Towards leveraging explicit negative statements in knowledge graph embeddings by Rita T. Sousa, Catia Pesquita, Heiko Paulheim

“…However, in many domains, negative information is particularly interesting, for example, in recommender systems, where negative associations of users and items can help in learning better user representations, or in the biomedical domain, where the knowledge that a patient does exhibit a specific symptom can be crucial for accurate disease diagnosis.In this paper, we argue that negative statements should be given more attention in knowledge graph embeddings. …”
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Accurate multi-behavior sequence-aware recommendation via graph convolution networks. by Doyeon Kim, Saurav Tanwar, U Kang

“…Multi-behavior recommender systems leverage various types of user behavior data to enhance recommendation performance for the target behavior. …”
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A Tri-Attention Neural Network Model-BasedRecommendation by Nanxin Wang, Libin Yang, Yu Zheng, Xiaoyan Cai, Xin Mei, Hang Dai

“…Heterogeneous information network (HIN), which contains various types of nodes and links, has been applied in recommender systems. Although HIN-based recommendation approaches perform better than the traditional recommendation approaches, they still have the following problems: for example, meta-paths are manually selected, not automatically; meta-path representations are rarely explicitly learned; and the global and local information of each node in HIN has not been simultaneously explored. …”
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Chinese Mathematical Knowledge Entity Recognition Based on Linguistically Motivated Bidirectional Encoder Representation from Transformers by Wei Song, He Zheng, Shuaiqi Ma, Mingze Zhang, Wei Guo, Keqing Ning

“…We assessed whether constructing a mathematical knowledge graph for a knowledge question-answering system or a course recommendation system, Named Entity Recognition (NER), is indispensable. …”
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Credit Card Fraud Detection through Parenclitic Network Analysis by Massimiliano Zanin, Miguel Romance, Santiago Moral, Regino Criado

“…While this has hitherto been tackled through data analysis techniques, the resemblances between this and other problems, like the design of recommendation systems and of diagnostic/prognostic medical tools, suggest that a complex network approach may yield important benefits. …”
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DeepFusion: Fusing User-Generated Content and Item Raw Content towards Personalized Product Recommendation by Mingxin Gan, Hang Zhang

“…Personalized recommender systems, as effective approaches for alleviating information overload, have received substantial attention in the last decade. …”
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Online comments of tourist attractions combining artificial intelligence text mining model and attention mechanism by Tingting Mou, Hongbo Wang

“…The proposed method not only improves the accuracy of sentiment analysis, but also provides strong support for the optimization of tourism recommendation system and provides more comprehensive, objective and accurate tourism information for scenic spot managers and tourism enterprises. …”
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Personalized Music Recommendation Simulation Based on Improved Collaborative Filtering Algorithm by Hui Ning, Qian Li

“…Collaborative filtering technology is currently the most successful and widely used technology in the recommendation system. It has achieved rapid development in theoretical research and practice. …”
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Study of Keyword Extraction Techniques for Electric Double-Layer Capacitor Domain Using Text Similarity Indexes: An Experimental Analysis by M. Saef Ullah Miah, Junaida Sulaiman, Talha Bin Sarwar, Kamal Z. Zamli, Rajan Jose

“…This study can help the NLP researchers working with the EDLC domain or recommender systems to select more suitable keyword extraction and similarity index calculation techniques.…”
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Interpretable Machine Learning Techniques for an Advanced Crop Recommendation Model by Mohamed Bouni, Badr Hssina, Khadija Douzi, Samira Douzi

“…Our research addresses this critical imperative by introducing an innovative predictive model that refines crop recommendation systems through advanced machine learning techniques, specifically random forest and SHapley Additive exPlanations (SHAP). …”
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A Triangular Personalized Recommendation Algorithm for Improving Diversity by Biao Cai, Xiaowang Yang, Yusheng Huang, Hongjun Li, Qiang Sang

“…Recommendation systems are used when searching online databases. …”
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English Grammar Discrimination Training Network Model and Search Filtering by Juan Zhao

“…In addition, in order to improve the scalability and real-time performance of the recommendation system, the data mining method is used to perform fuzzy clustering for users and projects in the offline data preprocessing stage. …”
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Exploration on the Action-Oriented Teaching Mode of Higher Vocational Political Courses under the Background of Internet + Education by Jiafang Li

“…In this paper, by adopting the personalized recommendation algorithm based on collaborative filtering, the recommendation algorithm in the recommendation system is applied to the action-oriented online teaching platform of higher vocational political courses, and a hybrid programming method of cross-platform compatibility HTML5 and high-performance MUI framework is designed. …”
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Maximum Butterfly Generators Search in Bipartite Networks by Jianrong Huang, Guangyao Pang, Fei Hao

“…Particularly, maximum butterfly as a special cohesive subgraph of bipartite graphs, is playing an critical role in many promising application such as recommendation systems and research groups detection. Enumerating maximal butterfly has been proved to be a NP-hard and suffers time and space complexity. …”
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Enhancing drug discovery and patient care through advanced analytics with the power of NLP and machine learning in pharmaceutical data interpretation by Nagalakshmi R, Surbhi Bhatia Khan, Ananthoju Vijay kumar, Mahesh T R, Mohammad Alojail, Saurabh Raj Sangwan, Mo Saraee

“…Utilizing a vast dataset sourced from a well-established online pharmacy, this research employs sophisticated ML algorithms and cutting-edge NLP techniques to critically analyze medical descriptions and optimize recommendation systems for drug prescriptions and patient care management. …”
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Going paperless: the strengths and limitations of electronic research ethics information management system in a health training institution in Tanzania by Rebecca D. Mkumbwa, Godwin Pancras, Nathanael S. Sirili

“…Institutions aspiring to adapt the system can learn from this experience and devise ways to overcome the limitations. We recommend system automation, strengthening the institutional capacity and further studies to be conducted on the adoptability of REIMS especially in resource limited settings.…”
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A CNN-Based Approach for Classical Music Recognition and Style Emotion Classification by Yawen Shi

“…Music recognition technology can help people recognize information such as the music title, artist, musical style, rhythm, and the emotions conveyed by the music in the audio, thus enabling applications like automated music information retrieval and recommendation systems. Classical music, due to its vast quantity, diverse types, and time span covering several centuries, presents challenges that existing music recognition software and traditional music recognition algorithms cannot effectively address. …”
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Research on PMF Model Based on BP Neural Network Ensemble Learning Bagging and Fuzzy Clustering by Zhengjin Zhang, Guilin Huang, Yong Zhang, Siwei Wei, Baojin Shi, Jiabao Jiang, Baohua Liang

“…Probability matrix factorization model can be used to solve the problem of high-dimensional sparsity of user and rating data in the recommender systems. However, most of the existing methods use the user to model the item rating, ignoring the relationship between the user and the item, so the accuracy of user-item rating prediction is still low. …”
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DMR: disentangled and denoised learning for multi-behavior recommendation by Yijia Zhang, Wanyu Chen, Fei Cai, Zhenkun Shi, Feng Qi

“…Abstract In recommender systems, leveraging auxiliary behaviors (e.g. view, cart) to enhance the recommendation in the target behavior (e.g. purchase) is crucial for mitigating the sparsity issue inherent in single-behavior recommendation. …”
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Research on multiple enhanced k combination reverse Skyline query method by Song Li, Xinyuan Zhang, Liping Zhang

“…It has been widely applied in business decision-making, recommendation systems, location-based services, knowledge discovery, and data mining. …”
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