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Journal articles on the topic 'Collaborative-Based Approach'

Author: Grafiati

Published: 5 June 2025

Last updated: 16 July 2025

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1

Trivedi, Rutansh. "Collaborative Filtering Using a Regression - Based Approach and Classification-Based Approach." IJARCCE 7, no. 12 (2018): 31–34. http://dx.doi.org/10.17148/ijarcce.2018.71207.

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2

Begibaevna, Turemuratova Aziza, Arzueva Ayjamal Azamat qizi, Kujamuratova Gulnaz Jumabayevna, and Iskendarova Shayra Sabirovna. "PEDAGOGICAL MECHANISMS OF DEVELOPING COLLABORATIVE SKILLS OF STUDENTS BASED ON A MULTI-VECTOR APPROACH." International Journal of Pedagogics 4, no. 9 (2024): 55–62. http://dx.doi.org/10.37547/ijp/volume04issue09-11.

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Pedagogical and psychological studies were carried out to improve the collaborative skills of young students by implementing multi-vector approaches to the analysis of pedagogical mechanisms in education. The main goal and tasks of this research was to develop a modern educational model with the help of pedagogical educational programs. Multi-vector approaches based on new pedagogical mechanisms have been analyzed to further increase their psychological state, spirit and interest in education in the formation of collaborative skills of the young generation.

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3

Begibaevna, Turemuratova Aziza, Asamatdinova Bazargul Bakhadirovna, Ismailova Zaynab Rinat kizi, and Aytboyeva Shaira Sapargali kizi. "PEDAGOGICAL FOUNDATIONS OF DEVELOPING STUDENTS' COLLABORATIVE SKILLS BASED ON A MULTI-VECTOR APPROACH." European International Journal of Pedagogics 4, no. 11 (2024): 183–87. http://dx.doi.org/10.55640/eijp-04-11-36.

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Іn the current tіme of globalіzatіon, collaboratіon among people іn vіrtual envіronments іs becomіng an іmportant precondіtіon of success. Thіs trend іs reflected also іn the educatіonal domaіn where students collaborate іn varіous short-term groups created repetіtіvely but changіng іn each round (e.g. іn MOOCs). Students іn thіs kіnd of dynamіc groups quіte often encounter varіous dіffіcultіes, whіch are obvіous maіnly when the students’ characterіstіcs do not complement each other. Іn spіte of varіous group formatіon methods aіmed to solve the group compatіbіlіty problem, most of the exіstіng approaches do not consіder dynamіc groups. Our results іndіcate that consіderіng feedback from students’ collaboratіon can іmprove the group formatіon process as the groups created by our method achіeved hіgher collaboratіon qualіty wіth next іteratіons.

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4

Cooper, Dorothy, and Brad Christensen. "Marketing Community-Based Services: A Collaborative Approach." Social Marketing Quarterly 2, no. 3 (1995): 18–23. http://dx.doi.org/10.1177/152450049500200303.

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LOVINGER, SARAH PRESSMAN. "Collaborative, Evidence-Based Approach Encouraged for Depression." Clinical Psychiatry News 35, no. 7 (2007): 28. http://dx.doi.org/10.1016/s0270-6644(07)70446-0.

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6

Vucetic, Slobodan, and Zoran Obradovic. "Collaborative Filtering Using a Regression-Based Approach." Knowledge and Information Systems 7, no. 1 (2005): 1–22. http://dx.doi.org/10.1007/s10115-003-0123-8.

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7

Zhao, Wentao, and Dewang Wang. "Similarity-based Graph Convolution Collaborative Recommendation Approach." International Journal of Computer Science and Information Technology 3, no. 1 (2024): 158–72. http://dx.doi.org/10.62051/ijcsit.v3n1.21.

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With the rapid and iterative development of science and technology, a large amount of information exceeds the range that can be accepted, processed, or effectively utilized by an individual or a system, and recommendation algorithms can, to a certain extent, solve such problems, but traditional recommendation algorithms do not have a good solution to the problems related to data sparsity and recommendation accuracy. A similarity-based graph convolutional neural collaborative recommendation method (GCSCF) is proposed. The similarity algorithm based on the attributes of the item is used to find the item with the highest similarity and has not interacted with the current user, and this item is set to interact with the current user. The relevant interaction information of the user and the item is converted into relative feature vectors; the feature vectors are propagated using a graph convolutional neural network to aggregate the localized information, and the weight coefficients based on the item ratings are normalized to reduce the noise caused by the information aggregation. Comparative experiments are conducted on two public datasets, MovieLens-1M and Movielens-100K, with five baseline models on the set, and using Recall, Normalized Discounted Cumulative Gain (NDCG), and Precision as the evaluation metrics, and the results of the experiments show that the performance of the proposed social recommendation model better than other models.

