Deep-Cross-Attention Recommendation Model for Knowledge Sharing Micro Learning Service

Lin, Jiayin; Sun, Geng; Shen, Jun; Pritchard, David E.; Cui, Tingru; Xu, Dongming; Li, Li; Beydoun, Ghassan; Chen, Jinjun

doi:10.1007/978-3-030-52240-7_31

Cited by 9 publications

(6 citation statements)

References 20 publications

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“…Wisdom service has the characteristics of efficiency, personalization, and diversification, and personalized service is the core of library wisdom service [18]. e personalized wisdom service is user-centered and user-driven, with the characteristics of contextuality, knowledge, initiative, and real-time, reflecting humanistic sentiment and humanistic wisdom.…”

Section: User Profiling and Personalized Smart Services For Librariesmentioning

confidence: 99%

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

Yang

2022

Wireless Communications and Mobile Computing

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Machine learning is one of the important branches of artificial intelligence, which provides new technical means for analyzing users, understanding users, and gaining insight into users. The framework of the personalized intelligent service model for libraries based on user portraits is proposed, and intelligent technologies such as machine learning are applied to analyze and mine users’ big data, build user portraits, associate users and resources with user portraits, and provide personalized intelligent services for users. The study gives a case study of book recommendation by firstly extracting users’ personalized interests, then applying the plain Bayesian algorithm to discover users’ current personalized potential demands for books, and finally providing users with personalized intelligent book recommendation services that match their interests and mainly meet their potential demands.

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Section: User Profiling and Personalized Smart Services For Librariesmentioning

confidence: 99%

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

Yang

2022

Wireless Communications and Mobile Computing

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“…It is worth mentioning that this Systematic Literature Mapping is mainly aimed at researchers moving from the Recommender Systems area to the Technology Enhanced Learning area, in particular, for formal learning. Therefore, the review does not include works that do not focus on formal learning or papers published in the Artificial Intelligence in Education area, although systems such as Intelligent Tutoring Systems have similar goals and might even implement similar algorithms [Manouselis et al, 2011, Paiva et al,2014, Penteado et al,2018, Bel Hadj Ammar et al, 2020, Fotoporlou et al, 2020, Lin et al, 2020.…”

Section: Discussionmentioning

confidence: 99%

The Use of Recommender Systems in Formal Learning. A Systematic Literature Mapping

Ugarte¹,

Larrañaga²,

Arruarte³

2022

jucs

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Recommender Systems provide users with content or products they are interested in. The main purpose of Recommender Systems is to find, among the vast amount of information that is available or advertised on the Internet, content that meets the user’s needs i.e., a product or content that satisfies his or her wishes. These systems are being used more and more in many of the services of our daily lives. In this paper, a systematic mapping review that explores the use of Rec- ommender Systems in formal learning stages is presented. The paper analyzes what kinds of items the Recommender Systems suggest, who the users that receive the recommendations are, what kinds of information the Recommender Systems use to carry out the recommendation process, the algorithms and techniques the Recommender Systems employ and, finally, how the Recommender Systems have been evaluated. The results obtained in the review will make it possible to iden- tify not only the current situation in this field but also some of the challenges that are still to be faced.

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“…n represents the number of samples, m is the number of categories, yij represents 1 when the i-th sample belongs to the category j, otherwise it is 0, and pij represents the probability that the i-th sample is classified as the j-th category [47]. When it is binary classification, the formula is simplified to: B = − ∑ * log A + *1 − + log*1 − A ++ (13) yi is the true category of the i-th input sample xi, and pi is the probability of predicting that xi is a positive sample [48]. The smaller the Logistic Loss, the better, and when it is 0, it is a perfect classifier.…”

Section: B Description Of Evaluation Indicatorsmentioning

confidence: 99%

“…In many cases, FM only involves second-order feature interaction, which is a shallow model, but in real life data is often highly nonlinear, as is the case with recommendation systems. High-order feature interaction is essential for good performance [13]. Although theoretically FM can fit high-order feature combinations, such calculations are too large, and time complexity and storage space consumption will explode.…”

Section: Introductionmentioning

confidence: 99%

Research on Disease Prediction Based on Improved DeepFM and IoMT

Amin

Alhussein

et al. 2021

IEEE Access

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In recent years, with the increase of computer computing power, Deep Learning has begun to be favored. Its learning of non-linear feature combinations has played a role that traditional machine learning cannot reach in almost every field. The application of Deep Learning has also driven the advancement of Factorization Machine (FM) in the field of recommendation systems, because Deep Learning and FM can learn high-order and low-order features combinations respectively, and FM's hidden vector system enables it to learn information from sparse data. The integration of them has attracted the attention of many scholars. They have researched many classic models such as Factorization-supported Neural Network (FNN), Product-based Neural Networks (PNN), Inner PNN (IPNN), Wide&Deep, Deep&Cross, DeepFM, etc. for the Click-Through-Rate (CTR) problem, and their performance is getting better and better. This kind of model is also suitable for agriculture, meteorology, disease prediction and other fields due to the above advantages. Based on the DeepFM model, we predicts the incidence of hepatitis in each sample in the structured disease prediction data of the 2020 Artificial Intelligence Challenge Preliminary Competition, and make minor improvements and parameter adjustments to DeepFM. Compared with other models, the improved DeepFM has excellent performance in AUC. This research can be applied to electronic medical records to reduce the workload of doctors and make doctors focus on the samples with higher predicted incidence rates. For some changing data, such as blood pressure, height, weight, cholesterol, etc., we can introduce the Internet of Medical Things (IoMT). IoMT's sensors can be used to conduct transmission to ensure that the disease can be predicted in time, just in case. After joining IoMT, a healthcare system is formed, which is superior in forecasting and time performance.

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Deep-Cross-Attention Recommendation Model for Knowledge Sharing Micro Learning Service

Cited by 9 publications

References 20 publications

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

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

The Use of Recommender Systems in Formal Learning. A Systematic Literature Mapping

Research on Disease Prediction Based on Improved DeepFM and IoMT

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