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Academic literature on the topic 'CONTEXT-AWARE RECOMMENDER'

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Published: 11 September 2023

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Journal articles on the topic "CONTEXT-AWARE RECOMMENDER"

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Adomavicius, Gediminas, Bamshad Mobasher, Francesco Ricci, and Alexander Tuzhilin. "Context-Aware Recommender Systems." AI Magazine 32, no. 3 (October 31, 2011): 67. http://dx.doi.org/10.1609/aimag.v32i3.2364.

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Context-aware recommender systems (CARS) generate more relevant recommendations by adapting them to the specific contextual situation of the user. This article explores how contextual information can be used to create more intelligent and useful recommender systems. It provides an overview of the multifaceted notion of context, discusses several approaches for incorporating contextual information in recommendation process, and illustrates the usage of such approaches in several application areas where different types of contexts are exploited. The article concludes by discussing the challenges and future research directions for context-aware recommender systems.
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WANG, Li-Cai, Xiang-Wu MENG, and Yu-Jie ZHANG. "Context-Aware Recommender Systems." Journal of Software 23, no. 1 (March 5, 2012): 1–20. http://dx.doi.org/10.3724/sp.j.1001.2012.04100.

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Pisotskyi, Marian, and Alexey Botchkaryov. "Online Video Platform with Context-aware Content-based Recommender System." Advances in Cyber-Physical Systems 6, no. 1 (January 23, 2021): 46–53. http://dx.doi.org/10.23939/acps2021.01.046.

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The problem of developing an online video platform with a context-aware content-based recommender system has been considered. Approaches to developing online video platforms have been considered. A comparison of popular online video platforms has been presented. A method of context-aware content-based recommendation of videos has been proposed. A method involves saving information about user interaction with video, obtaining and storing information about which videos the user liked, determining user context, composing a profile of user preferences, composing a profile of user preferences depending on context, determining the similarity between the video profile and a profile of user preferences (with and without context consideration), determining the relevance of the video to the context, the conclusive estimation of the relevance of the video to the user’s preferences based on the proposed composite relevance indicator. The developed structure of online video platform has been presented. The algorithm of its work has been considered. The structure of the online video platform database has been proposed. Features of designing the user interface of the online video platform have been considered. The issue of testing the developed online video platform has been considered.
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Qassimi, Sara, El Hassan Abdelwahed, and Meriem Hafidi. "Folksonomy Graphs Based Context-Aware Recommender System Using Spectral Clustering." International Journal of Machine Learning and Computing 10, no. 1 (January 2020): 63–68. http://dx.doi.org/10.18178/ijmlc.2020.10.1.899.

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Kavu, Tatenda D., Kudakwashe Dube, and Peter G. Raeth. "Holistic User Context-Aware Recommender Algorithm." Mathematical Problems in Engineering 2019 (September 29, 2019): 1–15. http://dx.doi.org/10.1155/2019/3965845.

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Existing recommender algorithms lack dynamism, human focus, and serendipitous recommendations. The literature indicates that the context of a user influences user decisions, and when incorporated in recommender systems (RSs), novel and serendipitous recommendations can be realized. This article shows that social, cultural, psychological, and economic contexts of a user influence user traits or decisions. The article demonstrates a novel approach of incorporating holistic user context-aware knowledge in an algorithm to solve the highlighted problems. Web content mining and collaborative filtering approaches were used to develop a holistic user context-aware (HUC) algorithm. The algorithm was evaluated on a social network using online experimental evaluations. The algorithm demonstrated dynamism, novelty, and serendipity with an average of 84% novelty and 85% serendipity.
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Ali, Waqar, Jay Kumar, Cobbinah Bernard Mawuli, Lei She, and Jie Shao. "Dynamic context management in context-aware recommender systems." Computers and Electrical Engineering 107 (April 2023): 108622. http://dx.doi.org/10.1016/j.compeleceng.2023.108622.

