Mobile Application Recommendation Research Articles

A DQN-based traffic classification method for mobile application recommendation with continual learning

With the popularity and development of smartphones, many mobile applications of various types have emerged. How to recommend mobile applications that match the user’s preferences and usage habits among the massive applications is a problem that needs to be solved. Traditional mobile application recommendation methods cannot dynamically track user behavior and preference changes in time and cannot timely correct the recommendation model, resulting in poor recommendation effects. The continual update of mobile applications will also invalidate the recommendation model based on traffic classification. To solve these problems, this paper proposes A Deep Q-Network (DQN) based traffic classification method for mobile application recommendation with continual learning, which embeds a DQN-based traffic classification model in the mobile terminal and sets up a reward and punishment mechanism to achieve self-supervised learning. By continuously adjusting and optimizing the model, the effectiveness of the traffic classification model is ensured, and the recommendation model is provided with accurate and reliable user behavior data support. Experiments on the ISCX and private datasets show that the proposed method performs better and can effectively guarantee the accuracy of the classification model.

TN-MR: topic-aware neural network-based mobile application recommendation

PurposeWith the increasing number of mobile applications, efficiently recommending mobile applications to users has become a challenging problem. Although existing mobile application recommendation approaches based on user attributes and behaviors have achieved notable effectiveness, they overlook the diffusion patterns and interdependencies of topic-specific mobile applications among user groups. mobile applications among user groups. This paper aims to capture the diffusion patterns and interdependencies of mobile applications among user groups. To achieve this, a topic-aware neural network-based mobile application recommendation method, referred to as TN-MR, is proposed.Design/methodology/approachIn this method, first, the user representations are enhanced by introducing a topic-aware attention layer, which captures both the topic context and the diffusion history context. Second, it exploits a time-decay mechanism to simulate changes in user interest. Multitopic user representations are aggregated by the time decay module to output the user representations of cascading representations under multiple topics. Finally, user scores that are likely to download the mobile application are predicted and ranked.FindingsExperimental comparisons and analyses were conducted on the actual 360App data set, and the results demonstrate that the effectiveness of mobile application recommendations can be significantly improved by using TN-MR.Originality/valueIn this paper, the authors propose a mobile application recommendation method based on topic-aware attention networks. By capturing the diffusion patterns and dependencies of mobile applications, it effectively assists users in selecting their applications of interest from thousands of options, significantly improving the accuracy of mobile application recommendations.

Heterogeneous Convolutional Neural Networks for Emotion Recognition Combined with Multimodal Factorised Bilinear Pooling and Mobile Application Recommendation

The field of emotion recognition has garnered considerable interest due to its diverse applications in mental health, personalised advertising and enhancing user experiences. This research paper introduces a unique and innovative method for emotion recognition by integrating heterogeneous convolutional neural networks (CNNs) with multimodal factorised bilinear pooling. Furthermore, the paper also incorporates the integration of mobile application recommendations as part of the overall approach. The proposed method leverages the power of CNNs to extract high-level features from different modalities, including facial expressions, speech signals and physiological signals. By using heterogeneous CNNs, each modality is processed independently to capture modality-specific emotional cues effectively. To fuse the extracted features, multimodal factorised bilinear pooling is employed, which captures the complex interactions between different modalities while reducing the computational complexity. This pooling technique efficiently combines the modality-specific features, resulting in a compact and discriminative representation of the emotional state. In addition to emotion recognition, this paper also introduces the integration of mobile app recommendations. By leveraging the recognised emotion, the system recommends relevant mobile applications that are tailored to the user’s emotional state. This integration enhances user experience and facilitates emotion regulation through the utilisation of appropriate mobile apps. Experimental evaluations are conducted on benchmark emotion recognition datasets, including the DEAP and MAHNOB_HCI datasets. The findings of the study highlight the effectiveness of the proposed methodology in terms of accuracy and robustness, surpassing existing approaches in the field. Additionally, the integration of the mobile app recommendation system showcases encouraging outcomes by offering personalised recommendations tailored to the user’s emotional state.

A deep multi-embedding model for mobile application recommendation

With the explosion in the number of mobile applications, it becomes difficult and time-consuming for users to find the most suitable and interesting application from the millions of applications that exist today. Therefore, mobile app recommendation becomes a top priority to help people solve this problem. In previous studies, there are two common methods to solve this problem, one is to apply matrix factorization to the rating data to predict the target user's ratings for other applications, and the other is to predict ratings based on the user's review text. In addition to combining the above two traditional methods, this study also combines deep learning methods to generate better models. For rating data, we use a deep matrix factorization method to obtain latent factor vectors of users and items from the rating matrix to improve the embedding of ratings. For review text, we improve the text representation by using a multi-embedding approach. Most importantly, our model also incorporates deep neural networks with nonlinear transformation capabilities to extract more representative abstract features. We conduct experiments on datasets collected from the real world. Extensive experimental results show that our proposed method has better performance than other existing methods.

