scholarly journals CFDIL: A Context-aware Feature Deep Interaction Learning for  App Recommendation

2021 ◽  
Author(s):  
Qingbo Hao ◽  
Ke Zhu ◽  
Chundong Wang ◽  
Peng Wang ◽  
Xiuliang Mo ◽  
...  
Keyword(s):  
Mobile Applications ◽  
Historical Data ◽  
Contextual Information ◽  
Rapid Development ◽  
User Preferences ◽  
Mobile Internet ◽  
Context Aware ◽  
Interaction Data ◽  
Learning Potential ◽  
The One

Abstract The rapid development of Mobile Internet has spa-wned various mobile applications (apps). A large number of apps make it difficult for users to choose apps conveniently, causing the app overload problem. As the most effective tool to solve the problem of app overload, the app recommendation has attracted extensive attention of researchers. Traditional recommendation methods usually use historical data of apps used by users to explore their preferences, and then make an app recommendation list for users. Although the traditional app recommendation methods have achieved certain results, the performance of app recommendation still needs to be improved due to the following two reasons. On the one hand, it is difficult to construct traditional app recommendation models when facing with the sparse user-app interaction data. On the other hand, contextual information has a large impact on users’ app usage preferences, which is often overlooked by traditional app recommendation methods. To overcome the aforementioned problems, we proposed a Context-aware Feature Deep Interaction Learning (CFDIL) method to explore user preferences, and then perform app recommendation by learning potential user-app relationships in different contexts. The novelty of CFDIL is as follows: (1) CFDIL incorporates contextual features into users' preferences modeling by constructing a novel user and app feature portrait. (2) The problem of data sparsity is effectively solved by the use of dense user and app feature portraits, as well as the tensor operations for label sets. (3) CFDIL trains a new deep network structure, which can make accurate app recommendation using the contextual information and attribute information of users and apps. We applied CFDIL on three real datasets and conducted extensive experiments, which showed that CFDIL outperformed the benchmark method.

scholarly journals Investigating the Temporal Effect of User Preferences with Application in Movie Recommendation

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Wen-Jun Li ◽  
Qiang Dong ◽  
Yan Fu
Keyword(s):  
Rapid Development ◽  
User Preferences ◽  
Mobile Internet ◽  
Smart Devices ◽  
Data Sets ◽  
Rating Data ◽  
Temporal Effect ◽  
Proposed Model ◽  
Target User ◽  
The Difference

As the rapid development of mobile Internet and smart devices, more and more online content providers begin to collect the preferences of their customers through various apps on mobile devices. These preferences could be largely reflected by the ratings on the online items with explicit scores. Both of positive and negative ratings are helpful for recommender systems to provide relevant items to a target user. Based on the empirical analysis of three real-world movie-rating data sets, we observe that users’ rating criterions change over time, and past positive and negative ratings have different influences on users’ future preferences. Given this, we propose a recommendation model on a session-based temporal graph, considering the difference of long- and short-term preferences, and the different temporal effect of positive and negative ratings. The extensive experiment results validate the significant accuracy improvement of our proposed model compared with the state-of-the-art methods.

scholarly journals Incorporating Contextual Information into Personalized Mobile Applications Recommendation

2021 ◽  
Author(s):  
Ke Zhu ◽  
Yingyuan Xiao ◽  
Wenguang Zheng ◽  
Xu Jiao ◽  
Chenchen Sun ◽  
...  
Keyword(s):  
Mobile Applications ◽  
Large Scale ◽  
Contextual Information ◽  
Mobile App ◽  
Mobile Internet ◽  
Current Time ◽  
Spatiotemporal Information ◽  
Data Overload ◽  
Effect Experiment ◽  
Contextual Characteristic

Abstract With the rise of the mobile internet, the number of mobile applications (apps) has shown explosive growth, which directly leads to the apps data overload. Currently, the recommender system has become the most effective method to solve the app data overload. App has the functional exclusiveness feature, which means the target users will not reuse apps with the same function in a certain spatiotemporal information. Most of the existing recommended methods for apps ignore the functional exclusiveness feature which makes it difficult to further improve the recommendation performance of the app recommendation. To solve this problem, we aim to improve the app recommendation performance, and propose a Personalized Context-aware Mobile App Recommendation Approach, called PCMARA. PCMARA comprehensively considers the user and app contextual information, which can mine the users app usage preference effectively. Specifically, (1) PCMARA explores the contextual characteristic of app, and constructs the app contextual factors for app which represent the function of app. (2) For the app functional exclusiveness problem, PCMARA leverages the app contextual factor to design a novel app similarity model, which enable to effectively eliminate this problem. (3) PCMARA considers the contextual information of users and apps to generates a recommendation list for target users based on the target users' current time and location. We applied the PCMARA to a real-world dataset and conducted a large-scale recommendation effect experiment. The experimental results show that the recommendation effect of PCMARA is satisfactory.

