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recommendation algorithm based on item quality and user rating preferences

Frontiers of Computer Science 2014年第2期8卷 289-297页

作者： Yuan GUAN Shimin CAI Mingsheng SHANG Web Sciences Center School of Computer Science and EngineeringUniversity of Electronic Science and Technology of China Chengdu 611731 China

Recommender systems are one of the most im- portant technologies in e-commerce to help users filter out the overload of information. However, current mainstream recommendation algorithms, such as the collaborative filter- ing CF family, have problems ness. These problems hinder such as scalability and sparse- further developments of rec- ommender systems. We propose a new recommendation al- gorithm based on item quality and user rating preferences, which can significantly decrease the computing complexity. Besides, it is interpretable and works better when the data is sparse. Through extensive experiments on three benchmark data sets, we show that our algorithm achieves higher accu- racy in rating prediction compared with the traditional ap- proaches. Furthermore, the results also demonstrate that the problem of rating prediction depends strongly on item quality and user rating preferences, thus opens new paths for further study.

关键词： recommendation algorithm item quality userrating preferences RMSE

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A Comparative Study of Various Book recommendation algorithms for Public Libraries

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TECHNICAL SERVICES QUARTERLY 2022年第4期39卷 369-380页

作者： Duchen, Huizi Guizhou Lib Public & Promot Dept Guiyang 550000 Guizhou Peoples R China

Book recommendation algorithms in public libraries can recommend catalogs based on users' borrowing records to assist in book retrieval. This paper introduced two recommendation algorithms, collaborative filtering and association rule, combined them into a hybrid recommendation algorithm, and conducted simulation experiments on the three recommendation algorithms. The results showed that the hybrid recommendation algorithm gave more personalized and specific recommendation results;the recommendation accuracy of the hybrid recommendation algorithm was higher than that of the other two algorithms regardless of the book borrowing volume of target users.

关键词： Public library recommendation algorithm collaborative filtering association rule

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Application of graph auto-encoders based on regularization in recommendation algorithms

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PEERJ COMPUTER SCIENCE 2023年 9卷 e1335页

作者： Xie, Chengxin Wen, Xiumei Pang, Hui Zhang, Bo Hebei Univ Architecture Coll Informat Engn Zhangjiakou Peoples R China Big Data Technol Innovat Ctr Zhangjiakou Zhangjiakou Peoples R China

Social networking has become a hot topic, in which recommendation algorithms are the most important. Recently, the combination of deep learning and recommendation algorithms has attracted considerable attention. The integration of autoencoders and graph convolutional neural networks, while providing an effective solution to the shortcomings of traditional algorithms, fails to take into account user preferences and risks over-smoothing as the number of encoder layers increases. Therefore, we introduce L1 and L2 regularization techniques and fuse them linearly to address user preferences and over-smoothing. In addition, the presence of a large amount of noisy data in the graph data has an impact on feature extraction. To our best knowledge, most existing models do not account for noise and address the problem of noisy data in graph data. Thus, we introduce the idea of denoising autoencoders into graph autoencoders, which can effectively address the noise problem. We demonstrate the capability of the proposed model on four widely used datasets and experimentally demonstrate that our model is more competitive by improving up to 1.3, 1.4, and 1.2, respectively, on the edge prediction task.

关键词： Graph auto-encoders recommendation algorithm Noise Regularization

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A recommendation algorithm for Two-sided Matching Problems based on F-Score

A Recommendation Algorithm for Two-sided Matching Problems b...

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2015 IEEE International Conference on Computer and Communications(ICCC 2015)

作者： Songchao Hu Cheng Zhu Fuli Xie Kaiming Xiao Science and Technology on Information Systems Engineering Laboratory National University of Defense Technology(NUDT)

According to the historical matched data and the agent's risk preference, the proposed algorithm calculates the agent's standardized F-Score to each agent on the opposite side. The agent on the opposite side which has the biggest standardized F-Score is recommended to the original agent. A typical application background of the algorithm is the parallel enrolment mechanism of college education in China. With contrast experiments between the proposed algorithm and other two recommendation algorithms using the real students and college admission data, it is found that the proposed algorithm has a high success recommendation rate and can respond to the agent's risk preference.