DeepApp: characterizing dynamic user interests for mobile application recommendation

DeepApp: characterizing dynamic user interests for mobile application recommendation

Semantic similarity for mobile application recommendation under scarce user data

The More Like This recommendation approach is ubiquitous in multiple domains and consists in recommending items similar to the one currently selected by the user, being particularly relevant when user data is scarce. We studied the impact of using semantic similarity in the context of the More Like This recommendation for mobile applications, by leveraging dense representations in order to infer the similarity between applications, based on their textual fields. Our approach was validated by comparing it to the solution currently in use by Aptoide, a mobile application store, since no benchmarks are available for this specific task. To further evaluate the proposed model, we asked 1262 users to compare the results achieved by both approaches, also allowing us to build an annotated dataset of similar applications. Results show that the semantic representations are able to capture the context of the applications, with more useful recommendations being presented to users, when compared to Aptoide’s current solution. For replication and future research, all the code and data used in this study was made publicly available, including two novel datasets (installed applications for more than one million users, and app user-labeled similarity), the fine-tuned model, and the test platform.

A Multi-modal Feature Fusion-based Approach for Mobile Application Classification and Recommendation

<p>With the rapid growth of the number and type of mobile applications, it becomes challenging to accurately classify and recommend mobile applications according to users’ individual requirements. The existing mobile application classification and recommendation methods, for one thing, do not take into account the correlation between large-scale data and model. For another, they also do not fully exploit the multi-modal, fine-grained interaction features with high-order and low-order in mobile application. To tackle this problem, we propose a mobile application classification and recommendation method based on multi-modal feature fusion. The method firstly extracts the image and description features of the mobile application using an “involution residual network + pre-trained language representation” model (i.e. the TRedBert model). Afterwards, these features are fused by using the attention mechanism in the transformer model. Then, the method classifies the mobile applications based on the fused features through a softmax classifier. Finally, the method extracts the high-order and low-order embedding features of the mobile app with a bi-linear feature interaction model (FiBiNET) based on the classification results of the mobile app, by combining the Hadamard product and inner product to achieve fine-grained high-order and low-order feature interaction, to update the mobile app representation and complete the recommendation task. The multiple sets of comparison experiments are performed on Kaggle’s real dataset, i.e., 365K IOS Apps Dataset. And the experimental results demonstrated that the proposed approach outperforms other methods in terms of Macro F1, Accuracy, AUC and Logloss.</p> <p> </p>

User-centered Design Strategies to Develop Mobile Application for Selection of Digital Forensics Tool

There are several types of digital forensics tool. Therefore, to help investigators to choose suitable tools, a recommendation system is needed. This research type is development of mobile application recommendation system. The development using User Centered Design (UCD) method. The evaluation system uses the Blackbox method and the User Acceptance Test (UAT) method. From the results, it is concluded that the application is useful, easy to use, and easy to learn by users. As for usability testing, it was found that the users found it very helpful to operate, which was 28 students with a margin of error of 5%.

Rating Prediction for Mobile Applications via Collective Matrix Factorization Considering App Categories

The rapid development of Internet and mobile communication, smart phone, tablet computer and other mobile devices become widespread, they have become an indispensable part of life, changing the human lifestyle, way of working and learning style, it brings great convenience and fun to us. This paper mainly discusses the status of personalized recommendation system, mobile application and analyse the main problems of recommendation system. This research proposes a rising mobile application domain, which uses web crawling technique to get explicit feedback from a real-world mobile application market. The explicit feedback is the users’ ratings data on Apps and the corresponding category information. To establish a mobile application recommendation model based on user interest and categorization information and propose a novel collective matrix factorization algorithm. Through the crawled data, the improved algorithm, and the traditional collaborative. The traditional mobile application recommendation systems are mostly based on keyword search, popularity, download usage and categories, thus they do not make personalized recommendation to users, and invalid to find applications that users are interested in. Through the user’s historical behaviour, a personalized recommendation system can be established to recommend the applications that fit user’s interests effectively. This paper is based on the collective matrix factorization using user interest such as user’s ratings and category information, designs a personalized recommendation system for mobile applications by using the relationship between them.