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Begibaevna, Turemuratova Aziza, Bayniyazova Rano Maxamatdinovna, Raimberganov Bobur Rashid o‘g’li, and Aminova Aziza Maxmudovna. "PEDAGOGICAL MECHANISMS OF DEVELOPING COLLABORATIVE SKILLS OF STUDENTS BASED ON A MULTI-VECTOR APPROACH." European International Journal of Pedagogics 4, no. 9 (2024): 13–15. http://dx.doi.org/10.55640/eijp-04-09-03.

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The possibility of using a multi-vector model of the zone of proximal development for a qualitative analysis of the cognitive-personal dynamics of a child's development in the process of overcoming learning difficulties is described. The model was developed within the framework of a reflexive-activity approach to providing advisory assistance. The use of this model in the analysis of the dynamics of a child's cognitive-personal development makes it possible to establish "steps in development" recorded as new formations in the child's cognitive abilities and to determine personal characteristics caused by primary cognitive changes or arising as a result of qualitative changes in the learning process itself.

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9

HUANG, Guang-qiu. "Approach to collaborative filtering recommendation based on HMM." Journal of Computer Applications 28, no. 6 (2008): 1601–4. http://dx.doi.org/10.3724/sp.j.1087.2008.01601.

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10

Shirbhate, Pranoti. "Travel-Package Recommendation Model based on Collaborative Approach." International Journal for Research in Applied Science and Engineering Technology 7, no. 5 (2019): 3809–13. http://dx.doi.org/10.22214/ijraset.2019.5625.

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11

Waqas, Jadoon, Zhang Yi, and Lei Zhang. "Collaborative neighbor representation based classification using -minimization approach." Pattern Recognition Letters 34, no. 2 (2013): 201–8. http://dx.doi.org/10.1016/j.patrec.2012.09.024.

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12

Shi, Wenxuan, and Maoqiang Xie. "A Reputation-based Collaborative Approach for Spam Filtering." AASRI Procedia 5 (2013): 220–27. http://dx.doi.org/10.1016/j.aasri.2013.10.082.

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13

Devi, D. Ganga, and S. Sampath. "A Probabilistic Approach for Item Based Collaborative Filtering." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9s (2023): 31–36. http://dx.doi.org/10.17762/ijritcc.v11i9s.7393.

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In this era, it is essential to know the customer’s necessity before they know it themselves. The Recommendation system is a sub-class of machine learning which deals with the user data to offer relevant content or product to the user based on their taste. This paper aims to develop an integrated recommendation system using statistical theory and methods. Therefore, the conventional Item Based Collaborative filtering integrated the probabilistic approach and the pseudo-probabilistic approach is proposed to update the k-NN approach. Here we synthesize the data using the Monte-Carlo approach with the binomial and the multinomial distribution. Then we examine the performance of the proposed methodologies on the synthetic data using the RMSE calculation.

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14

Rajesh, P. K., and B. MageshBabu. "Web-based collaborative conceptual design: an XML approach." International Journal of Advanced Manufacturing Technology 38, no. 5-6 (2007): 433–40. http://dx.doi.org/10.1007/s00170-007-1113-x.

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15

Thimm, Heiko. "Cloud-Based Collaborative Decision Making." International Journal of Decision Support System Technology 4, no. 4 (2012): 39–59. http://dx.doi.org/10.4018/jdsst.2012100103.