关键词： recommendation algorithm two-sided matching F-Score

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Personalized recommendation Method Based on Rating Matrix and Review Text

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COMPUTATIONAL INTELLIGENCE 2025年第1期41卷

作者： Wang, Shiru Du, Wenna Bhuiyan, Amran Chen, Zehua Taiyuan Univ Technol Coll Artificial Intelligence Taiyuan Peoples R China York Univ Informat Retrieval & Knowledge Management Res Lab Toronto ON Canada

In recent years, the algorithm based on review text has been widely used in recommendation systems, which can help mitigate the effect of sparsity in rating data within recommender algorithms. Existing methods typically employ a uniform model for capturing user and item features, but they are limited to the shallow feature level, and the user's personalized preferences and deep features of the item have not been fully explored, which may affect the relationship between the two representations learned by the model. The deeper relationship between them will affect the prediction results. Consequently, we propose a personalized recommendation method based on the rating matrix and review text denoted PRM-RR, which is used to deeply mine user preferences and item characteristics. In the process of processing the comment text, we employ ALBERT to obtain vector representations for the words present in the review text firstly. Subsequently, taking into account that significant words and reviews bear relevance not solely to the review text but also to the user's individualized preferences, the proposed personalized attention module synergizes the user's personalized preference information with the review text vector, thereby engendering an enriched review-based user representation. The fusion of the user's review representation and rating representation is accomplished through the feature fusion module using cross-modal attention, yielding the final user representation. Lastly, we employ a factorization machine to predict the user's rating for the item, thereby facilitating the recommendation process. Experimental results on three benchmark datasets show that our method outperforms the baseline algorithm in all cases, demonstrating that our method effectively improves the performance of recommendations. The code is available at .

关键词： personalized attention mechanism rating matrix recommendation algorithm review text

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Fuzzy clustering algorithm-based teaching resource recommendation method for automotive engine overhaul

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第6期24卷 3762-3771页

作者： Huang, Chaoqun Chongqing Technol & Business Inst Intelligent Mfg & Automobile Dept 15Siyuan RdUniv Pk Chongqing Hechuan Peoples R China Chongqing Open Univ Intelligent Mfg & Automobile Dept Chongqing Peoples R China

At present, the traditional teaching resource recommendation algorithm mainly constructs the evaluation matrix of teaching resources and determines the recommendation order according to the evaluation value, which lacks the grasp of user preferences and leads to poor recommendation effect. Therefore, a method of recommending teaching resources for automobile engine overhaul based on fuzzy clustering algorithm is proposed. This paper constructs the algorithm flow of automobile engine overhaul teaching resources recommendation, describes the K-means clustering algorithm and the improved K-means clustering algorithm, analyzes the user characteristics by using the clustering fuzzy algorithm, calculates the similarity between user interests and resources, and completes the recommendation of automobile engine overhaul teaching resources. Experiments verify the recommendation accuracy of this method, and the results show that this method has low MAE value and high recommendation accuracy when it is used to recommend teaching resources.

关键词： fuzzy clustering algorithm automotive engines teaching resources recommendation algorithm

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Two-sided matching theory-based second-hand house transaction evaluation and recommendation by the modified PLC-DEMATEL method

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APPLIED SOFT COMPUTING 2024年 166卷

作者： Li, Bo Zhu, Wenwen Xu, Zeshui Zhang, Chonghui Zhejiang Gongshang Univ Sch Stat & Math Hangzhou 310018 Peoples R China Sichuan Univ Business Sch Chengdu 610064 Sichuan Peoples R China

To enhance the two-sided matching efficiency in a multi-source heterogeneous environment, this paper takes the randomness and unstructured features of the online comments into consideration, and proposes a new matching mechanism by introducing the complex information representation tool. Firstly, the concept of the probabilistic linguistic normal cloud (PLNC) model is introduced to preserve information cohesion and characteristic distribution. Next, the basic operation laws and corresponding operators are given. Then, an innovative maximum boundary concept skipping the indirect approach is presented to update the similarity degree and distance measures, also the correlation coefficient. Furthermore, for the multi-indicator systems with interactions, the peer experts are invited to evaluate the relationship between indicators, a modified algorithm based on the Decision-Making and Trial Evaluation Laboratory (DEMATEL) method is applied to obtain the subjective weights of indicators. After that, a whole matching process and a correlation coefficient cluster method-based recommendation algorithm are presented. A case study is provided to illustrate the method, wherein a new indicator system is constructed by analyzing the correlation of multiple indicators based on online linguistic evaluations. The random forest model is combined to obtain the objective weights and balance its reliability. Finally, sensitivity analysis and comparative analysis are employed to validate the effectiveness and applicability.

关键词： Two-sided matching Second-hand house Probabilistic linguistic term set Cloud model recommendation algorithm

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AN IMPROVED recommendation algorithm VIA SOCIAL BEHAVIORS

AN IMPROVED RECOMMENDATION ALGORITHM VIA SOCIAL BEHAVIORS

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International Copmputer Conference on Wavelet Active Miedia Technology and Information Processing

作者： JIN-HU LIU YU-XIAO ZHU KUN-YU SHI Web Sciences Center University of Electronic Science and Technology of China Chengdu 611731 China

ISBN: (纸本)9781467382670

recommendation is an important problem in the traditional field of data mining. As a consequence, various kinds of algorithms have been proposed in the last few years to improve the recommendation performance. However, many of them overlook users' rating behaviors. In this paper, an improved recommendation algorithm with considering users' habits and rating behaviors will be proposed. Firstly, calculate the ratings entropy for each user via users' rating records which can reflect users' behavioral features. Secondly, widely-used user-based method will be improved by considering rating entropy. Finally, the predicting rate is generated. Experimental results on the three datasets: Movie Lens, Netflix and RYM all suggest that the proposed method can enhance the algorithmic accuracy. Furthermore, since this method can improve those missing value more accurately via users' similarity and rating behaviors which might shed some light on discovering users' purchasing intention and optimizing the performance of recommender systems by human dynamics.