A Novel Context-Aware Mobile Application Recommendation Approach Based on Users Behavior Trajectories

With the rapid development of mobile internet technology, mobile applications (apps) have been rapidly popularized. To facilitate users' choice of apps, app recommendation is becoming a research hotspot in academia and industry. Although traditional app recommendation approaches have achieved certain results, these methods only mechanically consider the user's current context information, ignoring the impact of the user's previous related context on the user's current selection of apps. We believe this has hindered the further improvement of the recommendation effect. Based on this fact, this paper proposes a novel context-aware mobile application recommendation approach based on user behavior trajectories. We named this approach CMARA, which is the initials acronym of the proposed approach. Specifically, 1) CMARA integrates the heterogeneous information of the target users such as the user's app, time, and location, into users behavior trajectories to model the users' app usage preferences; 2) CMARA constructs the context Voronoi diagram using the users' contextual point and leverages the context Voronoi diagram to build a novel user similarity model; 3) CMARA uses the target user's current contextual information to generate an app recommendation list that meets the user's preferences. Through experiments on large-scale real-world data, we verified the effectiveness of CMARA.

Serendipity adjustable application recommendation via joint disentangled recurrent variational auto-encoder

• Mobile app recommender able to adjust the level of serendipity is proposed. • It generates next app usage prediction based on the disentangled representation. • We vary the attributes of the disentanglement for serendipitous recommender. • It shows better results than other benchmark methods in the experiments. In this study, we propose an advanced mobile application recommendation method that can systematically adjust the level of serendipity. Previous studies focused on generating recommendations that are relevant to users’ interests. However, this leads to a lack of serendipity and novelty for the user and is therefore not sufficient to achieve high levels of user satisfaction. Our model is trained to predict the next application to be recommended based on a disentangled representation of the sequence in which applications were previously used. We introduce variety into our model by varying the attributes of the disentangled representation. The results of our experiments indicate that the proposed method can systematically and effectively adjust the level of serendipity by varying the values and dimensions of the attribute variables in the disentangled representation. Additionally, our method produces recommendations that are superior to those of other benchmark methods in terms of serendipity and relevance.

Functionality-based mobile application recommendation system with security and privacy awareness

Nowadays, there are a variety of mobile device applications to execute tasks, such as paying bills and ordering food. However, some apps (malicious) claim that they perform a certain task just to lure users to damage their devices and/or execute malicious activities such as leaking sensitive information. Because of that, users need a way to choose an app that is safe and meets their needs. Recommendation systems are currently being used to choose apps, but most approaches do not evaluate security and privacy, and when they do, only permissions are considered. In this context, this work presents a novel system to evaluate and suggest apps. The main contributions are the addition of a security layer, the evaluation of the metrics inside a functionality context and a mapping between permissions and API calls raising user confidence and understanding.

A topic attention mechanism and factorization machines based mobile application recommendation method

Faced with the explosive growth of mobile applications, how to recommend mobile applications accurately and efficiently for users to choose their desirable and interesting mobile applications, which has become a challenging issue nowadays. To solve this problem, we propose a topic attention mechanism and FMs based mobile application recommendation method. Firstly, it uses LSA to obtain the global topic of mobile application description text. Then, the local semantic representations of mobile application are trained by BiLSTM model. Secondly, as for the global topic information and local semantic information in the content representation of mobile application description text, attention mechanism is performed to distinguish the contribution degree of different words and gain their weight values. Thirdly, the classification and prediction of mobile application are completed by using the softmax activation function through a full connection layer. Finally, based on user’s searching requirement, it exploits factorization machines to combine the various features of the classified mobile applications to rank and recommend the user’s expected mobile application with higher predicted score. The evaluation is conducted on a real and open dataset Mobile App Store, and the experimental results indicate that the performance of the proposed approach is better than other baseline methods in terms of precision, recall, F1-score, MAE, RMSE, and AUC.

Mobile Recommendation System for Culinary Tourism Destination using KNN (K-nearest neighbor)

Currently culinary tourism is becoming very popular among the Indonesian people. People make visits to interesting culinary places. They want to visit a culinary place that is strategic, comfortable, and cheap as their culinary destination. The culinary destination in Yogyakarta that continues to grow every year makes the tourists who will visit be confused to determine the location of culinary tourism that they will choose. Travelers need references and recommendations to help them determine culinary destinations that meet their expectations. Mobile devices and application guides are able to gather information about an environment and suggest certain places such as tourism locations, based on context factors such as location, weather conditions, and the time needed to get there. The purpose of this study is to develop a recommendation system for culinary tourism destinations in Yogyakarta based on mobile using the KNN (K-nearest neighbor) algorithm. The method used in this study consists of five main steps: literature study, identification, data collection, implementation, and evaluation. The recommendation system is based on previous user ratings of food taste, environmental atmosphere, price, service and distance between users and location. The weight of each criterion is tailored to the needs of the user. The results showed that the mobile application recommendation system was able to provide recommendations to users to determine culinary tourism destinations in Yogyakarta in accordance with the parameters of user desires.

