A SURVEY OF CONTEXT-AWARE MOBILE RECOMMENDATIONS

2013 ◽  
Vol 12 (01) ◽  
pp. 139-172 ◽  
Author(s):  
QI LIU ◽  
HAIPING MA ◽  
ENHONG CHEN ◽  
HUI XIONG
Keyword(s):  
Recommender Systems ◽  
User Interfaces ◽  
State Of The Art ◽  
Contextual Information ◽  
General Notion ◽  
Context Aware ◽  
Key Issues ◽  
Privacy Problem ◽  
Mobile Context ◽  
The Right

Mobile recommender systems target on recommending the right product or information to the right mobile users at anytime and anywhere. It is well known that the contextual information is often the key for the performances of mobile recommendations. Therefore, in this paper, we provide a focused survey of the recent development of context-aware mobile recommendations. After briefly reviewing the state-of-the-art of recommender systems, we first discuss the general notion of mobile context and how the contextual information is collected. Then, we introduce the existing approaches to exploit contextual information for modeling mobile recommendations. Furthermore, we summarize several existing recommendation tasks in the mobile scenarios, such as the recommendations in the tourism domain. Finally, we discuss some key issues that are still critical in the field of context-aware mobile recommendations, including the privacy problem, the energy efficiency issues, and the design of user interfaces.

Review Aware Recommender System

2018 ◽  
Vol 10 (2) ◽  
pp. 28-50
Author(s):  
Fatima Zahra Lahlou ◽  
Houda Benbrahim ◽  
Ismail Kassou
Keyword(s):  
Recommender Systems ◽  
Recommender System ◽  
State Of The Art ◽  
Contextual Information ◽  
Empirical Evaluation ◽  
Feature Engineering ◽  
Context Aware ◽  
Recommendation Accuracy

Context aware recommender systems (CARS) are recommender systems (RS) that provide recommendations according to user contexts. The first challenge for building such a system is to get the contextual information. Some works tried to get this information from reviews provided by users in addition to their ratings. However, all of these works perform important feature engineering in order to infer the context. In this article, the authors present a new CARS architecture that allows to automatically use contextual information from reviews without requiring any feature engineering. Moreover, they develop a new CARS algorithm that is tailored to textual contexts, that they call Textual Context Aware Factorization Machines (TCAFM). An empirical evaluation shows that the proposed architecture allows to significantly improve recommendation accuracy using state of the art RS and CARS algorithms, whereas TCAFM leads to additional improvements.

scholarly journals Context-aware recommender systems and cultural heritage: a survey

2021 ◽  
Author(s):  
Mario Casillo ◽  
Francesco Colace ◽  
Dajana Conte ◽  
Marco Lombardi ◽  
Domenico Santaniello ◽  
...  
Keyword(s):  
Cultural Heritage ◽  
Recommender Systems ◽  
Context Awareness ◽  
Technological Development ◽  
Context Aware ◽  
Human Beings ◽  
Context Analysis ◽  
Analysis Techniques ◽  
The Right ◽  
Technological Improvements

AbstractIn the Big Data era, every sector has adapted to technological development to service the vast amount of information available. In this way, each field has benefited from technological improvements over the years. The cultural and artistic field was no exception, and several studies contributed to the aim of the interaction between human beings and artistic-cultural heritage. In this scenario, systems able to analyze the current situation and recommend the right services play a crucial role. In particular, in the Recommender Systems field, Context-Awareness helps to improve the recommendations provided. This article aims to present a general overview of the introduction of Context analysis techniques in Recommender Systems and discuss some challenging applications to the Cultural Heritage field.

The Context-Security Nexus in Ubiquitous Computing

2016 ◽  
pp. 660-679
Author(s):  
M. Fahim Ferdous Khan ◽  
Ken Sakamura
Keyword(s):  
Access Control ◽  
Ubiquitous Computing ◽  
Context Awareness ◽  
Contextual Information ◽  
Healthcare Services ◽  
Context Aware ◽  
Context Sensitive ◽  
Access Control Models ◽  
Spatio Temporal ◽  
The Right

Context-awareness is a quintessential feature of ubiquitous computing. Contextual information not only facilitates improved applications, but can also become significant security parameters – which in turn can potentially ensure service delivery not to anyone anytime anywhere, but to the right person at the right time and place. Specially, in determining access control to resources, contextual information can play an important role. Access control models, as studied in traditional computing security, however, have no notion of context-awareness; and the recent works in the nascent field of context-aware access control predominantly focus on spatio-temporal contexts, disregarding a host of other pertinent contexts. In this paper, with a view to exploring the relationship of access control and context-awareness in ubiquitous computing, the authors propose a comprehensive context-aware access control model for ubiquitous healthcare services. They explain the design, implementation and evaluation of the proposed model in detail. They chose healthcare as a representative application domain because healthcare systems pose an array of non-trivial context-sensitive access control requirements, many of which are directly or indirectly applicable to other context-aware ubiquitous computing applications.

