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Study on the impact of recommendation algorithms on user perceived stress and health management behaviour in short video platforms

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INFORMATION PROCESSING & MANAGEMENT 2024年第3期61卷

作者： Wang, Xiwei Wuji, Siguleng Liu, Yutong Luo, Ran Qiu, Chengcheng Jilin Univ Sch Business & Management Changchun Peoples R China Jilin Univ Res Ctr Big Data Management Changchun Peoples R China Jilin Univ Inst Natl Dev & Secur Studies Changchun Peoples R China

Short videos have become an ideal platform for global health information dissemination. Past research has tended to overlook the potentially negative experiences associated with recommendation algorithms and their positive coping mechanisms, while focusing too much on shortterm effects at the expense of long-term effects. This study integrates multiple theoretical frameworks and proposes a new theoretical model aimed at determining the impact of this technostress from recommendation algorithms on users' health stress perceptions, health-related short videos usage, and health self-management behaviours. This paper builds a theoretical model based on the stressor-strain-outcome (SSO) framework, combining the health belief model and self-validation theory. Data was collected and analyzed using a diary study to understand the mechanism of the influence of daily stress perception on users' health habits and behavioural changes through multilevel modelling. The findings show that external stressor variables have no significant effect on the continuous usage intention of health-related short videos under algorithm recommendation. Cognitive validation and affective validation among the internal stressor variables have a positive effect on users' continued intentions. Notably, despite the presence of continuous usage intention, it was negatively related to health self-management behaviours. These findings provide new insights into the composition of internal and external health stressors driven by recommendation algorithm technostress, constructing a multilevel theoretical model of health-related short video usage, and tapping into the potential for the application of short video platforms in health self-management.

关键词： Stress perception Short video usage recommendation algorithm Health self -management Behavioural influence

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A Solution of Missing Value in Collaborative Filtering recommendation algorithm

A Solution of Missing Value in Collaborative Filtering Recom...

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Chinese Automation Congress (CAC)

作者： Jie Qin Lei Cao Hui Peng PLA Univ Sci & Technol Coll Command Informat Syst Nanjing 210007 Jiangsu Peoples R China

ISBN: (纸本)9781467371896

In order to solve the sparsity of User-Item scoring matrix in collaborative filtering recommendation, proposed an algorithm that combining user's interests with item's quality to calculate ungraded items in matrix. By setting the weight of user and item, synthesized the value of missing value, which were used to replace the ungraded value in scoring matrix to calculate similarity. Experiment shows that the algorithm can improve the recommendation effect, and when the user's weight values 0.4, MAE reaches minimum, and recommendation quality reaches maximum.

关键词： collaborative filtering sparsity recommendation algorithm MAE

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An Improved Collaborative Filtering recommendation algorithm Incorporating Opinions Analysis 7

An Improved Collaborative Filtering Recommendation Algorithm...

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7th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)

作者： Li, Wei Sun, Bo China Post Grp Corp Training Ctr Dept Post Commun Management Shijiazhuang Peoples R China

ISBN: (纸本)9781479986460

Collaborative filtering recommendation algorithm has become a common way to deal with the problem of information overload, which hinders consumers to make appropriate decisions and firms to provide the items that consumers really interest in. Traditional collaborative method is basing on consumers' rating on the items, hence, their performance suffers from data sparsity and cold-start. In this paper, we propose the framework of a novel recommendation algorithm. The proposed algorithm adopts the method of opinion mining to extract consumers' preference from their reviews, and then incorporating it to collaborative filtering method to improve the performance of the algorithm. The current work is an improving method to the traditional item-based collaborative filtering algorithm.

