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Zeitschriftenartikel zum Thema „Système de Recommendation“

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Veröffentlicht am 8. Februar 2025

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1

Gupta, Aayush, Akshat Singh Gour, Akshat Singh Rathore und Akshay Keswani. „Book Recommendation System“. International Journal of Research Publication and Reviews 5, Nr. 5 (02.05.2024): 1519–22. http://dx.doi.org/10.55248/gengpi.5.0524.1128.

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Sharma, Abhishek, Alokit Sharma, Ankita Arya und Asit Joshi. „Crop Recommendation System“. International Journal of Research Publication and Reviews 5, Nr. 5 (02.05.2024): 1095–98. http://dx.doi.org/10.55248/gengpi.5.0524.1124.

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3

Prakash Gupta, Nipun, und Durgesh Kumar. „Music Recommendation System“. International Journal of Science and Research (IJSR) 10, Nr. 5 (27.05.2021): 1118–23. https://doi.org/10.21275/sr21524230547.

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4

Liu, Duen-Ren, Kuan-Yu Chen, Yun-Cheng Chou und Jia-Huei Lee. „Online recommendations based on dynamic adjustment of recommendation lists“. Knowledge-Based Systems 161 (Dezember 2018): 375–89. http://dx.doi.org/10.1016/j.knosys.2018.07.038.

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5

D., Dr Vanathi. „Review of Recommendation System Methodologies“. International Journal of Psychosocial Rehabilitation 23, Nr. 1 (29.03.2019): 524–31. http://dx.doi.org/10.37200/ijpr/v23i1/pr190495.

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6

Rashmi, A., Y. Ramachandra und Dr U. P. Kulkarni. „Preference Based Book Recommendation System“. Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (31.10.2016): 183–85. http://dx.doi.org/10.9756/bijsesc.8272.

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7

Sahare, Yash, Krunal Kamble, Tushar Bhakte, Rohit Warkade, Amrapali Besekar, Sumedh Patil und Dr Harish Gorewar. „Emotion Based Music Recommendation System“. International Journal of Research Publication and Reviews 4, Nr. 12 (02.12.2023): 818–34. http://dx.doi.org/10.55248/gengpi.4.1223.123327.

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8

Phan, Lan Phuong, Hung Huu Huynh und Hiep Xuan Huynh. „Implicative Rating-Based Hybrid Recommendation Systems“. International Journal of Machine Learning and Computing 8, Nr. 3 (Juni 2018): 223–28. http://dx.doi.org/10.18178/ijmlc.2018.8.3.691.

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9

Verma, Rakesh, Prince Verma und Abhishek Bhardwaja. „Hotel Recommendation System Using Machine Learning“. International Journal of Science and Research (IJSR) 11, Nr. 12 (05.12.2022): 550–54. http://dx.doi.org/10.21275/sr221117143622.

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10

Domlur Seetharama, Yogananda. „Automated Item Recommendation Systems for Retail Stores“. International Journal of Science and Research (IJSR) 11, Nr. 1 (05.01.2022): 1653–63. http://dx.doi.org/10.21275/sr24809044235.

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11

Putri, Salsa Nadira, Tjut Awaliyah Zuraiyah und Dinar Munggaran Akhmad. „Recommender Systems using Hybrid Demographic and Content-Based Filtering methods for UMKM Products“. Komputasi: Jurnal Ilmiah Ilmu Komputer dan Matematika 21, Nr. 1 (29.01.2024): 31–44. http://dx.doi.org/10.33751/komputasi.v21i1.8991.

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Marketing digitization such as e-commerce is needed by micro, small and medium enterprises (UMKM) in Bogor City and Regency so that the products are more easily accessible to consumers. One of the digital marketing that is commonly used by consumers is an e-commerce website. The Recommendation System is implemented into e-commerce websites to increase consumer convenience in online shopping. The recommendation systems method applied is Demographic Filtering and Content-based Filtering. Demographic Filtering uses IMDB Weighted Rating calculations which generate recommendations globally and give recommendations based on each product's IMDB Weighted score. Content-based Filtering uses Cosine Distance calculations which generate personal recommendations and give recommendations based on the score cosine distance of each product in the form of a presentation of the similarity of products that have been purchased with other products. This research uses 107 UMKM fashion and craft product data that was obtained from Bogor City Regional Craft Center which sells various kinds of UMKM products from Bogor City and Regency. Data preprocessing is then carried out on the raw data, with the Data Cleaning, Data Transforming and Data Splitting stages which divide the data in a ratio of 80:20. The accuracy of Demographic Filtering Recommendation System reaches 82.7% and Content-based Filtering Recommendation System reaches 100%.

