Literatura científica selecionada sobre o tema "Système de Recommendation"
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Artigos de revistas sobre o assunto "Système de Recommendation"
Gupta, Aayush, Akshat Singh Gour, Akshat Singh Rathore e Akshay Keswani. "Book Recommendation System". International Journal of Research Publication and Reviews 5, n.º 5 (2 de maio de 2024): 1519–22. http://dx.doi.org/10.55248/gengpi.5.0524.1128.
Texto completo da fonteSharma, Abhishek, Alokit Sharma, Ankita Arya e Asit Joshi. "Crop Recommendation System". International Journal of Research Publication and Reviews 5, n.º 5 (2 de maio de 2024): 1095–98. http://dx.doi.org/10.55248/gengpi.5.0524.1124.
Texto completo da fontePrakash Gupta, Nipun, e Durgesh Kumar. "Music Recommendation System". International Journal of Science and Research (IJSR) 10, n.º 5 (27 de maio de 2021): 1118–23. https://doi.org/10.21275/sr21524230547.
Texto completo da fonteLiu, Duen-Ren, Kuan-Yu Chen, Yun-Cheng Chou e Jia-Huei Lee. "Online recommendations based on dynamic adjustment of recommendation lists". Knowledge-Based Systems 161 (dezembro de 2018): 375–89. http://dx.doi.org/10.1016/j.knosys.2018.07.038.
Texto completo da fonteD., Dr Vanathi. "Review of Recommendation System Methodologies". International Journal of Psychosocial Rehabilitation 23, n.º 1 (29 de março de 2019): 524–31. http://dx.doi.org/10.37200/ijpr/v23i1/pr190495.
Texto completo da fonteRashmi, A., Y. Ramachandra e Dr U. P. Kulkarni. "Preference Based Book Recommendation System". Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (31 de outubro de 2016): 183–85. http://dx.doi.org/10.9756/bijsesc.8272.
Texto completo da fonteSahare, Yash, Krunal Kamble, Tushar Bhakte, Rohit Warkade, Amrapali Besekar, Sumedh Patil e Dr Harish Gorewar. "Emotion Based Music Recommendation System". International Journal of Research Publication and Reviews 4, n.º 12 (2 de dezembro de 2023): 818–34. http://dx.doi.org/10.55248/gengpi.4.1223.123327.
Texto completo da fontePhan, Lan Phuong, Hung Huu Huynh e Hiep Xuan Huynh. "Implicative Rating-Based Hybrid Recommendation Systems". International Journal of Machine Learning and Computing 8, n.º 3 (junho de 2018): 223–28. http://dx.doi.org/10.18178/ijmlc.2018.8.3.691.
Texto completo da fonteVerma, Rakesh, Prince Verma e Abhishek Bhardwaja. "Hotel Recommendation System Using Machine Learning". International Journal of Science and Research (IJSR) 11, n.º 12 (5 de dezembro de 2022): 550–54. http://dx.doi.org/10.21275/sr221117143622.
Texto completo da fonteDomlur Seetharama, Yogananda. "Automated Item Recommendation Systems for Retail Stores". International Journal of Science and Research (IJSR) 11, n.º 1 (5 de janeiro de 2022): 1653–63. http://dx.doi.org/10.21275/sr24809044235.
Texto completo da fonteTeses / dissertações sobre o assunto "Système de Recommendation"
Silveira, Netto Nunes Maria Augusta. "Système de Recommendation basé sur Traits de Personnalité". Phd thesis, Université Montpellier II - Sciences et Techniques du Languedoc, 2008. http://tel.archives-ouvertes.fr/tel-00348370.
Texto completo da fonteAligon, Julien. "Similarity-based recommendation of OLAP sessions". Thesis, Tours, 2013. http://www.theses.fr/2013TOUR4022/document.
