Exploring the Influence of Contexts for Mobile Recommendation

2017 ◽  
Vol 14 (4) ◽  
pp. 33-49 ◽  
Author(s):  
Jun Zeng ◽  
Feng Li ◽  
Yinghua Li ◽  
Junhao Wen ◽  
Yingbo Wu
Keyword(s):  
State Of The Art ◽  
Rapid Development ◽  
Great Influence ◽  
Mobile Internet ◽  
Personalized Recommendation ◽  
Context Aware ◽  
Mobile Environment ◽  
High Quality ◽  
Binary Information ◽  
Definition Of

With the rapid development of mobile internet, it is difficult to obtain high-quality recommendation in such a complicated mobile environment, just depending on traditional user-item binary information. How to use multiple contexts to generate satisfying recommendation has been a hot topic in some fields like e-commerce, tourism and news. Context aware recommender system (CARS) imports contexts into recommender to generate ubiquitous and personalized recommendation. In this paper, the basic information of CARS, such as the definition of context, the process of CARS and evaluation are introduced carefully. In order to explore whether contexts have a great influence on recommendation or not, the authors conduct experiments on real datasets. Experimental results show recommender that incorporates contexts significantly improves performance over the traditional recommender. Finally, State of the art about CARS is detailed.

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 A Comprehensive Survey of Personalized Music Identifier System

2021 ◽  
Vol 9 (6) ◽  
pp. 90-96
Author(s):  
Soumi Ghosh ◽  
Devanshu Tyagi ◽  
Daksh Vashisht ◽  
Abhishek Yadav ◽  
Dharmendra Rajput
Keyword(s):  
Rapid Development ◽  
Research Work ◽  
Personalized Recommendation ◽  
Systematic Research ◽  
Mobile Environment ◽  
Common People ◽  
Evaluation Of Performance ◽  
Effective System ◽  
Comprehensive Survey ◽  
Streaming Services

Music occupies a very important space in the heart and life of common people and it is rather subjective and universal nature indeed. Music Identifier System is obviously concerned with providing a very meaningful and personalized recommendation of items i.e. songs, music, playlist according to the mood, emotion, interest and preference of the users or listeners. With the advancement of technologies, rapid development of internet, it has become very common to use the streaming services to listen and enjoy music or songs in more convenient ways. In this paper, an attempt has been made to perform a comparative analysis, systematic research, empirical thorough review on various approaches or strategies proposed and applied by different researchers in the task of designing an effective system for music identification or recommendation. The basic theme of the paper includes music identifier system, its components, and different features along with emphasize on the methods, metrics, general framework and state-of-art strategies proposed during the last two decades or so, have been empirically reviewed. The existing studies were found lacking with systematic research work on the behaviour, requirements and preferences of the users plus poor level of extraction of features and limitations in the area of evaluation of performance of the music identifier systems. Although, the study reveals that systems based on effective, social information, emotional-traits, content, context and knowledge have been widely applied and improved the quality of identification or recommendation of music to a large extend but still it is not enough. In future, more in-depth studies or research work need to be conducted based on enlarging the scope of further development of personalized contextual awareness based music identifier system and generating a continuous and automatic top playlist of music and songs with added tracks matching with profile, mood, emotional traits, and behaviour of the user in a mobile environment.

scholarly journals A Collaborative Filtering Recommendation Algorithm Based on User Confidence and Time Context

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Guangxia Xu ◽  
Zhijing Tang ◽  
Chuang Ma ◽  
Yanbing Liu ◽  
Mahmoud Daneshmand
Keyword(s):  
Collaborative Filtering ◽  
Traditional Method ◽  
Recommendation System ◽  
Rapid Development ◽  
Poor Performance ◽  
Mobile Internet ◽  
Personalized Recommendation ◽  
Recommendation Algorithm ◽  
Rating Data ◽  
Similarity Calculation

Complex and diverse information is flooding entire networks because of the rapid development of mobile Internet and information technology. Under this condition, it is difficult for a person to locate and access useful information for making decisions. Therefore, the personalized recommendation system which utilizes the user’s behaviour information to recommend interesting items emerged. Currently, collaborative filtering has been successfully utilized in personalized recommendation systems. However, under the condition of extremely sparse rating data, the traditional method of similarity between users is relatively simple. Moreover, it does not consider that the user’s interest will change over time, which results in poor performance. In this paper, a new similarity measure method which considers user confidence and time context is proposed to preferably improve the similarity calculation between users. Finally, the experimental results demonstrate that the proposed algorithm is suitable for the sparse data and effectively improves the prediction accuracy and enhances the recommendation quality at the same time.