关键词： recommendation algorithm opinion mining collaborative filtering

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AN IMPROVED recommendation algorithm VIA SOCIAL BEHAVIORS 12

AN IMPROVED RECOMMENDATION ALGORITHM VIA SOCIAL BEHAVIORS

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12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Liu, Jin-Hu Zhu, Yu-Xiao Shi, Kun-Yu Univ Elect Sci & Technol China Web Sci Ctr Chengdu 611731 Peoples R China Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9781467382663

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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Research on recommendation algorithms Based on Cloud Models in Probabilistic Linguistic Environments

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Journal of Systems Science and Information 2024年第1期12卷 96-112页

作者： Peng YANG Xifeng MA Meng WEI Chunsheng CUI Libin CHE College of Computer and Information Engineering Henan University of Economics and LawZhengzhou 450046China

To solve the problem that the traditional cloud model can't directly process the textual review information in the recommendation algorithm,this paper combines the merits of the cloud model in transforming qualitative and quantitative knowledge with the multi-granularity advantages of probabilistic linguistic term sets in representing uncertain information,and proposes a recommendation algorithm based on cloud model in probabilistic language ***,this paper quantifies the attributes in the review text based on the probabilistic linguistic term ***,the maximum deviation method is used to determine the weight of each attribute in the evaluation information of the product to be recommended,and the comprehensive evaluation number and attribute weight are converted into the digital characteristic value of the cloud model by using the backward cloud ***,the products are recommended and sorted based on the digital characteristic value of the cloud *** algorithm is applied to the recommendation of 10 hotels,and the results show that the method is effective and practical,enriching the application of cloud models in the recommendation field.

关键词： recommendation algorithm cloud model probabilistic linguistic term set text reviews

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Personalized recommendation algorithm Based on Situation Awareness

Personalized Recommendation Algorithm Based on Situation Awa...

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International Conference on Logistics, Informatics and Service Sciences (LISS)

作者： Qiao, Lei Zhang, Runtong Beijing Jiaotong Univ Beijing Peoples R China

ISBN: (纸本)9781479918911

With the increasing number of mobile devices and the development of all kinds of mobile technology, the volume of data is getting larger and larger. People have to face huge amounts of data. At the same time, as the continuous development of various mobile devices providing more data acquisition technology, the dimension of data have a bigger growth too. It's not enough for the existing technology in the treatment of huge amounts and high dimension data. User's interests in different time have been taken into equal consideration with the method being used, which leads to lack of effectiveness in the given period of time. In order to solve this problem, personalized recommendation is faced with great challenge. What's more, timeliness which is getting more and more urgent in people's need is very important. Towards the problem of recommendation based on context-aware, this paper propose a personalized recommendation algorithm based on user context clustering, with the consideration of timeliness. This algorithm uses the idea of contextual pre-filtering. A method of fuzzy clustering is used on the users' context in history data set first to construct the user set which is similar with current user context, and then combined with user-based collaborative filtering algorithm for personalized recommendation.

关键词： context-aware clustering personalized recommendation algorithm

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A hybrid recommendation algorithm based on social networks 11

A hybrid recommendation algorithm based on social networks

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Proceedings of the 11th EAI International Conference on Heterogeneous Networking for Quality, Reliability, Security and Robustness

作者： Gong, Ling Gao, MeiLing Xu, Bixiao Wang, Wenjun Sun, Zhixin Nanjing Univ Posts & Telecommun Coll Math & Phys Key Lab Broadband Wireless Commun & Sensor Networ Nanjing Jiangsu Peoples R China Key Lab Broadband Wireless Commun & Sensor Networ Nanjing Jiangsu Peoples R China Nanjing Univ Posts & Telecommun Coll Math & Phys Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781631900631

In the light of the problem of collaborative recommendation and content-based cold start, this paper proposes a hybrid recommendation system based on social network. The method is based on the user social relations network. According to the social behavior of user, by establishing the model of social network users, it puts forward the user similarity measure. Then it takes random walk algorithm as a basis and selects out N users who have the highest similarity with the users' interest. The test results show that this method can obtain better recommendation effect and customer satisfaction.

关键词： Social network recommendation algorithm Collaborative recommendation

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An algorithm for Peer Reviewer recommendation Based on Scholarly Activity Assessment

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IEEE ACCESS 2023年 11卷 39609-39620页

作者： Choi, Dong-Hoon Hyun, Jin Woo Kim, Young Rock Korea Inst Sci & Technol Informat Seoul 02456 South Korea Hankuk Univ Foreign Studies Grad Sch Dept Math Seoul 02450 South Korea Hankuk Univ Foreign Studies Grad Sch Educ Seoul 02450 South Korea