Texto completo da fonteOLAP (On-Line Analytical Processing) is the main paradigm for accessing multidimensional data in data warehouses. To obtain high querying expressiveness despite a small query formulation effort, OLAP provides a set of operations (such as drill-down and slice-and-dice) that transform one multidimensional query into another, so that OLAP queries are normally formulated in the form of sequences called OLAP sessions. During an OLAP session the user analyzes the results of a query and, depending on the specific data she sees, applies one operation to determine a new query that will give her a better understanding of information. The resulting sequences of queries are strongly related to the issuing user, to the analyzed phenomenon, and to the current data. While it is universally recognized that OLAP tools have a key role in supporting flexible and effective exploration of multidimensional cubes in data warehouses, it is also commonly agreed that the huge number of possible aggregations and selections that can be operated on data may make the user experience disorientating
Lonjarret, Corentin. "Sequential recommendation and explanations". Thesis, Lyon, 2021. http://theses.insa-lyon.fr/publication/2021LYSEI003/these.pdf.
Texto completo da fonteRecommender systems have received a lot of attention over the past decades with the proposal of many models that take advantage of the most advanced models of Deep Learning and Machine Learning. With the automation of the collect of user actions such as purchasing of items, watching movies, clicking on hyperlinks, the data available for recommender systems is becoming more and more abundant. These data, called implicit feedback, keeps the sequential order of actions. It is in this context that sequence-aware recommender systems have emerged. Their goal is to combine user preference (long-term users' profiles) and sequential dynamics (short-term tendencies) in order to recommend next actions to a user. In this thesis, we investigate sequential recommendation that aims to predict the user's next item/action from implicit feedback. Our main contribution is REBUS, a new metric embedding model, where only items are projected to integrate and unify user preferences and sequential dynamics. To capture sequential dynamics, REBUS uses frequent sequences in order to provide personalized order Markov chains. We have carried out extensive experiments and demonstrate that our method outperforms state-of-the-art models, especially on sparse datasets. Moreover we share our experience on the implementation and the integration of REBUS in myCADservices, a collaborative platform of the French company Visiativ. We also propose methods to explain the recommendations provided by recommender systems in the research line of explainable AI that has received a lot of attention recently. Despite the ubiquity of recommender systems only few researchers have attempted to explain the recommendations according to user input. However, being able to explain a recommendation would help increase the confidence that a user can have in a recommendation system. Hence, we propose a method based on subgroup discovery that provides interpretable explanations of a recommendation for models that use implicit feedback
Nurbakova, Diana. "Recommendation of activity sequences during distributed events". Thesis, Lyon, 2018. http://www.theses.fr/2018LYSEI115/document.
Texto completo da fonteMulti-day events such as conventions, festivals, cruise trips, to which we refer to as distributed events, have become very popular in recent years, attracting hundreds or thousands of participants. Their programs are usually very dense, making it challenging for the attendees to make a decision which events to join. Recommender systems appear as a common solution in such an environment. While many existing solutions deal with personalised recommendation of single items, recent research focuses on the recommendation of consecutive items that exploits user's behavioural patterns and relations between entities, and handles geographical and temporal constraints. In this thesis, we first formulate the problem of recommendation of activity sequences, classify and discuss the types of influence that have an impact on the estimation of the user's interest in items. Second, we propose an approach (ANASTASIA) to solve this problem, which aims at providing an integrated support for users to create a personalised itinerary of activities. ANASTASIA brings together three components, namely: (1) estimation of the user’s interest in single items, (2) use of sequential influence on activity performance, and (3) building of an itinerary that takes into account spatio-temporal constraints. Thus, the proposed solution makes use of the methods based on sequence learning and discrete optimisation. Moreover, stating the lack of publicly available datasets that could be used for the evaluation of event and itinerary recommendation algorithms, we have created two datasets, namely: (1) event attendance on board of a cruise (Fantasy_db) based on a conducted user study, and (2) event attendance at a major comic book convention (DEvIR). This allows to perform evaluation of recommendation methods, and contributes to the reproducibility of results
Zhang, Zhao. "Learning Path Recommendation : A Sequential Decision Process". Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0108.