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The complexity of many decision problems of today’s globalized world requires new innovative solutions that are built upon proven decision support technology and also recent advancements in the area of information and communication technology (ICT) such as Cloud Computing and Mobile Communication. A combination of the cost-effective Cloud Computing approach with extended group decision support system technology bears several interesting unprecedented opportunities for the development of such solutions. These opportunities include ubiquitous accessibility to decision support software and, thus, the possibility to flexibly involve remote experts in group decision processes, guided access to background information, and facilitation support to direct group decision processes. The architects of such future solutions are challenged by numerous requirements that need to be considered and reflected in an integrated architectural approach. This article presents a thorough analysis of major design considerations for software solutions for collaborative decision making from a broad range of perspectives especially including the business process management perspective and the Cloud Computing perspective. The proposed architectural approach of the GRUPO-MOD system demonstrates how one can address the requirements in one integrated system architecture that supports different deployment options of Cloud Computing. A refinement of the high-level system architecture into a corresponding implementation architecture that builds on widely adopted standards such as OSGi and industry proven technology such as the Eclipse platform is also given in the article.

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16

Liu, Shihu, Xiaozhou Chen, Tauqir Ahmed Moughal, and Fusheng Yu. "Fuzzy Collaborative Clustering-Based Ranking Approach for Complex Objects." Mathematical Problems in Engineering 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/495829.

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This paper makes a discussion on the ranking problem of complex objects where each object is composed of some patterns described by individual attribute information as well as the relational information between patterns. This paper presents a fuzzy collaborative clustering-based ranking approach for this kind of ranking problem. In this approach, a referential object is employed to guide the ranking process. To achieve the final ranking result, fuzzy collaborative clustering is carried on the patterns in the referential object by using the collaborative information obtained from each ranked object. Since the collaborative information of ranking objects is represented by cluster centers and/or partition matrices, we give two forms of the proposed approach. With the aid of fuzzy collaborative clustering, the ranking results can be obtained by comparing the difference of the referential object before and after collaboration with respect to ranking objects. One can find that this proposed ranking approach is totally different from the previous ranking methods because of its completely collaborative clustering mechanism. Moreover, some synthetic examples show that our proposed ranking algorithm is valid.

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17

Begibaevna, Turemuratova Aziza, Kenjayeva Marhabo Ahmatjonovna, Saʼdullayeva Charos Yusufboy qizi, and Jumaniyozova Nigora Baxtiyorovna. "Formation ofStudents' Collaborative Skills Through Group Training Based onMulti-VectorPedagogical andPsychological Approaches inHigher Education." International Journal of Pedagogics 5, no. 4 (2025): 24–27. https://doi.org/10.37547/ijp/volume05issue04-07.

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This article is a study of improving students' collaborative skills through psychological training and pedagogical methods. We believe that it is appropriate to use collaborative learning methods in creating a new system based on multi-vector approaches in education. We will further increase the effectiveness of education by forming students' worldviews, improving collaborative skills, and consolidating their knowledge through psychological training. Through this study, we considered the most effective approach to learning in groups and collectives. We put forward the idea that this increases students' ability to remember the acquired knowledge and helps them think freely, reason, and make decisions. During this study, we conducted interviews with students through psychological training and listened to students' opinions.

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18

Gu, Xiaoyu, John E. Renaud, Leah M. Ashe, Stephen M. Batill, Amrjit S. Budhiraja, and Lee J. Krajewski. "Decision-Based Collaborative Optimization." Journal of Mechanical Design 124, no. 1 (2000): 1–13. http://dx.doi.org/10.1115/1.1432991.

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In this research a Collaborative Optimization (CO) approach for multidisciplinary systems design is used to develop a decision based design framework for non-deterministic optimization. To date CO strategies have been developed for use in application to deterministic systems design problems. In this research the decision based design (DBD) framework proposed by Hazelrigg [1,2] is modified for use in a collaborative optimization framework. The Hazelrigg framework as originally proposed provides a single level optimization strategy that combines engineering decisions with business decisions in a single level optimization. By transforming this framework for use in collaborative optimization one can decompose the business and engineering decision making processes. In the new multilevel framework of Decision Based Collaborative Optimization (DBCO) the business decisions are made at the system level. These business decisions result in a set of engineering performance targets that disciplinary engineering design teams seek to satisfy as part of subspace optimizations. The Decision Based Collaborative Optimization framework more accurately models the existing relationship between business and engineering in multidisciplinary systems design.

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19

Yurchak, I., and M. Hryhlevych. "Recommender system based on collaborative filtering." COMPUTER-INTEGRATED TECHNOLOGIES: EDUCATION, SCIENCE, PRODUCTION, no. 53 (December 16, 2023): 78–85. http://dx.doi.org/10.36910/6775-2524-0560-2023-53-12.