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Iqbal, Misbah, Mustansar Ali Ghazanfar, Asma Sattar, Muazzam Maqsood, Salabat Khan, Irfan Mehmood, and Sung Wook Baik. "Kernel Context Recommender System (KCR): A Scalable Context-Aware Recommender System Algorithm." IEEE Access 7 (2019): 24719–37. http://dx.doi.org/10.1109/access.2019.2897003.

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Naha, Sanchita, and Sudeep Marwaha. "Context-Aware Recommender System for Maize Cultivation." Journal of Community Mobilization and Sustainable Development 15, no. 2 (2020): 485–90. http://dx.doi.org/10.5958/2231-6736.2020.00034.

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Kumar, Rajeev, B. K. Verma, and Shyam Sunder Rastogi. "Context-aware Social Popularity based Recommender System." International Journal of Computer Applications 92, no. 2 (April 18, 2014): 37–42. http://dx.doi.org/10.5120/15985-4907.

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Woerndl, Wolfgang, Michele Brocco, and Robert Eigner. "Context-Aware Recommender Systems in Mobile Scenarios." International Journal of Information Technology and Web Engineering 4, no. 1 (January 2009): 67–85. http://dx.doi.org/10.4018/jitwe.2009010105.

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Dissertations / Theses on the topic "CONTEXT-AWARE RECOMMENDER"

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Liu, Xiaohu. "Context-aware recommender systems for implicit data." Thesis, University of York, 2014. http://etheses.whiterose.ac.uk/13237/.

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Recommender systems are software tools and techniques providing suggestions and recommendations for items to be of use to a user. These sug- gestions can help users make better decisions on choosing products or services, such as which film to watch, what music to listen to or which travel insurance to buy. When making suggestions, many recommender systems do not consider contextual information, such as location or time [5]. Recommender systems that make use of contextual information are called context-aware recommender systems. Many context-aware recommender systems can not generate reliable rec- ommendations on sparse data. Besides, in most context-aware recommender systems, the contexts are pre-defined and not personalised. These limitations of existing methods usually lead to inaccurate recommendations. In this thesis, new context-aware recommendation methods are presented. In these methods, personalised contexts are defined based on users’ activity patterns. The underlying associations between contexts are analysed, and similar contexts are combined so that the system can make use of existing data collected in similar contexts. Experimental results from two datasets show that the proposed methods can achieve significantly higher recommendation accuracy than existing context-aware recommendation methods.
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Al-Ghossein, Marie. "Context-aware recommender systems for real-world applications." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLT008/document.