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D., Mhamdi. „Job Recommendation System based on Text Analysis“. Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (31.03.2020): 1025–30. http://dx.doi.org/10.5373/jardcs/v12sp4/20201575.

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Pramod Wani, Pushkar. „Analysis of Recommendation System in Cloud Platform“. International Journal of Science and Research (IJSR) 11, Nr. 2 (05.02.2022): 64–66. http://dx.doi.org/10.21275/sr22130223310.

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14

RAHMANI, Khalil, und Belkacem Kamel-Eddine FETITA. „Pour une optimisation de la qualité de la formation doctorale à l’université algérienne : rôle du processus d’évaluation“. Langues & Cultures 5, Nr. 02 (10.12.2024): 319–33. https://doi.org/10.62339/jlc.v5i02.306.

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Cet article, s'appuyant sur une approche descriptive et analytique, se consacre à l'examen approfondi des méthodes et du processus d'évaluation des doctorants et leur impact sur la qualité de la formation doctorale. Il se focalise en particulier sur les différentes étapes clés d'évaluation des doctorants, couvrant l'admission au programme, le suivi pendant le cursus, ainsi que l'évaluation post-obtention du doctorat, en considérant les différences entre les types de systèmes de formation doctorale, avec un focus sur le système de la formation doctorale en Algérie. Cette étude vise à répondre à la question : Comment les méthodes et processus d'évaluation des doctorants peuvent-ils contribuer à améliorer la qualité de la formation doctorale ? Elle conclut que le système d’évaluation en phase doctorale en Algérie nécessite une réexamination, et propose des suggestions pour améliorer la qualité de la formation doctorale à toutes ses étapes ainsi que la réputation des institutions et l’encadrement qui l’assurent. Abstract This article, based on a descriptive and analytical approach, delves into a comprehensive examination of the methods, processes, and practices for evaluating doctoral students and their impact on the quality of doctoral training. It specifically focuses on key evaluation stages for doctoral students, including admission to the program, ongoing monitoring during the course of study, and post-doctoral assessment. The study considers variations across different doctoral training systems, with a particular emphasis on the Algerian doctoral training system. The central question addressed is: How can the evaluation methods and processes for doctoral students contribute to enhancing the quality of doctoral training? The study concludes that the Algerian doctoral evaluation system requires reevaluation and offers recommendations for improving the quality of doctoral training at all stages, as well as enhancing the reputation of institutions and the supervision they provide.

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K, SREENIVASA RAJU. „Recommendation System in Social Media: A Case Study“. Journal of Research on the Lepidoptera 51, Nr. 1 (28.02.2020): 574–79. http://dx.doi.org/10.36872/lepi/v51i1/301051.

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Yochum, Phatpicha, Liang Chang, Tianlong Gu und Manli Zhu. „Learning Sentiment over Network Embedding for Recommendation System“. International Journal of Machine Learning and Computing 11, Nr. 1 (Januar 2021): 12–20. http://dx.doi.org/10.18178/ijmlc.2021.11.1.1008.

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With the rapid development of Internet, various unstructured information, such as user-generated content, textual reviews, and implicit or explicit feedbacks have grown continuously. Though structured knowledge bases (KBs) which consist of a large number of triples exhibit great advantages in recommendation field recently. In this paper, we propose a novel approach to learn sentiment over network embedding for recommendation system based on the knowledge graph which we have been built, that is, we integrate the network embedding method with the sentiment of user reviews. Specifically, we use the typical network embedding method node2vec to embed the large-scale structured data into a low-dimensional vector space to capture the internal semantic information of users and attractions and apply the user weight scoring which is the combination of user review ratings and textual reviews to get similar attractions among users. Experimental results on real-world dataset verified the superior recommendation performance on precision, recall, and F-measure of our approach compared with state-of-the-art baselines.

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Gupta, Charul, und Dr Srikanth V. „Machine Learning- Recommendation System for Personalized Investment Portfolios“. International Journal of Research Publication and Reviews 5, Nr. 2 (27.02.2024): 3690–94. http://dx.doi.org/10.55248/gengpi.5.0224.0630.

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A Vida Mastrika, G., und I. G A G. „Music Recommendation System using Case-Based Reasoning Method“. International Journal of Science and Research (IJSR) 10, Nr. 1 (27.01.2021): 1047–50. https://doi.org/10.21275/sr21115194309.