Context-Aware P2P Over Opportunistic Networks

2011 ◽  
pp. 460-480 ◽  
Author(s):  
Marco Conti ◽  
Franca Delmastro ◽  
Andrea Passarella
Keyword(s):  
Dynamic Environment ◽  
Opportunistic Networks ◽  
Context Aware ◽  
Mobile Environment ◽  
P2p Systems ◽  
Intermittent Connectivity ◽  
Computing Paradigm ◽  
Stable Path ◽  
Hoc Networks ◽  
Definition Of

Recently, the popularity of p2p computing paradigm has been increasing, especially in the mobile environments, due to the large use of mobile devices as tools to generate and share content among users. Several works have been proposed in ad hoc networks literature to optimize legacy p2p systems over a mobile environment, mainly relying on the necessity of a stable path between pairs of nodes wishing to communicate. However, in the last few years, resources limitations and high mobility of users have introduced a new networking paradigm characterized by intermittent connectivity and frequent partitioning: the opportunistic networks. In such a dynamic environment, where systems must exploit all communication opportunities to enable the users to get in touch and exchange data, the authors propose a novel definition of mobile p2p, which exploits context information to enhance distributed services. In addition, they present a Context-aware opportunistic File Sharing application as a practical example of an optimized p2p service over opportunistic networks.

Research on the Factors Influencing the Reading Motivation of Social Media Users from the Perspective of Reading Promotion in China

Libri ◽  
2020 ◽  
Vol 70 (4) ◽  
pp. 279-290
Author(s):  
Han Wang ◽  
Qing Fang ◽  
Ye Chen ◽  
Lingshuang Guan ◽  
Ting Dong
Keyword(s):  
Social Media ◽  
Information Acquisition ◽  
Structural Equation ◽  
Rapid Development ◽  
Reading Motivation ◽  
Mobile Internet ◽  
Equation Modeling ◽  
High Quality ◽  
The Impact ◽  
Social Reading

AbstractWith the rapid development of information technology and mobile Internet access, social media content has become extremely abundant and open, and users have become heavily dependent on social reading. As a result, users’ reading motivation has greatly changed from traditional reading to digital reading to social reading. Exploring the potential effects of social reading can contribute to providing strategies to accurately target high-quality reading content to help promote reading with social media. Drawing upon the use and gratifications theory and reading motivation scales, the current study examines the effectiveness of reading motivation to explore in depth the influencing mechanism of users’ reading on social media. Structural equation modeling is employed to empirically test the impact factor model. The results indicate that social media users’ reading motivation mainly includes entertainment, self-presentation, information acquisition, social promotion, and social interaction. Regarding the overall contribution, social motivation is the most important factor in social media reading activities, followed by intrinsic reading motivation and information reading motivation. The findings and their implications are discussed to provide suggestions for social media operators promoting high-quality reading.

scholarly journals Mapping the Sharing Economy in China

2020 ◽  
Vol 12 (16) ◽  
pp. 6333
Author(s):  
Chan Liu ◽  
Raymond K. H. Chan ◽  
Maofu Wang ◽  
Zhe Yang
Keyword(s):  
Global Economy ◽  
Rapid Development ◽  
Sharing Economy ◽  
Essential Elements ◽  
Mobile Internet ◽  
Internet Technology ◽  
Short Term ◽  
Bona Fide ◽  
The Government ◽  
Idle Capacity

Harnessing the rapid development of mobile internet technology, the sharing economy has experienced unprecedented growth in the global economy, especially in China. Likely due to its increasing popularity, more and more businesses have adopted this label in China. There is a concern as to the essential meaning of the sharing economy. As it is difficult to have a universally accepted definition, we aim to map the sharing economy and demystify the use of it in China in this paper. We propose seven organizing essential elements of the sharing economy: access use rights instead of ownership, idle capacity, short term, peer-to-peer, Internet platforms mediated, for monetary profit, and shared value orientation. By satisfying all or only parts of these elements, we propose one typology of sharing economy, and to differentiate bona fide sharing economy from quasi- and pseudo-sharing economy. Finally, there are still many problems that need to be solved urgently in the real sharing economy from the perspective of the government, companies and individuals.

