; Hussain, 2023). In particular, machine learning technologies are being used for activities like automatic resource categorization and classification; collection recognition and weeding; automated cataloguing and data entry; personalized suggestions based on users’ search and lending histories and other data (Cordell, 2020; Gul and Bano, 2019; Pekala, 2017; Yang, 2022); and the application of voice assistants, virtual assistants and chatbots to educational institutes’ library services (Mckie and Narayan, 2019).…”

Section: Introductionmentioning

confidence: 99%

A study on the knowledge and perception of artificial intelligence

Subaveerapandiyan,

Sunanthini,

Amees

2023

IFLA Journal

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This study investigated the knowledge and perception of artificial intelligence among library and information science professionals in Zambia by surveying 245 randomly selected participants. The results show that the library and information science professionals had a favourable and encouraging outlook with regard to artificial intelligence but were also concerned about artificial intelligence replacing librarians’ roles and the barriers to adopting artificial intelligence technologies in Zambian libraries. The article suggests that libraries should consider the study’s findings before implementing artificial intelligence, particularly concerning technology and facilities, librarians’ proficiency in artificial intelligence, and leadership positions in artificial intelligence initiatives. The research can be used as a resource by library boards and associations to develop policies for implementing artificial intelligence in academic libraries, and fills a research vacuum in developing nations like Zambia regarding the knowledge of university and college libraries, and their willingness to use artificial intelligence.

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“…E-commerce companies are in unprecedented contact with users through the Internet, and all marketing strategies are closely focused on the needs of users. e rst time to grasp the rich user information and understand the needs of users, you can accurately grasp the pulse of market changes and control the highest point of competition [2]. Apart from that, because the sites in the eld of e-commerce are built based on the Internet and the convenience of using the Internet to obtain information, each site can provide very personalized, transparent, and humanized recommendations [3].Combining the above two reasons, the personalized service of e-commerce is getting more and more attention.…”

Section: Introductionmentioning

confidence: 99%

Improvement of Business Analysis Method of E-Commerce System from the Perspective of Intelligent Recommendation System

Shao

2022

Advances in Multimedia

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In recent years, with the continuous development of the country’s Internet platforms, China has gradually entered the e-commerce era of national online shopping, and more and more e-commerce platforms and stores have adopted intelligent recommendation systems to increase transaction rates. However, it is not easy for consumers to filter out the products they want from a large amount of information. The emergence of intelligent recommendation systems provides great convenience for people to screen out personalized products that meet their own characteristics. However, the algorithms used in traditional recommendation technology focus on the single-computer environment and do not consider the performance of the recommendation method when distributed parallel processing is required in the big data environment, so it cannot meet the personalized needs of users in the big data environment. Aiming at the new requirements for the development of e-commerce intelligent recommendation technology in the big data environment, this paper uses the big data processing technology based on cloud computing and focuses on the realization technology of the e-commerce intelligent recommendation algorithm and the comprehensive evaluation method of the recommendation system in the big data environment. A prototype system of personalized intelligent recommendation based on cloud computing has been developed, which is of great importance to meet the needs of e-commerce personalized intelligent recommendation in the big data environment, improve the effectiveness, scale, and real-time performance of the personalized intelligent recommendation system, and improve the level of personalized precision marketing., which is of theoretical significance and economic value.

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Personalized Intelligent Recommendation Algorithm Design for Book Services Based on Deep Learning

Cited by 6 publications

References 22 publications

Personalized Book Recommendation Algorithm for University Library Based on Deep Learning Models

Personalized Book Recommendation Algorithm for University Library Based on Deep Learning Models

A study on the knowledge and perception of artificial intelligence

Improvement of Business Analysis Method of E-Commerce System from the Perspective of Intelligent Recommendation System

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