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Collaborative filtering is a popular technique for providing personalized recommendations in recommender systems. However, the sparsity problem and the accuracy-diversity tradeoff are major challenges that limit its performance. In this article, we propose a novel approach that combines matrix factorization with novelty metrics to improve the accuracy and diversity of recommendations. We evaluate our approach on the MovieLens dataset and compare it with several state-of-the-art techniques, including neighborhood-based methods, probabilistic models, and hybrid approaches. Our experimental results show that our method is better than other techniques in terms of both accuracy and diversity, as measured by precision, recall, and novelty metrics.

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20

Fernández, Diego, Vreixo Formoso, Fidel Cacheda, and Victor Carneiro. "A Content-Based Approach to Profile Expansion." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, no. 06 (2020): 981–1002. http://dx.doi.org/10.1142/s0218488520500385.

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Collaborative Filtering algorithms suffer from the so-called cold-start problem. In particular, when a user has rated few items, recommendations offered by these algorithms are not too accurate. Profile Expansion techniques have been described as a way to tackle this problem without bothering the user with additional information requests by increasing automatically the size of the user profile. Up to now, only collaborative approaches had been proposed for Profile Expansion. However, content-based techniques can also be used. We perform a manual analysis of a movie dataset to analyze how content features behave. According to this analysis, we propose a content-based approach, which is also combined with collaborative information. Concretely, we expose the advantages and disadvantages of the combination with a popularity feature. Moreover, a comparison to pure collaborative approaches is performed. Our approach is evaluated in a new system situation. That is, not only the active user has few ratings, but also most of the users. The results show that content-based information is useful for rating prediction. In addition, recommendations are less personalized as popularity feature acquires more relevance for item selection.

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21

Lordly, Daphne, Debbie MacLellan, Jacqui Gingras, and Jennifer Brady. "A Team-based Approach to Qualitative Inquiry: The Collaborative Retreat." Canadian Journal of Dietetic Practice and Research 73, no. 2 (2012): 91–97. http://dx.doi.org/10.3148/73.2.2012.91.

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A team of researchers undertook a collaborative qualitative study to explore beginning dietitians’ life experiences and the meaning ascribed to those experiences in the context of dietetic practice. Data were collected using Seidman's three-step in-depth phenomenological interviewing method with 12 beginning dietitians who were graduates of the three participating dietetic programs. We outline the collaborative research process and highlight a writing and data analysis technique described as the collaborative retreat, a face-to-face, two-day gathering that facilitated the researchers’ collective decision-making and organization, discussion, and analysis of this complex qualitative data set. Use of a listening guide aided researchers’ understanding and interpretation of participant voices. Researchers concluded that the overall collaborative qualitative research process was positive and self-fulfilling, and that it resulted in multiple benefits for them individually and the research project collectively. Researchers were able to work through methodological and theoretical issues as these arose, with the assistance of technology, writing, listening, and dialogue. Relationship building and relationship maintenance emerged as factors critical to the success of the research process. Collaborative research teams that are committed to listening, writing, and dialogue will find that the collaborative retreat can be a productive site of knowledge generation and mentorship.

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22

Jacobs, Robin J. "Collaborative Practice A Theory-Based Collaborative Approach to HIV/AIDS Prevention in Latino Youth." Journal for Specialists in Pediatric Nursing 13, no. 2 (2008): 126–29. http://dx.doi.org/10.1111/j.1744-6155.2008.00144.x.

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23

Xue, Zhi, Yao Xue Zhang, Yue Zhi Zhou, and Wei Hu. "A Novel Collaborative Filtering Recommendation Approach Based on Field Authorities." Advanced Materials Research 765-767 (September 2013): 989–93. http://dx.doi.org/10.4028/www.scientific.net/amr.765-767.989.

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This paper presents a novel collaborative filtering recommendation algorithm based on field authorities which simulates the real life word of mouth recommendation mode. It uses the specialistic knowledge from field authorities of different genres, and successfully addresses data sparsity and noise problems existing in traditional collaborative filtering. Meanwhile it also improves prediction accuracy and saves computational overhead effectively. Experiments on MovieLens datasets show that the accuracy of our algorithm is significantly higher than collaborative filtering approach based on experts, and has larger scope because of no external data limitations. Meanwhile, compared to traditional k-NN collaborative filtering, our algorithm has a better performance both in MAE and precision experiments, and the computational overhead has a decrease of 19.2% while they provide the same accuracy level.