UBAR

2021 ◽  
Vol 20 (3) ◽  
pp. 1-25
Author(s):  
Elham Shamsa ◽  
Alma Pröbstl ◽  
Nima TaheriNejad ◽  
Anil Kanduri ◽  
Samarjit Chakraborty ◽  
...  
Keyword(s):  
Resource Management ◽  
Quality Of Experience ◽  
State Of The Art ◽  
State Of Charge ◽  
User Preference ◽  
Management Approach ◽  
High Quality ◽  
Trade Off ◽  
Run Time

Smartphone users require high Battery Cycle Life (BCL) and high Quality of Experience (QoE) during their usage. These two objectives can be conflicting based on the user preference at run-time. Finding the best trade-off between QoE and BCL requires an intelligent resource management approach that considers and learns user preference at run-time. Current approaches focus on one of these two objectives and neglect the other, limiting their efficiency in meeting users’ needs. In this article, we present UBAR, User- and Battery-aware Resource management, which considers dynamic workload, user preference, and user plug-in/out pattern at run-time to provide a suitable trade-off between BCL and QoE. UBAR personalizes this trade-off by learning the user’s habits and using that to satisfy QoE, while considering battery temperature and State of Charge (SOC) pattern to maximize BCL. The evaluation results show that UBAR achieves 10% to 40% improvement compared to the existing state-of-the-art approaches.

scholarly journals Global optimization of an encapsulated Si/SiO$$_2$$ L3 cavity with a 43 million quality factor

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
J. P. Vasco ◽  
V. Savona
Keyword(s):  
Global Optimization ◽  
Photonic Crystal ◽  
Quality Factor ◽  
State Of The Art ◽  
Optimization Strategy ◽  
High Quality ◽  
Optimal Value ◽  
Out Of Plane ◽  
Fabrication Tolerances ◽  
Structural Imperfections

AbstractWe optimize a silica-encapsulated silicon L3 photonic crystal cavity for ultra-high quality factor by means of a global optimization strategy, where the closest holes surrounding the cavity are varied to minimize out-of-plane losses. We find an optimal value of $$Q_c=4.33\times 10^7$$ Q c = 4.33 × 10 7 , which is predicted to be in the 2 million regime in presence of structural imperfections compatible with state-of-the-art silicon fabrication tolerances.

Enhanced context-aware recommendation using topic modeling and particle swarm optimization

2021 ◽  
pp. 1-16
Author(s):  
Ibtissem Gasmi ◽  
Mohamed Walid Azizi ◽  
Hassina Seridi-Bouchelaghem ◽  
Nabiha Azizi ◽  
Samir Brahim Belhaouari
Keyword(s):  
Topic Modeling ◽  
Latent Dirichlet Allocation ◽  
State Of The Art ◽  
Weighting Function ◽  
Contextual Factors ◽  
Pearson Correlation ◽  
Correlation Coefficients ◽  
Pso Algorithm ◽  
Context Aware ◽  
Proposed Model

Context-Aware Recommender System (CARS) suggests more relevant services by adapting them to the user’s specific context situation. Nevertheless, the use of many contextual factors can increase data sparsity while few context parameters fail to introduce the contextual effects in recommendations. Moreover, several CARSs are based on similarity algorithms, such as cosine and Pearson correlation coefficients. These methods are not very effective in the sparse datasets. This paper presents a context-aware model to integrate contextual factors into prediction process when there are insufficient co-rated items. The proposed algorithm uses Latent Dirichlet Allocation (LDA) to learn the latent interests of users from the textual descriptions of items. Then, it integrates both the explicit contextual factors and their degree of importance in the prediction process by introducing a weighting function. Indeed, the PSO algorithm is employed to learn and optimize weights of these features. The results on the Movielens 1 M dataset show that the proposed model can achieve an F-measure of 45.51% with precision as 68.64%. Furthermore, the enhancement in MAE and RMSE can respectively reach 41.63% and 39.69% compared with the state-of-the-art techniques.

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