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19

Seitay, Yernar. „Enhancing Recommendation Systems with Fuzzy Logic-Based Collaborative Filtering“. International Journal of Science and Research (IJSR) 13, Nr. 11 (05.11.2024): 1485–88. http://dx.doi.org/10.21275/sr241124204713.

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Boscart, Veronique M., Susan McNeill und Doris Grinspun. „Dementia Care in Canada: Nursing Recommendations“. Canadian Journal on Aging / La Revue canadienne du vieillissement 38, Nr. 03 (06.08.2019): 407–18. http://dx.doi.org/10.1017/s071498081800065x.

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RÉSUMÉAu Canada, la prévention et la prise en charge des démences ont atteint un point tournant. Bien que le taux de diagnostic des démences soit encore bas, le nombre de personnes qui en sont atteintes continue d’augmenter. Les politiques canadiennes en matière de soins de santé ont fait en sorte qu’un plus grand nombre de personnes avec démence vivent à la maison, où les soins sont principalement assurés par la famille, des amis ou des proches. Cette Note de politique présente un aperçu d’un document conjoint de l’Association canadienne des infirmières et infirmiers en gérontologie (AIIG) et de l’Association des infirmières et infirmiers autorisés de l’Ontario (AIIAO) devant le Comité sénatorial permanent des affaires sociales, des sciences et de la technologie. Le document expose le cadre contextuel et les recommandations pour les soins liés à la démence au Canada dans cinq domaines clés : les ressources du système de santé, la formation des prestataires de soins de santé, le logement, les partenaires de soins et l’intégration des soutiens offerts en services sociaux et de santé. Dans le cadre de ces cinq domaines clés, des interventions en matière de santé et de politiques sociales ont été examinées.

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Svystun, Oksana, und Iryna Yurchak. „Recommendation Dialog System for Selecting the Computer Hardware Configuration“. Advances in Cyber-Physical Systems 6, Nr. 1 (23.01.2021): 70–76. http://dx.doi.org/10.23939/acps2021.01.070.

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The development of modern technologies is extremely fast. Every day more and more new and various means appear in the world to improve the quality of life and more. It is not possible for a person to process all this new information on the scale and speed with which this information appears. Everyone has their own preferences and wants to receive and obtain information about certain events or things that they are interested in. This has become one of the most important reasons for creating referral systems. The purpose of developing a recommended dialog system for selecting the computer hardware configuration is to help users choose the computer’s hardware characteristics to suit their requirements and needs. This system is suitable for being used by both qualified users in this field, and for users unfamiliar with computer technology. There has been an attempt to analyze the types of recommendation dialog systems and their varieties in the paper. The principle of operation of the recommendation dialog system in the form of a chat bot made on the platform of the messenger Telegram has been considered.

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Zitar, Raed Abu, Ammar EL-Hassan und Oraib AL-Sahlee. „Deep Learning Recommendation System for Course Learning Outcomes Assessment“. Journal of Advanced Research in Dynamical and Control Systems 11, Nr. 10-SPECIAL ISSUE (31.10.2019): 1491–78. http://dx.doi.org/10.5373/jardcs/v11sp10/20192993.

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23

Praveen N., Praveen N., und Pushparaj U. Pushparaj U. „Service Recommendation and Visualization System Based on SOM Methodology“. International Journal of Scientific Research 3, Nr. 4 (01.06.2012): 205–7. http://dx.doi.org/10.15373/22778179/apr2014/70.

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24

Rtili, Mohammed Kamal, Ali Dahmani und Mohamed Khaldi. „Recommendation System Based on the Learners' Tracks in an Intelligent Tutoring System“. Journal of Advances in Computer Networks 2, Nr. 1 (2014): 40–43. http://dx.doi.org/10.7763/jacn.2014.v2.79.

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French, Susan E. „The Report on Institutional Care of the Elderly in Ontario. Commissioned by the Ontario Association of Registered Nursing Assistants1986, $10.00.“ Canadian Journal on Aging / La Revue canadienne du vieillissement 6, Nr. 3 (1987): 241–47. http://dx.doi.org/10.1017/s071498080000845x.