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Les systèmes de recommandation se sont révélés être des outils efficaces pour aider les utilisateurs à faire face à la surcharge informationnelle. D’importants progrès ont été réalisés dans le domaine durant les deux dernières décennies, menant en particulier à l’exploitation de l’information contextuelle pour modéliser l’aspect dynamique des utilisateurs et des articles. La définition traditionnelle du contexte, adoptée dans la plupart des systèmes de recommandation contextuels, ne répond pas à plusieurs contraintes rencontrées dans les applications du monde réel. Dans cette thèse, nous abordons les problèmes de recommandation en présence d’informations contextuelles partiellement observables et d’informations contextuelles non observables dans deux applications particulières, la recommandation d’hôtels et la recommandation en ligne, remettant en question plusieurs aspects de la définition traditionnelle du contexte, notamment l'accessibilité, la pertinence, l'acquisition et la modélisation.La première partie de la thèse étudie le problème de recommandation d’hôtels qui souffre du démarrage à froid continu, limitant la performance des approches classiques de recommandation. Le voyage n’est pas une activité fréquente et les utilisateurs ont tendance à adopter des comportements diversifiés en fonction de leurs situations spécifiques. Après une analyse du comportement des utilisateurs dans ce domaine, nous proposons de nouvelles approches de recommandation intégrant des informations contextuelles partiellement observables affectant les utilisateurs. Nous montrons comment cela contribue à améliorer la qualité des recommandations.La deuxième partie de la thèse aborde le problème de recommandation en ligne en présence de flux de données où les observations apparaissent continûment à haute fréquence. Nous considérons que les utilisateurs et les articles reposent sur des informations contextuelles non observables par le système et évoluent de façons différentes à des rythmes différents. Nous proposons alors d’effectuer de la détection active de changements et d’assurer la mise à jour des modèles en temps réel. Nous concevons de nouvelles méthodes qui s’adaptent aux changements qui apparaissent au niveau des préférences des utilisateurs et des perceptions et descriptions des articles, et montrons l’importance de la recommandation adaptative en ligne pour garantir de bonnes performances au cours du temps
Recommender systems have proven to be valuable tools to help users overcome the information overload, and significant advances have been made in the field over the last two decades. In particular, contextual information has been leveraged to model the dynamics occurring within users and items. Context is a complex notion and its traditional definition, which is adopted in most recommender systems, fails to cope with several issues occurring in real-world applications. In this thesis, we address the problems of partially observable and unobservable contexts in two particular applications, hotel recommendation and online recommendation, challenging several aspects of the traditional definition of context, including accessibility, relevance, acquisition, and modeling.The first part of the thesis investigates the problem of hotel recommendation which suffers from the continuous cold-start issue, limiting the performance of classical approaches for recommendation. Traveling is not a frequent activity and users tend to have multifaceted behaviors depending on their specific situation. Following an analysis of the user behavior in this domain, we propose novel recommendation approaches integrating partially observable context affecting users and we show how it contributes in improving the recommendation quality.The second part of the thesis addresses the problem of online adaptive recommendation in streaming environments where data is continuously generated. Users and items may depend on some unobservable context and can evolve in different ways and at different rates. We propose to perform online recommendation by actively detecting drifts and updating models accordingly in real-time. We design novel methods adapting to changes occurring in user preferences, item perceptions, and item descriptions, and show the importance of online adaptive recommendation to ensure a good performance over time
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Li, Siying. "Context-aware recommender system for system of information systems." Thesis, Compiègne, 2021. http://www.theses.fr/2021COMP2602.