Texto completo da fonteOver the past couple of decades, there has been an increasing adoption of Internet technology in the e-learning domain, associated with the availability of an increasing number of educational resources. Effective systems are thus needed to help learners to find useful and adequate resources, among which recommender systems play an important role. In particular, learning path recommender systems, that recommend sequences of educational resources, are highly valuable to improve learners' learning experiences. Under this context, this PhD Thesis focuses on the field of learning path recommender systems and the associated offline evaluation of these systems. This PhD Thesis views the learning path recommendation task as a sequential decision problem and considers the partially observable Markov decision process (POMDP) as an adequate approach. In the field of education, the learners' memory strength is a very important factor and several models of learners' memory strength have been proposed in the literature and used to promote review in recommendations. However, little work has been conducted for POMDP-based recommendations, and the models proposed are complex and data-intensive. This PhD Thesis proposes POMDP-based recommendation models that manage learners' memory strength, while limiting the increase in complexity and data required. Under the premise that recommending learners useful and effective learning paths is becoming more and more popular, the evaluation of the effectiveness these recommended learning paths is still a challenging task, that is not often addressed in the literature. Online evaluation is highly popular but it relies on the path recommendations to actual learners, which may have dramatic implications if the recommendations are not accurate. Offline evaluation relies on static datasets of learners' learning activities and simulates learning paths recommendations. Although easier to run, it is difficult to accurately evaluate the effectiveness of a learning path recommendation. This tends to justify the lack of literature on this topic. To tackle this issue, this PhD Thesis also proposes offline evaluation measures, that are designed to be simple to be used in most of the application cases. The recommendation models and evaluation measures the we propose are evaluated on two real learning datasets. The experiments confirm that the recommendation models proposed outperform the models from the literature, with a limited increase in complexity, including for a medium-size dataset
Omidvar, Tehrani Behrooz. "Optimization-based User Group Management : Discovery, Analysis, Recommendation". Thesis, Université Grenoble Alpes (ComUE), 2015. http://www.theses.fr/2015GREAM038/document.
Texto completo da fonteUser data is becoming increasingly available in multiple domains ranging from phone usage traces to data on the social Web. User data is a special type of data that is described by user demographics (e.g., age, gender, occupation, etc.) and user activities (e.g., rating, voting, watching a movie, etc.) The analysis of user data is appealing to scientists who work on population studies, online marketing, recommendations, and large-scale data analytics. However, analysis tools for user data is still lacking.In this thesis, we believe there exists a unique opportunity to analyze user data in the form of user groups. This is in contrast with individual user analysis and also statistical analysis on the whole population. A group is defined as set of users whose members have either common demographics or common activities. Group-level analysis reduces the amount of sparsity and noise in data and leads to new insights. In this thesis, we propose a user group management framework consisting of following components: user group discovery, analysis and recommendation.The very first step in our framework is group discovery, i.e., given raw user data, obtain user groups by optimizing one or more quality dimensions. The second component (i.e., analysis) is necessary to tackle the problem of information overload: the output of a user group discovery step often contains millions of user groups. It is a tedious task for an analyst to skim over all produced groups. Thus we need analysis tools to provide valuable insights in this huge space of user groups. The final question in the framework is how to use the found groups. In this thesis, we investigate one of these applications, i.e., user group recommendation, by considering affinities between group members.All our contributions of the proposed framework are evaluated using an extensive set of experiments both for quality and performance
Chi, Cheng. "Personalized pattern recommendation system of men’s shirts based on precise body measurement". Electronic Thesis or Diss., Centrale Lille Institut, 2022. http://www.theses.fr/2022CLIL0003.