scholarly journals Mobile Context-Aware Systems: Technologies, Resources and Applications

2016 ◽  
Vol 10 (2) ◽  
pp. 25 ◽  
Author(s):  
Alejandro Rivero-Rodriguez ◽  
Paolo Pileggi ◽  
Ossi Antero Nykänen
Keyword(s):  
Personal Information ◽  
Contextual Information ◽  
General Information ◽  
Context Aware ◽  
User Privacy ◽  
Contextual Data ◽  
Potential Applications ◽  
Mobile Context ◽  
Available Information ◽  
Further Development

Mobile applications often adapt their behavior according to user context, however, they are often limited to consider few sources of contextual information, such as user position or language. This article reviews existing work in context-aware systems (CAS), e.g., how to model context, and discusses further development of CAS and its potential applications by looking at available information, methods and technologies. Social Media seems to be an interesting source of personal information when appropriately exploited. In addition, there are many types of general information, ranging from weather and public transport to information of books and museums. These information sources can be combined in previously unexplored ways, enabling the development of smarter mobile services in different domains. Users are, however, reluctant to provide their personal information to applications; therefore, there is a crave for new regulations and systems that allow applications to use such contextual data without compromising the user privacy.

scholarly journals BEMD-3DCNN-Based Method for COVID-19 Detection

2021 ◽  
Author(s):  
Ali Riahi ◽  
Omar Elharrouss ◽  
Noor Almaadeed ◽  
Somaya Al-Maadeed
Keyword(s):  
Detection System ◽  
Imaging Techniques ◽  
Contextual Information ◽  
Context Aware ◽  
Feature Maps ◽  
Virus Identification ◽  
Mode Decomposition ◽  
The Right ◽  
Fully Connected ◽  
Deep Learning Model

Abstract Coronavirus outbreak continues to spread around the world and none knows when it will stop. Therefore, from the first day of the virus identification in Wuhan, scientists have launched numerous research projects to understand the nature of the virus, how to detect it, and search for the right medicine to help and protect patients. A fast diagnostic and detection system is a priority and should be found to stop COVID-19 from spreading. Medical imaging techniques has been used for this purpose. The existing works used transfer learning by exploiting different backbones like VGG, ResNet, DenseNet or combine them to detect COVID-19. By using these backbones many aspect can not be analysed like the spatial and contextual information in the images, while these information's can be useful for a better detection performance. For that in this paper, we used 3D representation of the data (video) as input of the 3DCNN-based deep learning model. The Bi-dimensional empirical mode decomposition (BEMD) technique to decompose the original image into IMFs, then built a video of these IMFs images. The formed video is used as input of 3DCNN model to classify and detect COVID-19 virus. 3DCNN model consists of a 3D VGG-16 backbone followed by a Context-aware attention (CAA) modules then fully connected layers for classification. Each CAA module takes the feature maps of different blocks of the backbone, which allows a learning from different feature maps. In the experiments we used 6484 X-Ray images, 1802 of them were COVID-19 positive cases, 1910 normal cases, and 2772 pneumonia cases. The experiment results showed that our proposed techniques achieved the desired results on the selected dataset. Also, the use of 3DCNN model with contextual information processing exploiting CAA networks helps to achieve better performance.

scholarly journals Context-Aware Image Inpainting with Learned Semantic Priors

2021 ◽  
Author(s):  
Wendong Zhang ◽  
Junwei Zhu ◽  
Ying Tai ◽  
Yunbo Wang ◽  
Wenqing Chu ◽  
...  
Keyword(s):  
State Of The Art ◽  
Contextual Information ◽  
Image Inpainting ◽  
Context Aware ◽  
Global Context ◽  
Low Level ◽  
Complex Scenes ◽  
Knowledge Distillation ◽  
Image Generator ◽  
High Level

Recent advances in image inpainting have shown impressive results for generating plausible visual details on rather simple backgrounds. However, for complex scenes, it is still challenging to restore reasonable contents as the contextual information within the missing regions tends to be ambiguous. To tackle this problem, we introduce pretext tasks that are semantically meaningful to estimating the missing contents. In particular, we perform knowledge distillation on pretext models and adapt the features to image inpainting. The learned semantic priors ought to be partially invariant between the high-level pretext task and low-level image inpainting, which not only help to understand the global context but also provide structural guidance for the restoration of local textures. Based on the semantic priors, we further propose a context-aware image inpainting model, which adaptively integrates global semantics and local features in a unified image generator. The semantic learner and the image generator are trained in an end-to-end manner. We name the model SPL to highlight its ability to learn and leverage semantic priors. It achieves the state of the art on Places2, CelebA, and Paris StreetView datasets