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Travailler en collaboration n’est plus une question mais une réalité, la question qui se pose aujourd’hui concerne la mise en œuvre de la collaboration de façon à ce qu’elle soit la plus réussie possible. Cependant, une collaboration réussie n’est pas facile et est conditionnée par différents facteurs qui peuvent l’influencer. Il est donc nécessaire de considérer ces facteurs au sein du contexte de collaboration pour favoriser l’efficacité de collaboration. Parmi ces facteurs, le collaborateur est un facteur principal, qui est étroitement associé à l’efficacité et à la réussite des collaborations. Le choix des collaborateurs et/ou la recommandation de ces derniers en tenant compte du contexte de la collaboration peut grandement influencer la réussite de cette dernière. En même temps, grâce au développement des technologies de l’information, de nombreux outils numériques de collaboration sont mis à la disposition tels que les outils de mail et de chat en temps réel. Ces outils numériques peuvent eux-mêmes être intégrés dans un environnement de travail collaboratif basé sur le web. De tels environnements permettent aux utilisateurs de collaborer au-delà de la limite des distances géographiques. Ces derniers laissent ainsi des traces d’activités qu’il devient possible d’exploiter. Cette exploitation sera d’autant plus précise que le contexte sera décrit et donc les traces enregistrées riches en description. Il devient donc intéressant de développer les environnements de travail collaboratif basé sur le web en tenant d’une modélisation du contexte de la collaboration. L’exploitation des traces enregistrés pourra alors prendre la forme de recommandation contextuelle de collaborateurs pouvant renforcer la collaboration. Afin de générer des recommandations de collaborateurs dans des environnements de travail collaboratifs basés sur le web, cette thèse se concentre sur la génération des recommandations contextuelles de collaborateurs en définissant, modélisant et traitant le contexte de collaboration. Pour cela, nous proposons d’abord une définition du contexte de collaboration et choisissons de créer une ontologie du contexte de collaboration compte tenu des avantages de l’approche de modélisation en l’ontologie. Ensuite, une similarité sémantique basée sur l’ontologie est développée et appliquée dans trois algorithmes différents (i.e., PreF1, PoF1 et PoF2) afin de générer des recommandations contextuelles des collaborateurs. Par ailleurs, nous déployons l’ontologie de contexte de collaboration dans des environnements de travail collaboratif basés sur le web en considérant une architecture de système des systèmes d’informations du point de vue des environnements de travail collaboratif basés sur le web. À partir de cette architecture, un prototype correspondant d’environnement de travail collaboratif basé sur le web est alors construit. Enfin, un ensemble de données de collaborations scientifiques est utilisé pour tester et évaluer les performances des trois algorithmes de recommandation contextuelle des collaborateurs
Working collaboratively is no longer an issue but a reality, what matters today is how to implement collaboration so that it is as successful as possible. However, successful collaboration is not easy and is conditioned by different factors that can influence it. It is therefore necessary to take these impacting factors into account within the context of collaboration for promoting the effectiveness of collaboration. Among the impacting factors, collaborator is a main one, which is closely associated with the effectiveness and success of collaborations. The selection and/or recommendation of collaborators, taking into account the context of collaboration, can greatly influence the success of collaboration. Meanwhile, thanks to the development of information technology, many collaborative tools are available, such as e-mail and real-time chat tools. These tools can be integrated into a web-based collaborative work environment. Such environments allow users to collaborate beyond the limit of geographical distances. During collaboration, users can utilize multiple integrated tools, perform various activities, and thus leave traces of activities that can be exploited. This exploitation will be more precise when the context of collaboration is described. It is therefore worth developing web-based collaborative work environments with a model of the collaboration context. Processing the recorded traces can then lead to context-aware collaborator recommendations that can reinforce the collaboration. To generate collaborator recommendations in web-based Collaborative Working Environments, this thesis focuses on producing context-aware collaborator recommendations by defining, modeling, and processing the collaboration context. To achieve this, we first propose a definition of the collaboration context and choose to build a collaboration context ontology given the advantages of the ontology-based modeling approach. Next, an ontologybased semantic similarity is developed and applied in three different algorithms (i.e., PreF1, PoF1, and PoF2) to generate context-aware collaborator recommendations. Furthermore, we deploy the collaboration context ontology into web-based Collaborative Working Environments by considering an architecture of System of Information Systems from the viewpoint of web-based Collaborative Working Environments. Based on this architecture, a corresponding prototype of web-based Collaborative Working Environment is then constructed. Finally, a dataset of scientific collaborations is employed to test and evaluate the performances of the three context-aware collaborator recommendation algorithms
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Hoque, Tania Tanzin. "A context aware recommender system for tourism with ambient intelligence." Master's thesis, Universidade de Évora, 2020. http://hdl.handle.net/10174/27906.

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Recommender system (RS) holds a significant place in the area of the tourism sector. The major factor of trip planning is selecting relevant Points of Interest (PoI) from tourism domain. The RS system supposed to collect information from user behaviors, personality, preferences and other contextual information. This work is mainly focused on user’s personality, preferences and analyzing user psychological traits. The work is intended to improve the user profile modeling, exposing relationship between user personality and PoI categories and find the solution in constraint satisfaction programming (CSP). It is proposed the architecture according to ambient intelligence perspective to allow the best possible tourist place to the end-user. The key development of this RS is representing the model in CSP and optimizing the problem. We implemented our system in Minizinc solver with domain restrictions represented by user preferences. The CSP allowed user preferences to guide the system toward finding the optimal solutions; RESUMO O sistema de recomendação (RS) detém um lugar significativo na área do sector do turismo. O principal fator do planeamento de viagens é selecionar pontos de interesse relevantes (PoI) do domínio do turismo. O sistema de recomendação (SR) deve recolher informações de comportamentos, personalidade, preferências e outras informações contextuais do utilizador. Este trabalho centra-se principalmente na personalidade, preferências do utilizador e na análise de traços fisiológicos do utilizador. O trabalho tem como objetivo melhorar a modelação do perfil do utilizador, expondo a relação entre a personalidade deste e as categorias dos POI, assim como encontrar uma solução com programação por restrições (CSP). Propõe-se a arquitetura de acordo com a perspetiva do ambiente inteligente para conseguir o melhor lugar turístico possível para o utilizador final. A principal contribuição deste SR é representar o modelo como CSP e tratá-lo como problema de otimização. Implementámos o nosso sistema com o solucionador em Minizinc com restrições de domínio representadas pelas preferências dos utilizadores. O CSP permitiu que as preferências dos utilizadores guiassem o sistema para encontrar as soluções ideais.
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Agagu, Tosin. "Recommendation Approaches Using Context-Aware Coupled Matrix Factorization." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/37012.