Texto completo da fonteCommercial garment recommendation systems have been widely used in the apparel industry. However, existing research on digital garment design has focused on the technical development of the virtual design process, with little knowledge of traditional designers. The fit of a garment plays a significant role in whether a customer purchases that garment. In order to develop a well-fitting garment, designers and pattern makers should adjust the garment pattern several times until the customer is satisfied. Currently, there are three main disadvantages of traditional pattern-making: 1) it is very time-consuming and inefficient, 2) it relies too much on experienced designers, 3) the relationship between the human body shape and the garment is not fully explored. In practice, the designer plays a key role in a successful design process. There is a need to integrate the designer's knowledge and experience into current garment CAD systems to provide a feasible human-centered, low-cost design solution quickly for each personalized requirement. Also, data-based services such as recommendation systems, body shape classification, 3D body modelling, and garment fit assessment should be integrated into the apparel CAD system to improve the efficiency of the design process.Based on the above issues, in this thesis, a fit-oriented garment pattern intelligent recommendation system is proposed for supporting the design of personalized garment products. The system works in combination with a newly developed design process, i.e. body shape identification - design solution recommendation - 3D virtual presentation and evaluation - design parameter adjustment. This process can be repeated until the user is satisfied. The proposed recommendation system has been validated by some successful practical design cases
Leblay, Joffrey. "Vers une nouvelle forme d'accompagnement des processus dans les systèmes interactifs : apport de la fouille de processus et de la recommandation". Thesis, La Rochelle, 2019. http://www.theses.fr/2019LAROS021.
Texto completo da fonteAn information system is a socio-technical system comprising Business Processes and related data. With the development and democratization of IT tools, stored information is getting more important and distributed. The same is true for processes that are becoming increasingly complex and sensitive for organizations. In order to obtain a service, we had to compose business processes to collect information, transform it and reinject it. The objective of this thesis is to explore the problematic of process control in order to give options for the fabrication of a companion that would guide the user when discovering a process. We focused on computer science aspects. In particular, we are studying the possibility of defining a recommendation methodology based on extracted processes and implementing the corresponding software architecture. The works presented are at the interface between several domains. We chose a research approach based on an iterative cycle. After analyzing the field of process mining and recommendation, we concluded that we needed to strengthen our approach to information gathering. This led us to carry out studies on trace-based systems. We then sought to validate the continuity of our approach on a simple case study. It is about personalizing the course of a student during his training. We have set up a demonstrator which, based on the collection of information from previous promotions, extracts knowledge about the students' courses and makes recommendations on the consequences of the course for a particular student. This study allowed us to set up our end-to-end recommendation process and to propose a first sketch of our architecture. We then looked for a more ambitious case study for which no business process was predefined by an expert. We wanted to see if it is possible to identify behaviors and / or strategies of users using a system. We have placed ourselves in a learning context where the learner is involved in a simulation of a micro-world. This case study allowed us to show how to adapt our methodology and how to take contextual data into account. This case study gave rise to an experiment where two groups used our simulator. The first without recommendation, which allowed us to build a set of execution traces that were used to extract the necessary knowledge on our business processes. The second group benefited from our recommendation system. We observed that in the latter group the performance criterion was improved because the trial / error phenomena are considerably reduced. The experience gained during this thesis pushes us to direct our work toward helping to personalize learning trajectory. In particular, with the definition of a class, taking into account the learner's profile, both in terms of knowledge acquired and learning strategies, leads to the creation of a learning path, and therefore a selection of training blocks, which must be personalized. The methodology we have proposed is a brick to build such an ecosystem
Nzekon, Nzeko'o Armel Jacques. "Système de recommandation avec dynamique temporelle basée sur les flots de liens". Electronic Thesis or Diss., Sorbonne université, 2019. http://www.theses.fr/2019SORUS454.