Journal editors are putting a lot of effort into selecting appropriate reviewers for fair and reliable peer review of submitted manuscripts. Editors consider whether the reviewers have no affinity with any of the authors of manuscripts and have sufficient expertise in reviewing the manuscripts. The affinity can be evaluated by whether any of the reviewers has been a coauthor and/or a coworker in a common institution with any of the authors of the manuscript. The expertise depends on the similarity of the research topic between the reviewer's published papers and the submitted manuscripts. In this paper we propose an algorithm to recommend appropriate reviewers to editors, based on the assessment of these scholarly activities and achievements. To implement this algorithm, TextRank and GenSim library are used to extract feature sets from abstract and introduction sections of both submitted manuscripts and the reviewer candidates' papers. Based on the extracted feature sets, affinity and expertise check are implemented. An experiment has been conducted with a data set of over 1,000 papers in the field of DB research to evaluate the performance of this algorithm. The experiment consists of affinity check by using 2-mode network matrix operations and expertise check based on Max Similarity and/or topic clustering. Experimental results show that the recommendation algorithm is reasonable on the basis of scholarly activity assessment and achievements. Our method is designed to optimize the reviewer pool for each journal, client and this algorithm is designed with an open license and has the advantage of being free to use and operate. In addition, our algorithm has the advantage of journal-specific optimization by designing to adjust the expertise of the reviewer selection and the weights for exclusion of interests to reflect journal-specific policies.

关键词： Peer review recommendation algorithm TextRank 2-mode network max similarity topic clustering

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Comparison of Multiple Book recommendation algorithms After Analysis of User Characteristics Using Big Data

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TECHNICAL SERVICES QUARTERLY 2023年第4期40卷 249-259页

作者： Wang, Ruixia He, Na Lib Henan Engn Coll Zhengzhou 451191 Henan Peoples R China

Through big data analysis of borrowing information, it is possible to find the characteristics of different users. This paper applied K-means clustering to classify users. User feature vectors were extracted, and several algorithms were utilized to recommend books to different users. The hybrid algorithm achieved a precision of 96.29%, a recall rate of 92.13%, and an score of 94.16%, significantly higher than other algorithms. The findings prove that it is feasible to discover the characteristics of different users through big data analysis for book recommendation, and the hybrid algorithm performs the best.

关键词： clustering analysis user characteristics book recommendation recommendation algorithm

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Nudging recommendation algorithms increases news consumption and diversity on YouTube

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PNAS NEXUS 2024年第12期3卷 pgae518页

作者： Yu, Xudong Haroon, Muhammad Menchen-Trevino, Ericka Wojcieszak, Magdalena Univ North Dakota Dept Commun Grand Forks ND USA Univ Calif Davis Dept Comp Sci Davis CA USA Univ Calif Davis Dept Commun Davis CA 95616 USA Univ Warsaw Ctr Excellence Social Sci Warsaw Poland

recommendation algorithms profoundly shape users' attention and information consumption on social media platforms. This study introduces a computational intervention aimed at mitigating two key biases in algorithms by influencing the recommendation process. We tackle interest bias, or algorithms creating narrow nonnews and entertainment information diets, and ideological bias, or algorithms directing the more strongly partisan users to like-minded content. Employing a sock-puppet experiment ( n = 8,600 sock puppets) alongside a month-long randomized experiment involving 2,142 frequent YouTube users, we investigate if nudging the algorithm by playing videos from verified and ideologically balanced news channels in the background increases recommendations to and consumption of news. We additionally test if providing balanced news input to the algorithm promotes diverse and cross-cutting news recommendations and consumption. We find that nudging the algorithm significantly and sustainably increases both recommendations to and consumption of news and also minimizes ideological biases in recommendations and consumption, particularly among conservative users. In fact, recommendations have stronger effects on users' exposure than users' exposure has on subsequent recommendations. In contrast, nudging the users has no observable effects on news consumption. Increased news consumption has no effects on a range of survey outcomes (i.e. political participation, belief accuracy, perceived and affective polarization, and support for democratic norms), adding to the growing evidence of limited attitudinal effects of on-platform exposure. The intervention does not adversely affect user engagement on YouTube, showcasing its potential for real-world implementation. These findings underscore the influence wielded by platform recommender algorithms on users' attention and information exposure.

关键词： social media news exposure filter bubbles recommendation algorithm computational social science

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