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In general, recommender systems attempt to estimate user preference based on historical data. A context-aware recommender system attempts to generate better recommendations using contextual information. However, generating recommendations for specific contexts has been challenging because of the difficulties in using contextual information to enhance the capabilities of recommender systems. Several methods have been used to incorporate contextual information into traditional recommendation algorithms. These methods focus on incorporating contextual information to improve general recommendations for users rather than identifying the different context applicable to the user and providing recommendations geared towards those specific contexts. In this thesis, we explore different context-aware recommendation techniques and present our context-aware coupled matrix factorization methods that use matrix factorization for estimating user preference and features in a specific contextual condition. We develop two methods: the first method attaches user preference across multiple contextual conditions, making the assumption that user preference remains the same, but the suitability of items differs across different contextual conditions; i.e., an item might not be suitable for certain conditions. The second method assumes that item suitability remains the same across different contextual conditions but user preference changes. We perform a number of experiments on the last.fm dataset to evaluate our methods. We also compared our work to other context-aware recommendation approaches. Our results show that grouping ratings by context and jointly factorizing with common factors improves prediction accuracy.
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Strand, Anton, and Markus Gunnarsson. "Code Reviewer Recommendation : A Context-Aware Hybrid Approach." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18288.

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Background. Code reviewing is a commonly used practice in software development. It refers to the process of reviewing new code changes, commonly before they aremerged with the code base. However, in order to perform the review, developers need to be assigned to that task. The problems with a manual assignment includes a time-consuming selection process; limited pool of known candidates; risk of high reuse of the same reviewers (high workload). Objectives. This thesis aims to attempt to address the above issues with a recommendation system. The idea is to receive feedback from experienced developers in order to expand upon identified reviewer factors; which can be used to determinethe suitability of developers as reviewers for a given change. Also, to develop and implement a solution that uses some of the most promising reviewer factors. The solution can later be deployed and validated through user and reviewer feedback in a real large-scale project. The developed recommendation system is named Carrot. Methods. An improvement case study was conducted at Ericsson. The identification of reviewer factors is found through literature review and semi-structured interviews. Validation of Carrot’s usability was conducted through static analysis,user feedback, and static validation. Results. The results show that Carrot can help identify adequate non-obvious reviewers and be of great assistance to new developers. There are mixed opinions on Carrot’s ability to assist with workload balancing and decrease of review lead time. The recommendations can be performed in a production environment in less than a quarter of a second. Conclusions. The implemented and validated approach indicates possible usefulness in performing recommendations, but could benefit significantly from further improvements. Many of the problems seen with the recommendations seem to be a result of corner-cases that are not handled by the calculations. The problems would benefit considerably from further analysis and testing.
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Thollot, Raphaël. "Dynamic situation monitoring and Context-Aware BI recommendations." Phd thesis, Ecole Centrale Paris, 2012. http://tel.archives-ouvertes.fr/tel-00718917.