Transforming Geo-Referenced Data in Contextual Information for Context-Aware Recommender Systems

2018 ◽  
Author(s):  
Igor Andre Santana ◽  
Abner Suniga ◽  
Juliano Donini ◽  
Camila Vaccari Sundermann ◽  
Solange Oliveira Rezende ◽  
...  
Keyword(s):  
Recommender Systems ◽  
Contextual Information ◽  
Context Aware

A Hybrid Filtering Approach for an Improved Context-aware Recommender System

2019 ◽  
Vol 13 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Mugdha Sharma ◽  
Laxmi Ahuja ◽  
Vinay Kumar
Keyword(s):  
Recommender Systems ◽  
Performance Improvement ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Context Aware ◽  
Post Evaluation ◽  
Contextual Modeling ◽  
Filter Approach ◽  
Single Approach ◽  
Post Filter

Background: The domain of context-aware recommender approaches has made a substantial advancement over the last decade, but many applications still do not include contextual information while providing recommendations. Contextual information is crucial for various application areas and should not be ignored. Objective: There are generally three algorithms which can be used to include context and those are - pre-filter approach, post-filter approach and contextual modeling. Each of the algorithms has their own drawbacks if any single approach is chosen. The goal of this work is to identify and propose a new hybrid approach which can include contextual information to improve the current movie recommender systems. Method: Post evaluation of various patents related to recommender systems, the proposed approach modifies the post filter approach to rectify its shortcomings and combines it with the pre-filter approach based on the importance of contextual attribute provided by the user. Results: The performance of the proposed system is measured in terms of precision of the system and ranking of the recommended movies to the user. The results of experimental setup also demonstrate that the proposed system improves the precision and ranking of the recommendations provided to the user. Conclusion: With the help of this hybrid approach, the proposed system eliminates the problem of sparsity which is present in the pre-filter algorithm, and has performance improvement over the traditional post-filter approach. The proposed system will be vital for movie ticketing brands for the promotional purposes and various online content providers to recommend the accurate movies to their users.

ConChi

2016 ◽  
pp. 798-820
Author(s):  
Luca Cagliero
Keyword(s):  
Contextual Information ◽  
Extraction Process ◽  
Mobile User ◽  
Mobile Service ◽  
Context Aware ◽  
Time Periods ◽  
Context Aware System ◽  
Mobile Context ◽  
High Level ◽  
Abstraction Levels

Mobile context-aware systems focus on adapting mobile service provisions to the actual user needs. They offer personalized services based on the context in which mobile users' requests have been submitted. Since contextual information changes over time, the application of established itemset change mining algorithms to context-aware data is an appealing research issue. Change itemset discovery focuses on discovering patterns which represent the temporal evolution of frequent itemsets in consecutive time periods. However, the sparseness of the analyzed data may bias the extraction process, because itemsets are likely to become infrequent at certain time periods. This chapter presents ConChI, a novel context-aware system that performs change itemset mining from context-aware data with the aim at supporting mobile expert decisions. To counteract data sparseness itemset change mining is driven by an analyst-provided taxonomy which allows analyzing data correlation changes at different abstraction levels. In particular, taxonomy is exploited to represent the knowledge that becomes infrequent in certain time periods by means of high level (generalized) itemsets. Experiments performed on real contextual data coming from a mobile application show the effectiveness of the proposed system in supporting mobile user and service profiling.

ConChi

2014 ◽  
pp. 263-285
Author(s):  
Luca Cagliero
Keyword(s):  
Contextual Information ◽  
Extraction Process ◽  
Mobile User ◽  
Mobile Service ◽  
Context Aware ◽  
Time Periods ◽  
Context Aware System ◽  
Mobile Context ◽  
High Level ◽  
Abstraction Levels

Mobile context-aware systems focus on adapting mobile service provisions to the actual user needs. They offer personalized services based on the context in which mobile users’ requests have been submitted. Since contextual information changes over time, the application of established itemset change mining algorithms to context-aware data is an appealing research issue. Change itemset discovery focuses on discovering patterns which represent the temporal evolution of frequent itemsets in consecutive time periods. However, the sparseness of the analyzed data may bias the extraction process, because itemsets are likely to become infrequent at certain time periods. This chapter presents ConChI, a novel context-aware system that performs change itemset mining from context-aware data with the aim at supporting mobile expert decisions. To counteract data sparseness itemset change mining is driven by an analyst-provided taxonomy which allows analyzing data correlation changes at different abstraction levels. In particular, taxonomy is exploited to represent the knowledge that becomes infrequent in certain time periods by means of high level (generalized) itemsets. Experiments performed on real contextual data coming from a mobile application show the effectiveness of the proposed system in supporting mobile user and service profiling.

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