Texto completo da fonteRecommending appropriate items to users is crucial in many e-commerce platforms that propose a large number of items to users. Recommender systems are one favorite solution for this task. Most research in this area is based on explicit ratings that users give to items, while most of the time, ratings are not available in sufficient quantities. In these situations, it is important that recommender systems use implicit data which are link stream connecting users to items while maintaining timestamps i.e. users browsing, purchases and streaming history. We exploit this type of implicit data in this thesis. One common approach consists in selecting the N most relevant items to each user, for a given N, which is called top-N recommendation. To do so, recommender systems rely on various kinds of information, like content-based features of items, past interest of users for items and trust between users. However, they often use only one or two such pieces of information simultaneously, which can limit their performance because user's interest for an item can depend on more than two types of side information. To address this limitation, we make three contributions in the field of graph-based recommender systems. The first one is an extension of the Session-based Temporal Graph (STG) introduced by Xiang et al., which is a dynamic graph combining long-term and short-term preferences in order to better capture user preferences over time. STG ignores content-based features of items, and make no difference between the weight of newer edges and older edges. The new proposed graph Time-weight Content-based STG addresses STG limitations by adding a new node type for content-based features of items, and a penalization of older edges. The second contribution is the Link Stream Graph (LSG) for temporal recommendations. This graph is inspired by a formal representation of link stream, and has the particularity to consider time in a continuous way unlike others state-of-the-art graphs, which ignore the temporal dimension like the classical bipartite graph (BIP), or consider time discontinuously like STG where time is divided into slices. The third contribution in this thesis is GraFC2T2, a general graph-based framework for top-N recommendation. This framework integrates basic recommender graphs, and enriches them with content-based features of items, users' preferences temporal dynamics, and trust relationships between them. Implementations of these three contributions on CiteUlike, Delicious, Last.fm, Ponpare, Epinions and Ciao datasets confirm their relevance
Rajaonarivo, Hiary Landy. "Approche co-évolutive humain-système pour l'exploration de bases de données". Thesis, Brest, 2018. http://www.theses.fr/2018BRES0114/document.
Texto completo da fonteThis thesis focus on a proposition that helps humans during the exploration of database. The particularity of this proposition relies on a co-evolution principle between the user and an intelligent interface. It provides a support to the understanding of the domain represented by the data. A metaphor of living virtual museum is adopted. This museum evolves incrementally according to the user's interactions. It incarnates both the data and the semantic information which are expressed by a knowledge model specific to the domain of the data. Through the topological organization and the incremental evolution, the museum personalizes online the user's exploration. The approach is insured by three main mechanisms: the evaluation of the user profile modelled by a dynamical weighting of the semantic information, the use of this dynamic profile to establish a recommendation as well as the incarnation of the data in the living museum. The approach has been applied to the heritage domain as part of the ANTIMOINE project, funded by the National Research Agency (ANR). The genericity of the latter has been demonstrated through its application to a database of publications but also using various types of interfaces (website, virtual reality).Experiments have validated the hypothesis that our system adapts itself to the user behavior and that it is able, in turn, to influence him.They also showed the comparison between a 2D interface and a 3D interface in terms of quality of perception, guidance, preference and efficiency
Livros sobre o assunto "Système de Recommendation"
International Telegraph and Telephone Consultative Committee. Plenary Assembly. Red book.: Recommendation Z.200. Geneva: International Telecommunication Union, 1985.
Encontre o texto completo da fonteVenugopal, K. R., K. C. Srikantaiah e Sejal Santosh Nimbhorkar. Web Recommendations Systems. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2513-1.
Texto completo da fonteInternational Telegraph and Telephone Consultative Committee. Plenary Assembly. Red book.: Recommendations of the R series : telegraph services, terminal equipment : recommendations of the S series. Geneva: International Telecommunication Union, 1985.
Encontre o texto completo da fonteInternational Telegraph and Telephone Consultative Committee. Plenary Assembly. Red book.: Recommendations X.40-X.181. Geneva: International Telecommunication Union, 1985.
Encontre o texto completo da fonteInternational Telegraph and Telephone Consultative Committee. Plenary Assembly. Red book.: Recommendations Q.601-Q.685. Geneva: International Telecommunication Union, 1985.
Encontre o texto completo da fonteInternational Telegraph and Telephone Consultative Committee. Plenary Assembly. Red book.: Recommendations of the series 1. Geneva: International Telecommunication Union, 1985.
Encontre o texto completo da fonteInternational Telegraph and Telephone Consultative Committee. Plenary Assembly. Red book.: Recommendations of the P series. Geneva: International Telecommunication Union, 1985.