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RÉSUMÉCe rapport d'une étude mise au point par l'Association des assistantes infirmières diplômées de l'Ontario brosse un tableau sur les soins hospitaliers offerts aux personnes du troisième âge tel que perçus par les travailleurs de front, soit les assistantes infirmières diplâmées. Ce document nous présente également le point de vue de l'association face aux services de santé requis par ce segment de la population en Ontario et le rôle que pourrait interpréter l'assistante au sein de ce système. Parmi les recommendations, on retrouve des stratégies qui aideraient à promouvoir l'humanisation des soins hospitaliers accordés aux gens âgés. Également, le rapport endosse le besoin d'un système de soins intégŕes continuel. Il contient des recommendations qui reflètent le désir du groupe d'évoluer d'un rôle d'assistant vers un rôle plus important, d'élargir le champ de leurs activités et d'intensifier leurs fonctions, jusqu'à présent négligeables, au sein de la communauté.

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Keerthi, Potnuru, und Manisha Das. „An Analysis of Music Recommendation Systems Tailored to Emotional States“. International Journal of Research Publication and Reviews 5, Nr. 11 (Januar 2024): 6640–44. https://doi.org/10.55248/gengpi.5.1124.3408.

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Silvester, Sinzy, und Shaji Kurain. „Dual-blend insight recommendation system for e-commerce recommendations and enhance personalization“. Indonesian Journal of Electrical Engineering and Computer Science 34, Nr. 2 (01.05.2024): 1181. http://dx.doi.org/10.11591/ijeecs.v34.i2.pp1181-1191.

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E-commerce, short for electronic commerce, refers to the buying and selling of goods and services over the internet. This digital transaction model has revolutionized the way businesses operate and consumers shop. In response to the burgeoning complexity of e-commerce datasets, this work addresses the need for advanced recommendation systems. This work introduces the dual-blend insight recommendation system (DIRS) model for personalized e-commerce recommendation system. The DIRS model involves dataset loading, preprocessing, and feature extraction, enabling training with recurrent neural network (RNN) and Bayesian personalized ranking (BPR) models. Recommendations are generated based on user-defined functions, i.e., location and session, and evaluation metrics such as hit rate (HR) and mean reciprocal rate (MRR) highlight DIRS’s superior performance. The model is evaluated using the Tmall dataset. Results reveal DIRS consistently outperforms alternative algorithms, showcasing its effectiveness in 10k and 20k recommendation sets. This study provides valuable insights into optimizing e-commerce recommendations, emphasizing DIRS as a powerful model for enhancing user experience and engagement.

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Yang, Xue. „Influence of informational factors on purchase intention in social recommender systems“. Online Information Review 44, Nr. 2 (05.11.2018): 417–31. http://dx.doi.org/10.1108/oir-12-2016-0360.

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Purpose Social recommender systems have recently gained increasing popularity. The purpose of this paper is to investigate the influences of informational factors on purchase intention in social recommender systems. Design/methodology/approach Specifically, this study validated the mediating effect of trust in recommendations and the perceived value between informational factors and consumers’ purchase intention. Findings The results confirm that recommendation persuasiveness was a strong predictor of trust in recommendations and perceived value. Recommendation completeness was positively related to trust in recommendations and perceived value as well. Trust in recommendations and perceived value was found to be strong drivers of purchase intention. Originality/value The author identifies two sets of informational factors, i.e. recommendation persuasiveness and recommendation completeness, which are relevant to consumer attitudes. The current study proved that informational factors on consumers’ purchase intention are fully mediated through trust in recommendations and perceived value.

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Dhumal, Priya Ramdas, Poonam Maruti Dumbare, Satyabhama Dnyanoba Mane und Trushali Pandurang Sandbhor. „Parallel Patient Treatment Time Prediction Using Effective Queuing Recommendation System“. Journal of Advances and Scholarly Researches in Allied Education 15, Nr. 2 (01.04.2018): 726–29. http://dx.doi.org/10.29070/15/57073.

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Verma Abhishek Amarpreet, Rakesh. „An Effective Hotel Recommendation System Based on Ensemble stacking Methods“. International Journal of Science and Research (IJSR) 12, Nr. 5 (05.05.2023): 2390–94. http://dx.doi.org/10.21275/sr23527114952.

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CINAR, ZEYNEP B., und HALUK O. BINGOL. „GETTING RECOMMENDATION IS NOT ALWAYS BETTER IN ITERATED PRISONER’S DILEMMA“. Advances in Complex Systems 23, Nr. 05 (August 2020): 2050013. http://dx.doi.org/10.1142/s0219525920500137.