scholarly journals Design and Development of a Context-aware Personalized Recommendation System: Mobile and Web Application

2017 ◽  
Author(s):  
Sara Saeedi ◽  
Xueyang Zou ◽  
Mariel Gonzales ◽  
Steve Liang
Keyword(s):  
Web Application ◽  
Management Practices ◽  
Recommendation System ◽  
Contextual Information ◽  
User Preferences ◽  
Personalized Recommendation ◽  
Context Information ◽  
Context Aware ◽  
Context Reasoning ◽  
Past Behavior

The ubiquity of mobile sensors (such as GPS, accelerometer and gyroscope) together with increasing computational power have enabled an easier access to contextual information, which proved its value in next generation of the recommender applications. The importance of contextual information has been recognized by researchers in many disciplines, such as ubiquitous and mobile computing, to filter the query results and provide recommendations based on different user status. A context-aware recommendation system (CoARS) provides a personalized service to each individual user, driven by his or her particular needs and interests at any location and anytime. Therefore, a contextual recommendation system changes in real time as a user’s circumstances changes. CoARS is one of the major applications that has been refined over the years due to the evolving geospatial techniques and big data management practices. In this paper, a CoARS is designed and implemented to combine the context information from smartphones’ sensors and user preferences to improve efficiency and usability of the recommendation. The proposed approach combines user’s context information (such as location, time, and transportation mode), personalized preferences (using individuals past behavior), and item-based recommendations (such as item’s ranking and type) to personally filter the item list. The context-aware methodology is based on preprocessing and filtering of raw data, context extraction and context reasoning. This study examined the application of such a system in recommending a suitable restaurant using both web-based and android platforms. The implemented system uses CoARS techniques to provide beneficial and accurate recommendations to the users. The capabilities of the system is evaluated successfully with recommendation experiment and usability test.

scholarly journals Paas EUD Tool for Developing Expert Context-Aware Mobile Applications

2021 ◽  
Vol 10 (2) ◽  
pp. 215-224
Author(s):  
Sahar Elshafei ◽  
◽  
Ehab Hassanein ◽  
Hanan Elazhary ◽  
◽  
...  
Keyword(s):  
Mobile Applications ◽  
Expert Knowledge ◽  
Contextual Information ◽  
Quality Data ◽  
Software Systems ◽  
Context Aware ◽  
Software Developers ◽  
Platform As A Service ◽  
Context Data ◽  
Current Context

Context-awareness enables systems to be tailored to the needs of users and their real circumstances at certain times. A noteworthy trend in software development is that an increasing number of software systems are being developed by individuals with expert knowledge in other sectors. Because most of the current context-aware development toolkits are intended for software developers, these types of systems cannot be easily developed by non-technical consumers. The development of tools for designing context-aware frameworks by consumers who are not programming experts but are specialists in the area of implementation would result in faster adoption of such services by businesses. This paper provides a cloud-based framework for people without programming experience to create context-aware mobile applications. The platform can provide a lightweight distribution of packaged applications that allows experts to send specified information to mobile users based on their context data without overlapping between the rules of the application. An energy-efficient mobile application was developed to acquire contextual information from the user device and to create quality data accordingly. The framework adopts Platform as a Service (PaaS) and containerization to facilitate development of context-aware mobile applications by experts in various domains rather than developing a tool for each domain in isolation, while considering multitenancy.

