A novel hybrid deep recommendation system to differentiate user’s preference and item’s attractiveness

Zhang, Xiaofeng; Liu, Huijie; Chen, Xiaoyun; Zhong, Jingbin; Wang, Di

doi:10.1016/j.ins.2020.01.044

Cited by 46 publications

(8 citation statements)

References 10 publications

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“…AutoRec can solve the prediction problem of non-integer rating, and the prediction result was better than the traditional matrix factorization methods. F. Strub et al [27] extended the AutoRec, and proposed a collaborative filtering neural network (CFN) based on denoising autoencoder. CFN adopted denoising technology to increase the generalization ability of the model and made it more robust.…”

Section: Related Workmentioning

confidence: 99%

An Interactive Perception Method Based Collaborative Rating Prediction Algorithm

Yan

Zhang

2023

Chinese J. Elect.

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To solve the rating prediction problems of low accuracy and data sparsity on different datasets, we propose an interactive perception method based collaborative rating prediction algorithm named DCAE-MF, by fusing dual convolutional autoencoder (DCAE) and probability matrix factorization (PMF). Deep latent representations of users and items are captured simultaneously by DCAE and are deeply integrated with PMF to collaboratively make rating predictions based on the known rating history of users. A global multi-angle collaborative optimization learning method is developed to effectively optimize all the parameters of DCAE-MF. Extensive experiments are performed on seven real-world datasets to demonstrate the superiority of DCAE-MF on key rating accuracy metrics of the root mean squared error (RMSE) and mean absolute error (MAE).

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Section: Related Workmentioning

confidence: 99%

An Interactive Perception Method Based Collaborative Rating Prediction Algorithm

Yan

Zhang

2023

Chinese J. Elect.

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“…Two decades ago, the retail industry encountered a profound and influential challenge that reshaped its landscape: the emergence of e-commerce. E-commerce recommendation systems gather diverse user-related data and employ predictive analytics to anticipate user preferences and deliver item recommendations [50]. Prominent e-commerce platforms like Amazon and Taobao have implemented their own recommendation systems, facilitating business expansion and revenue growth by providing accurate and preferred recommendations to users.…”

Section: Metaverse Recommendation Systems For Product Transaction Sce...mentioning

confidence: 99%

Recommendation Systems for the Metaverse

Wei,

Wang,

Wang

et al. 2023

Blockchains

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The metaverse, a virtual world closely intertwined with reality, represents the next-generation form of the Internet and encompasses advanced sociability and interaction. With the rapid growth of users and virtual products within this environment, the importance of recommendation systems becomes paramount. This article presents a comprehensive survey that examines the technologies influencing the development of recommendation systems tailored for the metaverse. Firstly, we identify the key trends and fundamental concepts associated with these systems. Secondly, we delve into the core supporting technologies and application scenarios within metaverse recommendation systems. Lastly, we provide insights into future research directions for this rapidly evolving field. Our survey reveals that while progress has been achieved, further investigation is necessary to optimize the technical layout and content of metaverse recommendation systems.

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“…However, old recommendation systems typically focus on factual data (e.g., citation, keyword matching), "Like" given by human users based on their recommendation on any particular object (e.g., journal articles), or even using ontology network analysis to select papers based on the user's profile of interest, instead of focusing on the research gaps that students can look into [16,18]. With the help of advanced data sciences and AI technologies, the latest recommendation system techniques focus more on the semantics and meanings through advanced data mining or AI technologies, such as natural language processing (NLP) and sentiment analysis [19,20].…”

Section: Recommendation Systemmentioning

confidence: 99%

An Exploratory Study of Helping Undergraduate Students Solve Literature Review Problems Using Litstudy and NLP

Wong,

2023

Education Sciences

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(1) Many undergraduate students struggle to produce a good literature review in their dissertations, as they are not experienced, do not have sufficient time, and do not have the required skills to articulate information. (2) Subsequently, we deployed Litstudy and NLP tools and developed a recommendation system to analyze articles in an academic database to help the students produce literature reviews. (3) The recommendation system successfully performed three levels of analysis. The elementary-level analysis provided demographic statistical analysis to the students, helping them understand the background information of the selected articles they would review. The intermediate-level analysis provided visualization of citations in network graphs for the students to understand the relationships of the articles’ authors, regions, and institutes so that the flow of ideas, development, and similarity of the selected articles can be better analyzed. The advanced level of analysis provided topic modeling functions for the students to understand the high-level themes of the selected articles to improve productivity as they read through them and simultaneously boost their creativity. (4) The three levels of analysis successfully analyzed the selected articles to provide innovative results and triggered the students to handle literature reviews in a new way. Further enhancement opportunities were identified in integrating the NLP technologies with large language models to facilitate the generation of research ideas/insights. This would be an exciting opportunity to have AI/NLP integrated to help the students with their research.

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A novel hybrid deep recommendation system to differentiate user’s preference and item’s attractiveness

Cited by 46 publications

References 10 publications

An Interactive Perception Method Based Collaborative Rating Prediction Algorithm

An Interactive Perception Method Based Collaborative Rating Prediction Algorithm

Recommendation Systems for the Metaverse

An Exploratory Study of Helping Undergraduate Students Solve Literature Review Problems Using Litstudy and NLP

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