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We present an extended version of the Iterated Prisoner’s Dilemma game in which agents with limited memory receive recommendations about the unknown opponents to decide whether to play with. Since agents can receive more than one recommendation about the same opponent, they have to evaluate the recommendations according to their disposition such as optimist, pessimist, or realist. They keep their first hand experience in their memory. Since agents have limited memory, they have to use different forgetting strategies. Our results show that getting recommendations does not always perform better. With the support of recommendation, cooperators can beat defectors. We observe that realist performs the best and optimist the worse.

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Vellino, Andre. „Recommending research articles using citation data“. Library Hi Tech 33, Nr. 4 (16.11.2015): 597–609. http://dx.doi.org/10.1108/lht-06-2015-0063.

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Purpose – The purpose of this paper is to present an empirical comparison between the recommendations generated by a citation-based recommender for research articles in a digital library with those produced by a user-based recommender (ExLibris “bX”). Design/methodology/approach – For these computer experiments 9,453 articles were randomly selected from among 6.6 M articles in a digital library as starting points for generating recommendations. The same seed articles were used to generate recommendations in both recommender systems and the resulting recommendations were compared according to the “semantic distance” between the seed articles and the recommended ones, the coverage of the recommendations and the spread in publication dates between the seed and the resulting recommendations. Findings – Out of the 9,453 test runs, the recommendation coverage was 30 per cent for the user-based recommender vs 24 per cent for the citation-based one. Only 12 per cent of seed articles produced recommendations with both recommenders and none of the recommended articles were the same. Both recommenders yielded recommendations with about the same semantic distance between the seed article and the recommended articles. The average differences between the publication dates of the recommended articles and the seed articles is dramatically greater for the citation-based recommender (+7.6 years) compared with the forward-looking user-based recommender. Originality/value – This paper reports on the only known empirical comparison between the Ex Librix “bX” recommendation system and a citation-based collaborative recommendation system. It extends prior preliminary findings with a larger data set and with an analysis of the publication dates of recommendations for each system.

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KABORÉ, Souleymane, und Michel GERVAIS. „Quels systèmes d'animation pour l'entreprise Burkinabè?“ Management international 11, Nr. 3 (2007): 17–35. http://dx.doi.org/10.59876/a-bhkd-688a.

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This article proposes to think of modes of animation which would be appropriate to the Burkinabe Company by looking further into the multiethnic dimension of the problem. An investigation, taking again the cultural dimensions defined by Hofstede and of dimensions more specifically African, analyzes the cultural sensitivities of the employees. Then, recommendations as regards structures of animation are made.

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Kumar, Praveen, Mukesh Kumar Gupta, Channapragada Rama Seshagiri Rao, M. Bhavsingh und M. Srilakshmi. „A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems“. International Journal on Recent and Innovation Trends in Computing and Communication 11, Nr. 3s (11.03.2023): 184–92. http://dx.doi.org/10.17762/ijritcc.v11i3s.6180.

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Collaborative Filtering (CF) is a widely used technique in recommendation systems to suggest items to users based on their previous interactions with the system. CF involves finding correlations between the preferences of different users and using those correlations to provide recommendations. This technique can be divided into user-based and item-based CF, both of which utilize similarity metrics to generate recommendations. Content-based filtering is another commonly used recommendation technique that analyzes the attributes of items to suggest similar items. To enhance the accuracy of recommendation systems, hybrid algorithms that combine CF and content-based filtering techniques have been developed. These hybrid systems leverage the strengths of both approaches to provide more accurate and personalized recommendations. In conclusion, collaborative filtering is an essential technique in recommendation systems, and the use of various similarity metrics and hybrid techniques can enhance the quality of recommendations.

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Amitha, Amitha, und Merin Meleet. „A System for Recommendation of Medication Using Gaussian Naïve Bayes Classifier“. International Journal of Innovative Research in Computer Science & Technology 7, Nr. 3 (Mai 2019): 100–103. http://dx.doi.org/10.21276/ijircst.2019.7.3.13.

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K., Dhinakaran. „Distributed Data Analytics for Improving Indian Economical Growth Using Recommendation System“. Journal of Advanced Research in Dynamical and Control Systems 12, SP4 (31.03.2020): 134–40. http://dx.doi.org/10.5373/jardcs/v12sp4/20201474.

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Sukenda, Ari Purno Wahyu und Sunjana. „Medicine Product Recommendation System using Apriori Algorithm and Fp-Growth Algorithm“. International Journal of Psychosocial Rehabilitation 24, Nr. 02 (12.02.2020): 3208–11. http://dx.doi.org/10.37200/ijpr/v24i2/pr200629.