关键词： recommendation algorithm similarity human dynamics data mining

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Food recommendation towards personalized wellbeing

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TRENDS IN FOOD SCIENCE & TECHNOLOGY 2025年 156卷

作者： Qiao, Guanhua Zhang, Dachuan Zhang, Nana Shen, Xiaotao Jiao, Xidong Lu, Wenwei Fan, Daming Zhao, Jianxin Zhang, Hao Chen, Wei Zhu, Jinlin Jiangnan Univ State Key Lab Food Sci & Resources Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Sch Food Sci & Technol Wuxi 214122 Jiangsu Peoples R China Jiangnan Univ Sch Artificial Intelligence & Comp Sci Wuxi 214122 Jiangsu Peoples R China Swiss Fed Inst Technol Inst Environm Engn Laura Hezner Weg 7 CH-8093 Zurich Switzerland Nanyang Technol Univ Lee Kong Chian Sch Med Singapore Singapore Jiangnan Univ Natl Engn Res Ctr Funct Food Wuxi 214122 Jiangsu Peoples R China

Background: The intersection of nutrition and technology gave birth to the research of food recommendation system (FRS), which marked the transformation of traditional diet to a more personalized and healthy direction. The FRS uses advanced data analysis and machine learning technology to provide customized dietary advice according to users' personal preferences, and nutritional needs, which plays a vital role in promoting public health and reducing disease risks. Scope and approach: This review presents the architecture of FRS and deeply discusses various recommendation algorithms, including the content-based method, collaborative filtering method, knowledge graph-based method, and hybrid methods. The review further introduces existing data resources and evaluation metrics, and highlights key technologies in user profiling and food analysis. In addition, the wide application of personalized FRS is summarized, and the importance of these systems in satisfying users' dietary preferences and maintaining balanced nutrition is emphasized. Finally, the key challenges and development trends of FRS are deeply analyzed from data level, model level and user experience level. Key findings and conclusions: Personalized FRS shows great potential in helping users make healthier dietary decisions. Although there are still many challenges, such as dealing with heterogeneous data and interpretability. But with the progress of technology, there will be broader development in the future. For example, the powerful data processing ability of deep learning will effectively improve the accuracy of the system. In addition, the application of interactive recommendation system and large language model will also provide strong support for satisfying user experience and improving acceptance.

关键词： Food recommendation system Machine learning Personalized diet recommendation algorithm Precision nutrition

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Automatic Classification and recommendation for English Teaching Materials Based on Natural Language Processing

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2025年第6期34卷

作者： Bai, Yun Xian, Huihui Shandong Univ Sch Foreign Languages & Literature Jinan 250100 Shandong Peoples R China Shandong Univ Weihai Sch Translat Studies Weihai 264209 Shandong Peoples R China

The wide range of digital educational resources calls for developing an accurate and efficient method for categorizing and recommending English teaching materials. An automatic classification and recommendation system has been created and implemented using Natural Language Processing (NLP) techniques. Data from essays produced by English Language Learners (ELLs) in grades 8 through 12, as well as components including content, competency levels and score notes, are organized for this study. Models for precise language proficiency assessments are planned to be developed to enhance automated feedback mechanisms. Preprocessing methods such as stop-word removal, stemming and tokenization were applied to tidy up the data. Term Frequency-Inverse Document Frequency (TF-IDF) and word embeddings were two strategies used in the feature extraction process to convert textual data into numerical vectors. Then, the recently created Support Vector Machine-Neural Network-Genetic algorithm (SVM-NN-GA) was fed to classify these vectors. The model's performance was evaluated using F1-measure, accuracy, precision and recall metrics. Methods of collaborative filtering and content-based filtering were studied for the recommendation system. In contrast to collaborative filtering, which used user interaction data to identify patterns and suggest relevant items, content-based filtering matched materials with user preferences based on attributes gathered from NLP models. A hybrid recommendation system combines different approaches, increasing recommendations' personalization and relevance. The results demonstrate that the hybrid recommendation and NLP-based categorization approach as a combination method suggestively improves the effectiveness of selecting appropriate teaching materials, helping teachers to enhance the learning process.

关键词： Natural Language Processing (NLP) English teaching materials automatic classification recommendation algorithm machine learning text preprocessing feature extraction Term Frequency-Inverse Document Frequency (TF-IDF) word embedding Support Vector Machine-Neural Network-Genetic algorithm (SVM-NN-GA) collaborative filtering content-based filtering

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