Encontre o texto completo da fonteInternational Telegraph and Telephone Consultative Committee. Plenary Assembly. Red book.: Recommendations Q.721-Q.795. Geneva: International Telecommunication Union, 1985.
Encontre o texto completo da fonteInternational Telegraph and Telephone Consultative Committee. Plenary Assembly. Red book.: Recommendations Z.301-Z.341. Geneva: International Telecommunication Union, 1985.
Encontre o texto completo da fonteInternational Telegraph and Telephone Consultative Committee. Plenary Assembly. Red book.: Recommendations Q.251-Q.300. Geneva: International Telecommunication Union, 1985.
Encontre o texto completo da fonteCapítulos de livros sobre o assunto "Système de Recommendation"
Barga, Roger, Valentine Fontama e Wee Hyong Tok. "Recommendation Systems". In Predictive Analytics with Microsoft Azure Machine Learning, 243–62. Berkeley, CA: Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-1200-4_12.
Texto completo da fonteSrinivasa, K. G., Siddesh G. M. e Srinidhi H. "Recommendation Systems". In Computer Communications and Networks, 303–18. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77800-6_15.
Texto completo da fonteJoshi, Ameet V. "Recommendation Systems". In Machine Learning and Artificial Intelligence, 251–60. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12282-8_21.
Texto completo da fonteLee, Joonseok. "Recommendation Systems". In Big Data and Computational Intelligence in Networking, 227–64. Boca Raton, FL : CRC Press, [2018]: CRC Press, 2017. http://dx.doi.org/10.1201/9781315155678-14.
Texto completo da fonteShikhman, Vladimir, e David Müller. "Recommendation Systems". In Mathematical Foundations of Big Data Analytics, 41–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-662-62521-7_3.
Texto completo da fonteKubrak, Kateryna, Lana Botchorishvili, Fredrik Milani, Alexander Nolte e Marlon Dumas. "Explanatory Capabilities of Large Language Models in Prescriptive Process Monitoring". In Lecture Notes in Computer Science, 403–20. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70396-6_23.
Texto completo da fonteJoshi, Ameet V. "Recommendations Systems". In Machine Learning and Artificial Intelligence, 199–204. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26622-6_21.
Texto completo da fonteVenugopal, K. R., K. C. Srikantaiah e Sejal Santosh Nimbhorkar. "Introduction". In Web Recommendations Systems, 1–9. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2513-1_1.
Texto completo da fonteVenugopal, K. R., e K. C. Srikantaiah. "Web Data Extraction and Integration System for Search Engine Results". In Web Recommendations Systems, 11–25. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2513-1_2.
Texto completo da fonteVenugopal, K. R., e K. C. Srikantaiah. "Mining and Cyclic Behaviour Analysis of Web Sequential Patterns". In Web Recommendations Systems, 27–48. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2513-1_3.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Système de Recommendation"
A, Mahalakshmi, Dharani P, DivyaPriya S e Gayathri M. "News Recommendation Systems". In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS), 1744–47. IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10716952.
Texto completo da fonteD, Shyam Prakash, Jyothir B. A, Raja Santhosh M, Prithivi Raj P e Mithun Raj M. "Music Recommendation System". In 2024 10th International Conference on Advanced Computing and Communication Systems (ICACCS), 2618–23. IEEE, 2024. http://dx.doi.org/10.1109/icaccs60874.2024.10716833.
Texto completo da fontePawar, Sahil, Ajinkya Pawar, Parth Pawar e Jayashri Bagade. "Car Recommendation System". In 2024 International Conference on Intelligent Systems and Advanced Applications (ICISAA), 1–6. IEEE, 2024. https://doi.org/10.1109/icisaa62385.2024.10828698.
Texto completo da fonteKolobov, Oleg S., Anna A. Knyazeva, Yulia V. Leonova e Igor Yu Turchanovsky. "Personalizing digital services as exemplified by library recommendation service". In Twenty Fifth International Conference and Exhibition «LIBCOM-2021». Russian National Public Library for Science and Technology, 2022. http://dx.doi.org/10.33186/978-5-85638-247-0-2022-35-40.