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The amount of information generated and maintained by information systems and their users leads to the increasingly important concern of information overload. Personalized systems have thus emerged to help provide more relevant information and services to the user. In particular, recommender systems appeared in the mid 1990's and have since then generated a growing interest in both industry and academia. Besides, context-aware systems have been developed to model, capture and interpret information about the user's situation, generally in dynamic and heterogeneous environments. Decision support systems like Business Intelligence (BI) platforms also face usability challenges as the amount of information available to knowledge workers grows. Remarkably, we observe that only a small part of personalization and recommendation techniques have been used in the context of data warehouses and analysis tools. Therefore, our work aims at exploring synergies of recommender systems and context-aware systems to develop personalization and recommendation scenarios suited in a BI environment. In response to this, we develop in our work an open and modular situation management platform using a graph-based situation model. Besides, dynamic aspects are crucial to deal with context data which is inherently time-dependent. We thus define two types of active components to enable dynamic maintenance of situation graphs, activation rules and operators. In response to events which can describe users' interactions, activation rules - defined using the event-condition-action framework - are evaluated thanks to queries on underlying graphs, to eventually trigger appropriate operators. These platform and framework allow us to develop and support various recommendation and personalization scenarios. Importantly, we design a re-usable personalized query expansion component, using semantics of multi-dimensional models and usage statistics from repositories of BI documents like reports or dashboards. This component is an important part of another experimentation we realized, Text-To-Query. This system dynamically generates multi-dimensional queries to illustrate a text and support the knowledge worker in the analysis or enrichment of documents she is manipulating. Besides, we also illustrate the integration and usage of our graph repository and situation management frameworks in an open and extensible federated search project, to provide background knowledge management and personalization.
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Alghamdi, Hamzah. "E-Tourism: Context-Aware Points of Interest Finder and Trip Designer." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/35676.

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Many countries depend heavily on tourism for their economic growth. The invention of the web has opened new opportunities for tourists to discover new places and live new adventures. However, the number of possible destinations has become huge and even an entire lifespan would not be enough to visit all of these places. Even for one city, there are a significant number of possible places to visit. Nowadays, searching online to find an interesting place to visit is harder than ever, not because there is a lack of information but rather due to the vast amount of information that can be found. Trip planning is a tedious task, especially when the tourist does not want to pick a preplanned itinerary from a traveling agency. That being said, even these preplanned itineraries need a lot of time and effort to be customized. Moreover, the set of itineraries that a tourist can select from is usually limited. In addition, there may be many places that tourists would enjoy visiting but that are not included in the itineraries. Thus, static planners do not always choose the right place at the right time. This is why the planning process should take into consideration many factors in order to give the tourist the best possible suggestions. In this Thesis, we propose an algorithm called the Balanced Orienteering Problem to design trips for tourists. This algorithm, combined with a context-aware recommender system for tourism suggestions, create the infrastructure of the mobile application for the augmented reality tourism guide that we developed. We cover the background knowledge of tour planning problems and tourism recommender systems and describe the existing techniques. Furthermore, a comparison between the existing systems and our algorithm is completed to illustrate that our proposed algorithm yields better results. We also discuss the workflow of our system implementation and how our mobile application is designed. Lastly, we address suggestions for future works and end with a conclusion.
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Sauer, Christian Severin. "Knowledge elicitation and formalisation for context and explanation-aware computing with case-based recommender systems." Thesis, University of West London, 2016. http://repository.uwl.ac.uk/id/eprint/2226/.

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Case-based reasoning (CBR), as one of the problem solving paradigms in the field of Artificial Intelligence (AI), is an approach to the re-use of experience to solve problem. The aim of this research was to identify and evaluate existing and new approaches to elicit and formalise knowledge for context-aware systems as well as systems that are able to perform explanation-aware computing. The research was centred on systems that employ the specific AI approach of CBR. The research identified positive and negative effects of knowledge formalisation as well as synergies of knowledge formalisation for context-awareness and explanation-aware computing. The research focused on a set of specific knowledge sources such as sensors, human experts, online sources such as web communities and social media as well as a combination of these sources. A set of knowledge formalisation approaches was evaluated during the implementation of six prototype systems, representing a series of product- and work-flow recommender systems. Example domains for the systems developed include CBR-based recommendation in audio mastering, gold ore refinement and travel medicine. Test data gathered from real-world use of the prototypes formed the basis for a quantitative and qualitative analysis to establish the performance and quality of the knowledge formalisation approaches used within the prototypes development. The outcome of this research work consists of new approaches to knowledge elicitation and formalisation for expert work-flow recommender systems, new approaches to context- and explanatory-knowledge formalisation in combination with software engineering techniques, new approaches to knowledge extraction and formalisation from web sources and contributions to the further development of the myCBR 3 software, an open source software for the rapid prototyping of CBR systems.
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Akermi, Imen. "A hybrid model for context-aware proactive recommendation." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30101/document.