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Luo, Changri, Tingting He, Xinhua Zhang und Zibo Zhou. „Learning Forum Posts Topic Discovery and Its Application in Recommendation System“. Journal of Software 10, Nr. 4 (April 2015): 392–402. http://dx.doi.org/10.17706/jsw.10.4.392-402.

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Kim, Jaehyuk, und Yeunwoong Kyung. „Activity-based Friend Recommendation System (ARS) in Location-based Social Network“. Webology 19, Nr. 1 (20.01.2022): 4482–90. http://dx.doi.org/10.14704/web/v19i1/web19295.

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Common friend and place recommendation services in Location-based Social Network (LBSN) is based on user’s location tracking. However, since each user can do different activities even in the same place, location data is not enough to provide accurate recommendation for LSBN. To address this problem, Activity-based friend and place Recommendation System (ARS) is proposed. ARS considers two additional factors to improve recommendation accuracy: time and activity. ARS collects the time-related activity and location data from users through the developed scheduler application and then performs the recommendation for users based on the calculated similarity among them. Performance evaluation shows that ARS can provide accurate recommendation between users who have similar activity and location patterns according to time.

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Kohut, Yurii, und Iryna Yurchak. „Recommendation System for Purchasing Goods Based on the Decision Tree Algorithm“. Advances in Cyber-Physical Systems 6, Nr. 2 (17.12.2021): 121–27. http://dx.doi.org/10.23939/acps2021.02.121.

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Over the past few years, interest in applications related to recommendation systems has increased significantly. Many modern services create recommendation systems that, based on user profile information and his behavior. This services determine which objects or products may be interesting to users. Recommendation systems are a modern tool for understanding customer needs. The main methods of constructing recommendation systems are the content-based filtering method and the collaborative filtering method. This article presents the implementation of these methods based on decision trees. The content-based filtering method is based on the description of the object and the customer’s preference profile. An object description is a finite set of its descriptors, such as keywords, binary descriptors, etc., and a preference profile is a weighted vector of object descriptors in which scales reflect the importance of each descriptor to the client and its contribution to the final decision. This model selects items that are similar to the customer’s favorite items before. The second model, which implements the method of collaborative filtering, is based on information about the history of behavior of all customers on the resource: data on their purchases, assessments of product quality, reviews, marked product. The model finds clients that are similar in behavior and the recommendation is based on their assessments of this element. Voting was used to combine the results issued by individual models — the best result is chosen from the results of two models of the ensemble. This approach minimizes the impact of randomness and averages the errors of each model. The aim: The purpose of work is to create real competitive recommendation system for short period of time and minimum costs.

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Gaur, Shubham, Rishabh Naulakha, Khushal Hanswal, Meenakshi Sharma und Abhishek Mohanty. „Multi - Category & Multi - Criteria Recommendation System using Collaborative Based Filtering“. International Journal of Science and Research (IJSR) 10, Nr. 8 (27.08.2021): 1076–80. https://doi.org/10.21275/mr21824001401.

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Das, Joydeep, Subhashis Majumder und Kalyani Mali. „Clustering Techniques to Improve Scalability and Accuracy of Recommender Systems“. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, Nr. 04 (August 2021): 621–51. http://dx.doi.org/10.1142/s0218488521500276.

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Recommender systems have emerged as a class of essential tools in the success of modern e-commerce applications. These applications typically handle large datasets and often face challenges like data sparsity and scalability. Clustering techniques help to reduce the computational time needed for recommendation as well as handle the sparsity problem more efficiently. Traditional clustering based recommender systems create partitions (clusters) of the user-item rating matrix and execute the recommendation algorithm in the clusters separately in order to decrease the overall runtime of the system. Each user or item generally belong to at most one cluster. However, it may so happen that some users (boundary users) present in a particular cluster exhibit higher similarity with the preferences of the users residing in the nearby clusters than the ones present in their own cluster. Therefore, we propose a clustering based scalable recommendation algorithm that has a provision for switching a user from its original cluster to another cluster in order to provide more accurate recommendations. For a user belonging to multiple clusters, we aggregate recommendations from those clusters to which the user belongs in order to produce the final set of recommendations to that user. In this work, we propose two types of clustering, one on the basis of rating and the other on the basis of frequency and then compare their performances. Finally, we explore the applicability of cluster ensembles techniques in the proposed method. Our aim is to develop a recommendation framework that can scale well to handle large datasets without much affecting the recommendation quality. The outcomes of our experiments clearly demonstrate the scalability as well as efficacy of our method. It reduces the runtime of the baseline CF algorithm by a minimum of 58% and a maximum of 90% for MovieLens-10M dataset, and a minimum of 42% and a maximum of 84% for MovieLens-20M dataset. The accuracies of recommendations in terms of F1, MAP and NDCG metrics are also better than the existing clustering based recommender systems.