Texto completo da fonteObeidat, Raghad, Rehab Duwairi e Ahmad Al-Aiad. "A Collaborative Recommendation System for Online Courses Recommendations". In 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML). IEEE, 2019. http://dx.doi.org/10.1109/deep-ml.2019.00018.
Texto completo da fontePetruzzelli, Alessandro. "Towards Symbiotic Recommendations: Leveraging LLMs for Conversational Recommendation Systems". In RecSys '24: 18th ACM Conference on Recommender Systems, 1361–67. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3640457.3688023.
Texto completo da fonteTschinkel, Gerwald, Cecilia Di Sciascio, Belgin Mutlu e Vedran Sabol. "The Recommendation Dashboard: A System to Visualise and Organise Recommendations". In 2015 19th International Conference on Information Visualisation (iV). IEEE, 2015. http://dx.doi.org/10.1109/iv.2015.51.
Texto completo da fonteYacouba, Kyelem, Kabore Kiswendsida Kisito, Ouedraogo Tounwendyam Frédéric e Sèdes Florence. "Comparative Study of Justification Methods in Recommender Systems: Example of Information Access Assistance Service (IAAS)". In 7th International Conference on Natural Language Computing (NATL 2021). Academy and Industry Research Collaboration Center (AIRCC), 2021. http://dx.doi.org/10.5121/csit.2021.112013.
Texto completo da fonteZhu, Feng, Yan Wang, Chaochao Chen, Guanfeng Liu, Mehmet Orgun e Jia Wu. "A Deep Framework for Cross-Domain and Cross-System Recommendations". In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/516.
Texto completo da fonteLebib, Fatma-Zohra, Hakima Mellah e Linda Mohand-Oussaid. "Ontological Interaction Modeling and Semantic Rule-based Reasoning for User Interface Adaptation". In Special Session on Social Recommendation in Information Systems. SCITEPRESS - Science and and Technology Publications, 2016. http://dx.doi.org/10.5220/0005854303470354.
Texto completo da fonteRelatórios de organizações sobre o assunto "Système de Recommendation"
Michelitsch, Roland, Odette Maciel, Claudia Figueroa, Regina Legarreta, Melanie Putic e Alejandro Ahumada. Management’s Implementation of OVE Recommendations: IDB Group’s Evaluation Recommendation Tracking System. Inter-American Development Bank, julho de 2019. http://dx.doi.org/10.18235/0002182.
Texto completo da fonteIsaacs, Hedy. Short Form for the Institutional Assessment of Civil Service Systems: Case of Barbados. Inter-American Development Bank, novembro de 2004. http://dx.doi.org/10.18235/0011458.
Texto completo da fonteDziurłaj, John. Recommendations for Voting System Interoperability. Gaithersburg, MD: National Institute of Standards and Technology, 2022. http://dx.doi.org/10.6028/nist.gcr.22-034.
Texto completo da fonteCooper, David A., Daniel C. Apon, Quynh H. Dang, Michael S. Davidson, Morris J. Dworkin e Carl A. Miller. Recommendation for Stateful Hash-Based Signature Schemes. National Institute of Standards and Technology, outubro de 2020. http://dx.doi.org/10.6028/nist.sp.800-208.
Texto completo da fonteOsambela Zavala, Emilio, e Martin Naranjos Lander. Jamaica Financial System: Diagnostic and Recommendations. Inter-American Development Bank, janeiro de 2003. http://dx.doi.org/10.18235/0008527.
Texto completo da fonteWei, Wenbin, Nigel Blampied e Raajmaathangi Sreevijay. Evaluation, Comparison, and Improvement Recommendations for Caltrans Financial Programming Processes and Tools. Mineta Transportation Institute, fevereiro de 2023. http://dx.doi.org/10.31979/mti.2023.2058.
Texto completo da fonteAuthor, Not Given. Total System Performance Assessment for the Site Recommendation. Office of Scientific and Technical Information (OSTI), outubro de 2000. http://dx.doi.org/10.2172/765259.
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