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24

Sallam, Rouhia M., Mahmoud Hussein, and Hamdy M. Mousa. "An Enhanced Collaborative Filtering-based Approach for Recommender Systems." International Journal of Computer Applications 176, no. 41 (2020): 9–15. http://dx.doi.org/10.5120/ijca2020920531.

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25

N G, Prof Bhojne, Sagar Deore, Rushikesh Jagtap, Gaurav Jain, and Chirag Kalal. "Collaborative Approach based Restaurant Recommender System using Naive Bayes." IJARCCE 6, no. 4 (2017): 6–13. http://dx.doi.org/10.17148/ijarcce.2017.6402.

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26

Vadivelou, G., and E. Ilavarasan. "Collaborative Filtering Based Hybrid Approach for Web Service Recommendations." Research Journal of Applied Sciences, Engineering and Technology 8, no. 5 (2014): 615–22. http://dx.doi.org/10.19026/rjaset.8.1013.

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27

Wang, Shuang-xi, Hong-wei Ge, Jian-ping Gou, et al. "Kernelized discriminative–collaborative representation-based approach for pattern classification." Computers and Electrical Engineering 103 (October 2022): 108342. http://dx.doi.org/10.1016/j.compeleceng.2022.108342.

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28

Kittredge, Diane, Constance D. Baldwin, Miriam Bar-on, R. Franklin Trimm, and Patricia S. Beach. "One Specialty’s Collaborative Approach to Competency-Based Curriculum Development." Academic Medicine 84, no. 9 (2009): 1262–68. http://dx.doi.org/10.1097/acm.0b013e3181b18c4c.

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29

Wilson, David C., Barry Smyth, and Derry O' Sullivan. "Sparsity Reduction in Collaborative Recommendation: A Case-Based Approach." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 05 (2003): 863–84. http://dx.doi.org/10.1142/s0218001403002678.

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Recommender systems research combines techniques from user modeling and information filtering in order to build search systems that are better able to respond to the preferences of individual users during the search for a particular item or product. Collaborative recommenders leverage the preferences of communities of similar users in order to guide the search for relevant items. The success of collaborative recommendation has always been restrained by the so-called sparsity problem, in which a lack of available user similarity knowledge ultimately limits the formation of high-quality user communities and has a subsequent impact on recommender accuracy. This article presents an approach to addressing the sparsity problem by describing and evaluating how implicit similarity knowledge can be discovered and exploited using data-mining techniques and an approach to recommendation that is inspired by case-based reasoning research.

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30

Bailey, Edward N. "At the Tipping Point: A Strength-Based Collaborative Approach." Breastfeeding Medicine 5, no. 5 (2010): 201–2. http://dx.doi.org/10.1089/bfm.2010.0072.

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31

Li Sharpe, Elizabeth, Heidi Bobek, and Courtney Shihabuddin. "An Evidence-Based Interprofessional Collaborative Approach to Preceptor Development." Journal for Nurse Practitioners 20, no. 8 (2024): 105126. http://dx.doi.org/10.1016/j.nurpra.2024.105126.

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32

Bögel, Stephan, Stefan Stieglitz, and Christian Meske. "A role model-based approach for modelling collaborative processes." Business Process Management Journal 20, no. 4 (2014): 598–614. http://dx.doi.org/10.1108/bpmj-07-2013-0094.

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33

Lever, Jake, Sitanshu Gakkhar, Michael Gottlieb, et al. "A collaborative filtering-based approach to biomedical knowledge discovery." Bioinformatics 34, no. 4 (2017): 652–59. http://dx.doi.org/10.1093/bioinformatics/btx613.

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34

Deng, P.-S., and E. G. Tsacle. "A market-based computational approach to collaborative organizational learning." Journal of the Operational Research Society 54, no. 9 (2003): 924–35. http://dx.doi.org/10.1057/palgrave.jors.2601604.

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35

Kaleli, Cihan. "An entropy-based neighbor selection approach for collaborative filtering." Knowledge-Based Systems 56 (January 2014): 273–80. http://dx.doi.org/10.1016/j.knosys.2013.11.020.