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Villavicencio, Christian, Silvia Schiaffino, Jorge Andrés Diaz-Pace und Ariel Monteserin. „An approach for explaining group recommendations based on negotiation information“. IAES International Journal of Artificial Intelligence (IJ-AI) 13, Nr. 1 (01.03.2024): 162. http://dx.doi.org/10.11591/ijai.v13.i1.pp162-173.

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Explaining group recommendations has gained importance over the last years. Although the topic of recommendation explanation has received attention in the context of single-user recommendations, only a few group recommender systems (GRS) currently provide explanations for their group recommendations. However, those GRS that support explanations, provide either explanations being highly reliant on the aggregation technique used for generating the recommendation (most of them trying to tackle shortcomings of the underlying technique), or explanations with a rich content but requiring users to provide considerable additional data. In this article, we present a novel approach for providing explanations of group recommendations, which are generated by a GRS based on multi-agent negotiation techniques. An evaluation of our approach with a user study in the movies domain has shown promising results. Explanations provided by our GRS system helped users during the decision-making process, since they modiﬁed the feedback given to recommended items. This is an improvement with respect to systems that do not provide explanations for their recommendations.

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Huang, Xiao, Pengjie Ren, Zhaochun Ren, Fei Sun, Xiangnan He, Dawei Yin und Maarten de Rijke. „Report on the international workshop on natural language processing for recommendations (NLP4REC 2020) workshop held at WSDM 2020“. ACM SIGIR Forum 54, Nr. 1 (Juni 2020): 1–5. http://dx.doi.org/10.1145/3451964.3451970.

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This paper summarizes the outcomes of the International Workshop on Natural Language Processing for Recommendations (NLP4REC 2020), held in Houston, USA, on February 7, 2020, during WSDM 2020. The purpose of this workshop was to explore the potential research topics and industrial applications in leveraging natural language processing techniques to tackle the challenges in constructing more intelligent recommender systems. Specific topics included, but were not limited to knowledge-aware recommendation, explainable recommendation, conversational recommendation, and sequential recommendation.

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Buck, R. P., S. Rondinini, A. K. Covington, F. G. K. Baucke, Christopher M. A. Brett, M. F. Camoes, M. J. T. Milton et al. „Measurement of pH. Definition, standards, and procedures (IUPAC Recommendations 2002)“. Pure and Applied Chemistry 74, Nr. 11 (01.01.2002): 2169–200. http://dx.doi.org/10.1351/pac200274112169.

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The definition of a “primary method of measurement ” has permitted a full consideration of the definition of primary standards for pH, determined by a primary method (cell without transference, Harned cell), of the definition of secondary standards by secondary methods, and of the question whether pH, as a conventional quantity, can be incorporated within the internationally accepted system of measurement, the International System of Units (SI, Système International d’ Unités). This approach has enabled resolution of the previous compromise IUPAC 1985 Recommendations [Pure Appl. Chem.57, 531 (1985)]. Furthermore, incorporation of the uncertainties for the primary method, and for all subsequent measurements, permits the uncertainties for all procedures to be linked to the primary standards by an unbroken chain of comparisons. Thus, a rational choice can be made by the analyst of the appropriate procedure to achieve the target uncertainty of sample pH. Accordingly, this document explains IUPAC recommended definitions, procedures, and terminology relating to pH measurements in dilute aqueous solutions in the temperature range 5-50 °C. Details are given of the primary and secondary methods for measuring pH and the rationale for the assignment of pH values with appropriate uncertainties to selected primary and secondary substances.

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Setapa, Sharipah, und Tengku Puteri Suhilah. „An Access Control List for Role-Based System: An Observation and Recommendation“. International Journal of Information and Education Technology 4, Nr. 6 (2014): 468–72. http://dx.doi.org/10.7763/ijiet.2014.v4.452.

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Gupta, Shivani. „Classifiers Recommendation System for Overlapped Software Defect Prediction Using Multi-Label Framework“. Journal of Advanced Research in Dynamical and Control Systems 12, SP3 (28.02.2020): 1472–78. http://dx.doi.org/10.5373/jardcs/v12sp3/20201399.