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L'accès aux informations pertinentes, adaptées aux besoins et au profil de l'utilisateur est un enjeu majeur dans le cadre actuel caractérisé par une prolifération massive des ressources d'information hétérogènes. Le développement d'appareils mobiles équipés de plusieurs fonctionnalités telles que la caméra, le WIFI, la géo-localisation et bien plus d'autres permettent aux systèmes mobiles de recommandation actuels d'être hautement contextualisés et pouvant fournir à l'utilisateur des informations pertinentes au bon moment quand il en a le plus besoin, sans attendre qu'il établisse une interaction avec son appareil. C'est dans ce cadre que s'insère notre travail de thèse. En effet, nous proposons une approche de recommandation contextuelle et proactive dans un environnement mobile qui permet de recommander des informations pertinentes à l'utilisateur sans attendre à ce que ce dernier initie une interaction. Un système proactif peut prendre la forme d'un guide touristique personnalisé qui se base sur la localisation et les préférences de l'utilisateur pour suggérer à ce dernier des endroits intéressants sans qu'il fournisse, sa préférence ou une requête explicite. Cela permettra de réduire les efforts, le temps et l'interaction de l'utilisateur avec son appareil mobile et de présenter les informations pertinentes au bon moment et au bon endroit. Cette approche prend aussi en considération les situations où la recommandation pourrait déranger l'utilisateur. Il s'agit d'équilibrer le processus de recommandation contre les interruptions intrusives. En effet, il existe différents facteurs et situations qui rendent l'utilisateur moins ouvert aux recommandations. Comme nous travaillons dans le contexte des appareils mobiles, nous considérons que les applications mobiles telles que la caméra, le clavier, l'agenda, etc., sont de bons représentants de l'interaction de l'utilisateur avec son appareil puisqu'ils représentent en quelque sorte la plupart des activités qu'un utilisateur pourrait entreprendre avec son appareil mobile au quotidien, comme envoyer des messages, converser, tweeter, naviguer ou prendre des photos
Just-In-Time recommender systems involve all systems able to provide recommendations tailored to the preferences and needs of users in order to help them access useful and interesting resources within a large data space. The user does not need to formulate a query, this latter is implicit and corresponds to the resources that match the user's interests at the right time. Our work falls within this framework and focuses on developing a proactive context-aware recommendation approach for mobile devices that covers many domains. It aims at recommending relevant items that match users' personal interests at the right time without waiting for the users to initiate any interaction. Indeed, the development of mobile devices equipped with persistent data connections, geolocation, cameras and wireless capabilities allows current context-aware recommender systems (CARS) to be highly contextualized and proactive. We also take into consideration to which degree the recommendation might disturb the user. It is about balancing the process of recommendation against intrusive interruptions. As a matter of fact, there are different factors and situations that make the user less open to recommendations. As we are working within the context of mobile devices, we consider that mobile applications functionalities such as the camera, the keyboard, the agenda, etc., are good representatives of the user's interaction with his device since they somehow stand for most of the activities that a user could use in a mobile device in a daily basis such as texting messages, chatting, tweeting, browsing or taking selfies and pictures
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Books on the topic "CONTEXT-AWARE RECOMMENDER"

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1st, Kala K. U., and Nandhini M. 2nd. Deep Learning Model for Categorical Context Adaptation in Sequence-Aware Recommender Systems. INSC International Publisher (IIP), 2021.

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Book chapters on the topic "CONTEXT-AWARE RECOMMENDER"

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Adomavicius, Gediminas, and Alexander Tuzhilin. "Context-Aware Recommender Systems." In Recommender Systems Handbook, 217–53. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-85820-3_7.