Mobile Phone User Behavior Prediction Base on Multivariable Linear Regression Model

With the development of the Internet industry and the smart phone industry, the amount of mobile application software is increasing and the variety is becoming more and more abundant. The ensuing question is how mobile users can choose the applications they are interested in among many mobile applications. Therefore, mobile application recommendation service for mobile phone users came into place. This paper takes mobile phone users as the starting point, uses multiple linear regression models, uses the commonly used least mean square to evaluate the error, and uses the batch gradient descent method to optimize the training analysis error. Through experiments on the usage data of a large number of mobile phone users, the relationship between gender, age, mobile phone model, mobile phone usage location and app category of the mobile phone user is analyzed, and the mobile phone user app category is predicted to personalized recommend apps for the mobile phone user.

A network traffic-aware mobile application recommendation system based on network traffic cost consideration

A network traffic-aware mobile application recommendation system based on network traffic cost consideration

A network traffic-aware mobile application recommendation system based on network traffic cost consideration

A large amount and different types of mobile applications (or apps) are being offered to end users via app markets. Existing mobile app recommender systems generally recommend the most popular mobile apps to mobile users to facilitate the proper selection of mobile apps. However, these apps normally generate network traffic, which will consume the user's mobile data plan and may even cause potential security issues. Therefore, more and more mobile users are hesitant or even reluctant to use the mobile apps that are recommended by the mobile app markets. To fill this crucial gap, this paper proposes a mobile app recommendation approach which can provide app recommendations by considering both the apps' popularity and their traffic cost. To achieve this goal, this paper first estimates app network traffic score based on bipartite graph. Then, this paper proposes a flexible approach based on benefit-cost analysis, which can recommend apps by maintaining a balance between the apps' popularity and the traffic cost concern. Finally, this paper evaluates our approach with extensive experiments on a large-scale data set collected from Google Play. The experimental results clearly validate the effectiveness and efficiency of our approach.

A privacy-preserving mobile application recommender system based on trust evaluation

Too many mobile applications in App stores results in information overload in App market. Mobile users are confused in choosing suitable and trustworthy mobile applications due to a large number of available candidates. A mobile application recommender system is a powerful tool that helps users solve this problem. However, there are few feasible recommender systems focusing on recommending mobile applications in the literature. First, few researches study user trust behavior based recommendation on mobile applications. Second, the accuracy and personalization of existing recommender systems need to be further improved. Particularly, privacy preservation is still an open issue in mobile application recommendation. In this paper, we propose two privacy-preserving mobile application recommendation schemes based on trust evaluation. Recommendations on mobile application are generated based on user trust behaviors of mobile application usage. In these two schemes, user private data can be preserved by applying our proposed security protocols and utilizing homomorphic encryption. We further implement two schemes and develop two mobile Apps that can be applied in different scenarios, i.e., a centralized cloud service and distributed social networking. Security analysis, performance evaluation and simulation results show that our schemes have sound security, efficiency, accuracy, and robustness.

Learning Geographical and Mobility Factors for Mobile Application Recommendation

With myriad features and functionalities, mobile app users have the option to run different types of apps when they move to different locations. For a specific place, the decision process involved in choosing a mobile app can be complex and influenced by various factors, such as app popularity, user preferences, geographical influences, and user mobility behaviors. Although several researchers have studied recommendation in mobile apps, they omitted an integrated analysis of the joint effect of multiple factors from a geographical perspective. This article proposes a novel location-based probabilistic factor analysis mechanism that considers multiple factors to help people visiting a new location get an appropriate mobile app recommendation. In particular, the authors model mobile app usage from a geographical perspective. Experimental results on real mobile usage data show that the proposed recommendation method outperforms baseline algorithms by 30 percent.

A Mobile Application Recommendation Framework by Exploiting Personal Preference with Constraints

Explosive mobile applications (Apps) are proliferating with the popularity of mobile devices (e.g., smartphones, tablets). These Apps are developed to satisfy different function needs of users. Majority of existing App Stores have difficulty in recommending proper Apps for users. Therefore, it is of significance to recommend mobile Apps for users according to personal preference and various constraints of mobile devices (e.g., battery power). In this paper, we propose a mobile App recommendation framework by incorporating different requirements from users. We exploit modern portfolio theory (MPT) to combine the popularity of mobile Apps, personal preference, and mobile device constraints for mobile App recommendation. Based on this framework, we discuss the recommendation approaches by constraints of phone power and limited mobile data plan. Extensive evaluations show that the proposed mobile App recommendation framework can well adapt to power and network data plan constraints. It satisfies the user App preference and mobile device constraints.