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Najafabadi, Maryam Khanian, Azlinah Mohamed, Madhavan A/L Balan Nair und Sayed Mojtaba Tabibian. „An Effective Collaborative User Model Using Hybrid Clustering Recommendation Methods“. Ingénierie des systèmes d information 26, Nr. 2 (30.04.2021): 151–58. http://dx.doi.org/10.18280/isi.260202.

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Collaborative Filtering (CF) has been known as the most successful recommendation technique in which recommendations are made based on the past rating records from like-minded users. Significant growth of users and items have negatively affected the efficiency of CF and pose key issues related to computational aspects and the quality of recommendation such as high dimensionality and data sparsity. In this study, a hybrid method was proposed and was capable to solve the mentioned problems using a neighborhood selection process for each user through two clustering algorithms which were item-based k-means clustering and user-based Fuzzy Clustering. Item-based k-means clustering was applied because of its advantages in computational time and hence it is able to address the high dimensionality issues. To create user groups and find the correlation between users, we employed the user-based Fuzzy Clustering and it has not yet been used in user-based CF clustering. This clustering can calculate the degree of membership among users into set of clustered items. Furthermore, a new similarity metric was designed to compute the similarity value among users with affecting the output of user-based Fuzzy Clustering. This metric is an alternative to the basic similarity metrics in CF and it has been proven to provide high-quality recommendations and a noticeable improvement on the accuracy of recommendations to the users. The proposed method has been evaluated using two benchmark datasets, MovieLens and LastFM in order to make a comparison with the existing recommendation methods.

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Liu, Duen-Ren, Yun-Cheng Chou und Ciao-Ting Jian. „Integrating collaborative topic modeling and diversity for movie recommendations during news browsing“. Kybernetes 49, Nr. 11 (27.11.2019): 2633–49. http://dx.doi.org/10.1108/k-08-2019-0578.

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Purpose Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits. Recommending movie information to users reading news online can enhance the impression of diverse information and may consequently improve benefits. Accordingly, providing online movie recommendations can improve users’ satisfactions with the website, and thus is an important trend for online news websites. This study aims to propose a novel online recommendation method for recommending movie information to users when they are browsing news articles. Design/methodology/approach Association rule mining is applied to users’ news and movie browsing to find latent associations between news and movies. A novel online recommendation approach is proposed based on latent Dirichlet allocation (LDA), enhanced collaborative topic modeling (ECTM) and the diversity of recommendations. The performance of proposed approach is evaluated via an online evaluation on a real news website. Findings The online evaluation results show that the click-through rate can be improved by the proposed hybrid method integrating recommendation diversity, LDA, ECTM and users’ online interests, which are adapted to the current browsing news. The experiment results also show that considering recommendation diversity can achieve better performance. Originality/value Existing studies had not investigated the problem of recommending movie information to users while they are reading news online. To address this problem, a novel hybrid recommendation method is proposed for dealing with cross-type recommendation tasks and the cold-start issue. Moreover, the proposed method is implemented and evaluated online in a real world news website, while such online evaluation is rarely conducted in related research. This work contributes to deriving user’s online preferences for cross-type recommendations by integrating recommendation diversity, LDA, ECTM and adaptive online interests. The research findings also contribute to increasing the commercial value of the online news websites.

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Wenwen, Zhou. „Building an Urban Smart Community System Based on Association Rule Algorithms“. Security and Communication Networks 2022 (19.07.2022): 1–11. http://dx.doi.org/10.1155/2022/8773259.

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Intelligent system development is an integral component of smart community development and has a significant impact on the development of smart communities. Some cities continue to implement personalized smart community services, resulting in the formation of smart city communities with unique characteristics. Urban smart communities are based on the principle of owner-occupant convenience, integrating a wealth of community information and making it more relevant to each and every resident through intelligent management. Increasing information transmission rates have enhanced the ability of smart community systems to integrate information, but the smart community recommendation method is still based on traditional categorized recommendations. This paper addresses the deficiency of recommended information in smart urban communities. By analyzing user interaction and operation data, we can determine the interest and recognition of browsing attractions among users. Compared to conventional classification recommendations, weighted association rules can identify potentially very important rules applicable to small groups, thereby meeting the needs of various groups and enabling personalized services. Through continuous feedback from user behavior data, the system gradually identifies the community information that users are interested in during the specific recommendation process. After testing, the smart community system’s recommendation accuracy and real-time performance have vastly improved in comparison to categorical recommendations, and it can effectively meet the needs of tenants for community recommendations.

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