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Adomavicius, Gediminas, and Alexander Tuzhilin. "Context-Aware Recommender Systems." In Recommender Systems Handbook, 191–226. Boston, MA: Springer US, 2015. http://dx.doi.org/10.1007/978-1-4899-7637-6_6.

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Negre, Elsa, Franck Ravat, and Olivier Teste. "OLAP Queries Context-Aware Recommender System." In Lecture Notes in Computer Science, 127–37. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98812-2_9.

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Zheng, Yong, Bamshad Mobasher, and Robin Burke. "Emotions in Context-Aware Recommender Systems." In Human–Computer Interaction Series, 311–26. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31413-6_15.

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Miao, Huiyu, Bingqing Luo, and Zhixin Sun. "An Improved Context-Aware Recommender Algorithm." In Intelligent Computing Theories and Application, 153–62. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42291-6_15.

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Zhong, Jinfeng, and Elsa Negre. "Context-Aware Explanations in Recommender Systems." In Progresses in Artificial Intelligence & Robotics: Algorithms & Applications, 76–85. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98531-8_8.

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Inzunza, Sergio, and Reyes Juárez-Ramírez. "A Comprehensive Context-Aware Recommender System Framework." In Computer Science and Engineering—Theory and Applications, 1–24. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74060-7_1.

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Herzog, Daniel, and Wolfgang Wörndl. "Mobile and Context-Aware Event Recommender Systems." In Lecture Notes in Business Information Processing, 142–63. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-66468-2_8.

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Dixit, Veer Sain, and Parul Jain. "Weighted Percentile-Based Context-Aware Recommender System." In Advances in Intelligent Systems and Computing, 377–88. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1822-1_35.

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Tang, Tiffany Y., Pinata Winoto, and Gordon McCalla. "Further Thoughts on Context-Aware Paper Recommendations for Education." In Recommender Systems for Technology Enhanced Learning, 159–73. New York, NY: Springer New York, 2014. http://dx.doi.org/10.1007/978-1-4939-0530-0_8.

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Conference papers on the topic "CONTEXT-AWARE RECOMMENDER"

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Adomavicius, Gediminas, and Alexander Tuzhilin. "Context-aware recommender systems." In the 2008 ACM conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1454008.1454068.

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Abbas, Manzar, Muhammad Usman Riaz, Asad Rauf, Muhammad Taimoor Khan, and Shehzad Khalid. "Context-aware Youtube recommender system." In 2017 International Conference on Information and Communication Technologies (ICICT). IEEE, 2017. http://dx.doi.org/10.1109/icict.2017.8320183.

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Elahi, Mehdi. "Context-aware intelligent recommender system." In the 15th international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1719970.1720045.

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Jain, Rajshree, Jaya Tyagi, Sandeep Kumar Singh, and Taj Alam. "Hybrid context aware recommender systems." In ADVANCEMENT IN MATHEMATICAL SCIENCES: Proceedings of the 2nd International Conference on Recent Advances in Mathematical Sciences and its Applications (RAMSA-2017). Author(s), 2017. http://dx.doi.org/10.1063/1.5008707.

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Unger, Moshe. "Latent Context-Aware Recommender Systems." In RecSys '15: Ninth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2792838.2796546.

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Ricci, Francesco. "Context-aware music recommender systems." In the 21st international conference companion. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2187980.2188215.

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Kahng, Minsuk, Sangkeun Lee, and Sang-goo Lee. "Ranking in context-aware recommender systems." In the 20th international conference companion. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1963192.1963226.

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Cella, Leonardo. "Efficient Context-Aware Sequential Recommender System." In Companion of the The Web Conference 2018. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3184558.3191581.

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Adomavicius, Gediminas, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci, Alexander Tuzhilin, and Moshe Unger. "Workshop on context-aware recommender systems." In RecSys '19: Thirteenth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3298689.3346954.

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Pichl, Martin, Eva Zangerle, and Günther Specht. "Improving Context-Aware Music Recommender Systems." In ICMR '17: International Conference on Multimedia Retrieval. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3078971.3078980.

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