A study of a personalized library recommendation algorithm

Abstract

The traditional library recommendation method mainly relies on manual analysis of readers’ reading history, book classification, and user feedback to achieve personalized recommendation, but this method has limitations such as low efficiency and difficulty in accurately capturing users’ complex needs. In view of this, this paper innovatively designs a recommendation system based on Wide & Deep model. The system utilizes a linear model (Wide part) to process hand-constructed features as a way to remember specific user behavioural patterns; at the same time, high-dimensional sparse features are processed with the help of a deep neural network (Deep part) to capture the complex relationship between user interests. After both outputs are further processed by the fully connected layer, the model outputs the probability distribution of each category through Softmax function and then predicts the book categories that users may be interested in. The experimental results show that the model has excellent performance, with an AUC score of over 99%, showing a strong differentiation ability; precision is close to 97%, recall is close to 96%, and F1 value is close to 54%, which not only can accurately recommend the book resources that the user is interested in but also can effectively cover the diversified needs of the user, and provide a better solution for personalized recommendation service in libraries.

Keywords

personalized recommendations book resources depth and width models book recommendation system deep learning

Get full access to this article

View all access options for this article.

References

Liu

Quan Quan

. Personalised book recommendation system for colleges and universities based on improved user collaborative filtering algorithm. Computer and Digital Engineering 2020; 48(10): 2458–2461.

Kunlun

meng

Zhi Bo

, et al. An improved collaborative filtering recommendation algorithm incorporating trustworthiness values and semi-supervised density peak clustering. Small Microcomputer Systems 2020; 41(08): 1613–1619.

Fang Qing

Liu

, et al. Fusion of context information and kernel density estimation for collaborative filtering recommendation. Computer Technology and Development 2021; 31(04): 34–39.

. A personalised recommendation method for network information resources based on generalised regression neural network. Information and Computer (Theoretical Edition) 2023; 35(05): 38–40.

Roy

Dutta

. A systematic review and research perspective on recommender systems. J Big Data 2022; 9(1): 59.

Raj

Renumol

. A rule-based approach for adaptive content recommendation in a personalised learning environment: an experimental analysis [C]. In: 2019 IEEE tenth international conference on technology for education (T4E). Piscataway: IEEE, 2019.

Antony Rosewelt

Arokia Renjit

. A content recommendation system for effective e-learning using embedded feature selection and fuzzy DT based CNN. J Intell Fuzzy Syst 2020; 39(1): 795–808.

Wang

, et al. A survey on accuracy-oriented neural recommendation: From collaborative filtering to information-rich recommendation. IEEE Trans Knowl Data Eng 2022; 35(5): 4425–4445.

Latrech

Kodia

Ben Azzouna

. CoDFi-DL: a hybrid recommender system combining enhanced collaborative and demographic filtering based on deep learning. J Supercomput 2024; 80(1): 1160–1182.

10.

Feng

Fan

. Research on learning behavior patterns from the perspective of educational data mining: Evaluation, prediction and visualization. Expert Syst Appl 2024; 237: 121555.

11.

Iftikhar

Ghazanfar

Ayub

, et al. A reinforcement learning recommender system using bi-clustering and Markov Decision Process. Expert Syst Appl 2024; 237: 121541.

12.

Jiang

Ding

Zhang

. A personalized recommendation algorithm based on weighted information entropy and particle swarm optimization. Mob Inf Syst 2021; 1: 3209140–3209149.

13.

Liu

. Simulation of E-learning in English personalized learning recommendation system based on Markov chain algorithm and adaptive learning algorithm. Entertainment Computing 2024; 51: 100719.

14.

Santos-Hermosa

Atenas

. Building capacities in open knowledge: recommendations for library and information science professionals and schools. Front Educ 2022; 7: 866049.