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36

Bellogín, Alejandro, and Pablo Sánchez. "Collaborative filtering based on subsequence matching: A new approach." Information Sciences 418-419 (December 2017): 432–46. http://dx.doi.org/10.1016/j.ins.2017.08.016.

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37

Rosser, Elizabeth. "Evidence-based practice: the need for a collaborative approach." British Journal of Nursing 24, no. 21 (2015): 1105. http://dx.doi.org/10.12968/bjon.2015.24.21.1105.

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38

Valdiviezo-Diaz, Priscila, Fernando Ortega, Eduardo Cobos, and Raul Lara-Cabrera. "A Collaborative Filtering Approach Based on Naïve Bayes Classifier." IEEE Access 7 (2019): 108581–92. http://dx.doi.org/10.1109/access.2019.2933048.

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39

Panda, Sanjaya Kumar, Sourav Kumar Bhoi, and Munesh Singh. "A collaborative filtering recommendation algorithm based on normalization approach." Journal of Ambient Intelligence and Humanized Computing 11, no. 11 (2020): 4643–65. http://dx.doi.org/10.1007/s12652-020-01711-x.

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40

Wei, Chih-Ping, Chin-Sheng Yang, and Han-Wei Hsiao. "A collaborative filtering-based approach to personalized document clustering." Decision Support Systems 45, no. 3 (2008): 413–28. http://dx.doi.org/10.1016/j.dss.2007.05.008.

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41

Li, Yingguang, Ruijie Yan, and Jianbang Jian. "A semantics-based approach for collaborative aircraft tooling design." Advanced Engineering Informatics 24, no. 2 (2010): 149–58. http://dx.doi.org/10.1016/j.aei.2009.07.004.

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42

Pradeep, M. D., and Raj Charan. "Broad Based Benefit Model for the Elderly-Collaborative Approach." International Journal of Applied Engineering and Management Letters (IJAEML) 1, no. 2 (2018): 112–22. https://doi.org/10.5281/zenodo.1136044.

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The old age declines the functional capacity of the organs due to physiological transformation. The study of physical and psychological changes that occur in old age is called “gerontology”. Vital role is played by the legislations in granting welfare of the elderly population. The elderly welfare requires the solutions to the practical problems of the elderly. India has adopted United Nations International Plan of Action on Ageing to provide care and protection to the elders by considering them within the social category who requires special attention. The principle of equity is enshrined in the Constitution of India in its Preamble, Fundamental Rights and Directive Principles of State Policy. Elderly population belongs to the marginalised and deprived sections in India. This paper introduces Pradeep & Charan’s model of ‘Broad Based Benefit’ (BBB) for the elderly by integrating legislative and social interventions towards empowerment of elderly. This paper provides guidelines to interlink the efforts of Government and Non Government Organisaitons by using legislative and judicial interfaces to promote elderly welfare. This model guides the way for empowerment of elderly population to be self sufficient and sustainable in their lives. The study is descriptive by nature, by using primary and secondary sources of data.  

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43

Hartanto, Widhi, Noor Akhmad Setiawan, and Teguh Bharata Adji. "Serendipity Identification Using Distance-Based Approach." IJITEE (International Journal of Information Technology and Electrical Engineering) 5, no. 1 (2021): 9. http://dx.doi.org/10.22146/ijitee.62344.

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The recommendation system is a method for helping consumers to find products that fit their preferences. However, recommendations that are merely based on user preference are no longer satisfactory. Consumers expect recommendations that are novel, unexpected, and relevant. It requires the development of a serendipity recommendation system that matches the serendipity data character. However, there are still debates among researchers about the available common definition of serendipity. Therefore, our study proposes a work to identify serendipity data's character by directly using serendipity data ground truth from the famous Movielens dataset. The serendipity data identification is based on a distance-based approach using collaborative filtering and k-means clustering algorithms. Collaborative filtering is used to calculate the similarity value between data, while k-means is used to cluster the collaborative filtering data. The resulting clusters are used to determine the position of the serendipity cluster. The result of this study shows that the average distance between the recommended movie cluster and the serendipity movie cluster is 0.85 units, which is neither the closest cluster nor the farthest cluster from the recommended movie cluster.