Infrastructures for Development of Context-Aware Mobile Applications

2011 ◽  
pp. 1104-1118
Author(s):  
Hugo Feitosa de Figueirêdo ◽  
Tiago Eduardo da Silva ◽  
Anselmo Cardoso de Paiva ◽  
José Eustáquio Rangel de Queiroz ◽  
Cláudio De Souza Baptista
Keyword(s):  
Mobile Devices ◽  
Mobile Applications ◽  
Contextual Information ◽  
Wireless Networking ◽  
Context Aware ◽  
Context Model ◽  
Technological Advances ◽  
Communication Method ◽  
Context Aware System

Context-aware mobile applications are becoming popular, as a consequence of the technological advances in mobile devices, sensors and wireless networking. Nevertheless, developing a context-aware system involves several challenges. For example, what will be the contextual information, how to represent, acquire and process this information and how it will be used by the system. Some frameworks and middleware have been proposed in the literature to help programmers to overcome these challenges. Most of the proposed solutions, however, neither have an extensible ontology-based context model nor uses a communication method that allows a better use of the potentialities of the models of this kind.

scholarly journals Deep Learning-Based Context-Aware Recommender System Considering Contextual Features

2021 ◽  
Vol 12 (1) ◽  
pp. 45
Author(s):  
Soo-Yeon Jeong ◽  
Young-Kuk Kim
Keyword(s):  
Deep Learning ◽  
Recommender System ◽  
Contextual Information ◽  
User Preferences ◽  
Context Aware ◽  
Data Sparsity ◽  
Contextual Features ◽  
Proposed Model ◽  
Multidimensional Matrix ◽  
Sparsity Problem

A context-aware recommender system can make recommendations to users by considering contextual information such as time and place, not only the scores assigned to items by users. However, as a user preferences matrix is expanded in a multidimensional matrix, data sparsity is maximized. In this paper, we propose a deep learning-based context-aware recommender system that considers the contextual features. Based on existing deep learning models, we combine a neural network and autoencoder to extract characteristics and predict scores in the process of restoring input data. The newly proposed model is able to easily reflect various type of contextual information and predicts user preferences by considering the feature of user, item and context. The experimental results confirm that the proposed method is mostly superior to the existing method in all datasets. Also, for the dataset with data sparsity problem, it was confirmed that the performance of the proposed method is higher than that of existing methods. The proposed method has higher precision by 0.01–0.05 than other recommender systems in a dataset with many context dimensions. And it showed good performance with a high precision of 0.03 to 0.09 in a small dimensional dataset.

scholarly journals CONTEXT-AWARE INTERACTION AND NAVIGATION IN MOBILE GAMES

2006 ◽  
Vol 35 (3) ◽  
Author(s):  
Silke Jahn ◽  
Aizhen Liu ◽  
Mihail Dimitrov ◽  
Michail Mazo ◽  
Friedrich Jürgen ◽  
...  
Keyword(s):  
Human Computer Interaction ◽  
Mobile Phones ◽  
Mobile Applications ◽  
Contextual Information ◽  
Context Aware ◽  
Next Generation ◽  
Mobile Games ◽  
Mobile Game ◽  
Usability Problems ◽  
Computer Interaction

In this paper an approach of using contextual information for structuring and displaying menus on small devices will be discussed, based on the implementation of a game for mobile games. CitizenMOB is a location-based, multiplayer, never-ending society-driven strategic mobile game that has been developed in order to understand today's possibilities and challenges in the design of complex games for mobile phones. Integrating a context-aware navigation and adaptive menu structure is an attempt not only to reflect the effect of new contexts of use on human-computer-interaction, it is also meant to overcome usability problems that occur when limitations of small screens are combined with complex rules and massive options in the next generation of rich mobile applications.

scholarly journals Enhancing Existing Communication Services with Context Awareness

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Bachir Chihani ◽  
Emmanuel Bertin ◽  
Irsalina Salsabila Suprapto ◽  
Julien Zimmermann ◽  
Noël Crespi
Keyword(s):  
User Experience ◽  
Context Awareness ◽  
Communication System ◽  
Information Sources ◽  
Contextual Information ◽  
User Preferences ◽  
Context Aware ◽  
Communication Management ◽  
Communication Services ◽  
Service Customization

Context aware communication services rely on information sources and sensors, to derive users’ current situation and potential needs, and to adapt their communication services accordingly. If extensive studies have been driven on context awareness by industrials and researchers from academia, the design of such systems without modifying uses and manners of underlying communication services—while keeping them simple, intuitive, and reactive—remains a challenge. In this work, we introduce a context aware communication system that takes into account user’s preferences, workload, and situation to customize telephony services. In this implementation, we use IMS for communication management. The benefits of this implementation are the enhancement of IMS with context awareness features, and the coupling of user preferences with contextual information to provide improved service customization, without modifying the user experience.

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