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Oriola, Oluwafemi, Adesesan Barnabas Adeyemo, Maria Papadaki, and Eduan Kotzé. "A collaborative approach for national cybersecurity incident management." Information & Computer Security 29, no. 3 (2021): 457–84. http://dx.doi.org/10.1108/ics-02-2020-0027.

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Purpose Collaborative-based national cybersecurity incident management benefits from the huge size of incident information, large-scale information security devices and aggregation of security skills. However, no existing collaborative approach has been able to cater for multiple regulators, divergent incident views and incident reputation trust issues that national cybersecurity incident management presents. This paper aims to propose a collaborative approach to handle these issues cost-effectively. Design/methodology/approach A collaborative-based national cybersecurity incident management architecture based on ITU-T X.1056 security incident management framework is proposed. It is composed of the cooperative regulatory unit with cooperative and third-party management strategies and an execution unit, with incident handling and response strategies. Novel collaborative incident prioritization and mitigation planning models that are fit for incident handling in national cybersecurity incident management are proposed. Findings Use case depicting how the collaborative-based national cybersecurity incident management would function within a typical information and communication technology ecosystem is illustrated. The proposed collaborative approach is evaluated based on the performances of an experimental cyber-incident management system against two multistage attack scenarios. The results show that the proposed approach is more reliable compared to the existing ones based on descriptive statistics. Originality/value The approach produces better incident impact scores and rankings than standard tools. The approach reduces the total response costs by 8.33% and false positive rate by 97.20% for the first attack scenario, while it reduces the total response costs by 26.67% and false positive rate by 78.83% for the second attack scenario.

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Wang, Pu. "An Ontology-Based Collaborative Filtering Personalized Recommendation." Applied Mechanics and Materials 267 (December 2012): 79–82. http://dx.doi.org/10.4028/www.scientific.net/amm.267.79.

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Recommender systems have been successfully used to tackle the problem of information overload, where users of products have too many choices and overwhelming amount of information about each choice. Personalization is widely used in various fields to provide users with more suitable and personalized service. Many e-commerce web sites such as online shop retailers make use of recommendation systems. In order to make recommendations to a user, collaborative filtering is an important personalized recommendation technique applied widely in E-commerce. The collaborative approach faces the hard issue of cold start problem and the matrix sparsity problem. The paper presents a collaborative filtering personalized recommendation approach based on ontology in the special domain. The method combines ontology technology and item-based collaborative filtering. The given recommendation approach can tackle the traditional recommenders problems, such as matrix sparsity and cold start problems.

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SIVAKUMARAN, DR AR, YETUKURI GOUTHAMI MAHESWARI, TANGIRALA M N S KALYANI, and TELAKAPALLY RENUSRI. "NEURAL COLLABORATIVE FILTERING BASED GROUP RECOMMENDATIONS." Journal of Engineering Sciences 15, no. 10 (2024): 251–61. http://dx.doi.org/10.36893/jes.2024.v15i10.030.

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Group recommendations, where a system suggests items for a group of users rather than individuals, present a unique challenge in collaborative filtering. Traditional approaches to group recommendations often rely on aggregating individual preferences, which can lead to suboptimal results when preferences are diverse or conflicting. This paper explores a novel approach using Neural Collaborative Filtering (NCF) to improve group recommendation accuracy. NCF, which leverages deep learning techniques to model complex user-item interactions, offers a more nuanced understanding of group dynamics by incorporating both individual and group-level preferences. We propose a new NCF-based framework designed to handle group recommendations by effectively learning and integrating diverse user preferences. Our experimental results demonstrate that the proposed approach outperforms traditional group recommendation methods in terms of prediction accuracy and user satisfaction. This research highlights the potential of neural networks in enhancing group recommendation systems and provides a foundation for future developments in this area.

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Qiao-Feng Song, Qiao-Feng Song, Jun Wang Qiao-Feng Song, Ji-Xu Gao Jun Wang, and Jia-Hao Liu Ji-Xu Gao. "A Multi-edge Collaborative Computational Offloading Scheme Based on Game Theory." 電腦學刊 34, no. 5 (2023): 149–66. http://dx.doi.org/10.53106/199115992023103405011.

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<p>A common approach in existing collaborative edge computing offloading schemes is to partition tasks into independent sub-tasks and offload them to participating servers. However, in practice, these sub-tasks often have dependencies, resulting in waiting time. To address this problem, we propose a collaborative computation offloading scheme based on Stackelberg game theory and graph theory (CCOSGG). First, we introduce a task clustering method based on graph theory, which uses task reconstruction and graph partition algorithm to cluster strongly related sub-tasks into appropriately sized clusters. Second, we use Stackelberg game theory to introduce an incentive mechanism that encourages remote edges to participate in the collaborative offloading. Finally, simulation results demonstrate that the proposed scheme can minimize latency and energy consumption at different network scales.</p> <p>&nbsp;</p>

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Spoorthi, Chinivar. "Personalized Recommendations of Products to Users." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 3 (2022): 105–9. https://doi.org/10.35940/ijrte.C7274.0911322.

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<strong>Abstract:</strong> Many organizations utilize recommendation systems to increase their profitability and win over their customers, including Facebook, which suggests friends, LinkedIn, which promotes employment, Spotify, which recommends music, Netflix, which recommends movies, and Amazon, which recommends purchases. When it comes to movie recommendation system, suggestions are made based on user similarities (collaborative filtering) or by considering a specific user's behavior (content-based filtering) that he or she wishes to interact with. Using TF-IDF, cosine similarity method for content-based filtering, and deep learning for a collaborative approach, this study compares two movie recommendation system. The proposed systems are evaluated by calculating the precision and recall values. On a small dataset, a content-based filtering methodology had a precision of 5.6% whereas a collaborative approach had a precision of 57%. Collaborative filtering clearly worked better than content-based filtering. Future improvements involve creating a single hybrid recommendation system that combines a collaborative and content-based approach to improve the outcomes.

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Ifada, Noor, Nur Fitriani Dwi Putri, and Mochammad Kautsar Sophan. "Normalization based Multi-Criteria Collaborative Filtering Approach for Recommendation System." Rekayasa 13, no. 3 (2020): 234–39. http://dx.doi.org/10.21107/rekayasa.v13i3.8545.

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A multi-criteria collaborative filtering recommendation system allows its users to rate items based on several criteria. Users instinctively have different tendencies in rating items that some of them are quite generous while others tend to be pretty stingy. Given the diverse rating patterns, implementing a normalization technique in the system is beneficial to reveal the latent relationship within the multi-criteria rating data. This paper analyses and compares the performances of two methods that implement the normalization based multi-criteria collaborative filtering approach. The framework of the method development consists of three main processes, i.e.: multi-criteria rating representation, multi-criteria rating normalization, and rating prediction using a multi-criteria collaborative filtering approach. The developed methods are labelled based on the implemented normalization technique and multi-criteria collaborative filtering approaches, i.e., Decoupling normalization and Multi-Criteria User-based approach (DMCUser) and Decoupling normalization and Multi-Criteria User-based approach (DMCItem). Experiment results using the real-world Yelp Dataset show that DMCItem outperforms DMCUser at most in terms of Precision and Normalized Discounted Cumulative Gain (NDCG). Though DMCUser can perform better than DMCItem at large , it is still more practical to implement DMCItem rather than DMCUser in a multi-criteria recommendation system since users tend to show more interest to items at the top list.

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Lee, Seulki, and Sonia M. Ospina. "A Framework for Assessing Accountability in Collaborative Governance: A Process-Based Approach." Perspectives on Public Management and Governance 5, no. 1 (2022): 63–75. http://dx.doi.org/10.1093/ppmgov/gvab031.

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Abstract Despite the complexities involved around the accountability mechanisms of collaborative governance, little is known about how to assess accountability at the network level and disentangle possible accountability deficits. This study first explicates the nature of collaborative governance accountability in contrast to accountability in traditional public administration and market-based governance. The analysis shows how collaborative governance accountability is distinctive: (a) accountability relationships shift from bilateral to multilateral; (b) horizontal as well as vertical accountability relationships are involved; (c) not only formal standards but also informal norms are used; and (d) accountability challenges move from control/audit issues to trust-building and paradox management issues. We then propose a framework for accountability in collaborative governance, drawing its dimensions from the process-based accountability research. Our framework builds on three dimensions of collaborative accountability—information, discussion, and consequences—and elaborates on their components and indicators. Based on the framework, questions to guide future research are provided, focusing on tensions and paradoxes that can arise in each process dimension as primary accountability challenges in collaborative contexts.

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