Mobile App Recommender System Research Articles

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.

Leveraging Online Word of Mouth for Personalized App Recommendation

The popularity and development of mobile devices and mobile apps have dramatically changed human life. Due to the tremendous and still rapidly growing number of mobile apps, helping users find apps that satisfy their demands remains a difficult task. To address this problem, we propose a personalized mobile app recommender system based on the textual data of user reviews on the app store. Topic modeling techniques are applied to extract hidden topics of user reviews, and the probability distributions of the topics are utilized to represent the features of the apps. Then, the user profile is constructed based on the user’s installed apps to capture user preferences. Both the topic distributions of the apps and user preferences are taken into account to produce recommendation scores to generate recommendation lists for target users. We crawl real-world data sets from app stores to evaluate the performance. The experimental results show that user reviews are effective for deriving the features of apps, and the proposed user-review-based app recommender system improves the performance of existing approaches. We conclude that the user reviews on the app store effectively represent the features of apps and play a significant role in personalized app recommender systems.

Mining Exploratory Behavior to Improve Mobile App Recommendations

With the widespread usage of smart phones, more and more mobile apps are developed every day, playing an increasingly important role in changing our lifestyles and business models. In this trend, it becomes a hot research topic for developing effective mobile app recommender systems in both industry and academia. Compared with existing studies about mobile app recommendations, our research aims to improve the recommendation effectiveness based on analyzing a psychological trait of human beings, exploratory behavior, which refers to a type of variety-seeking behavior in unfamiliar domains. To this end, we propose a novel probabilistic model named Goal-oriented Exploratory Model (GEM), integrating exploratory behavior identification with personalized item recommendation. An algorithm combining collapsed Gibbs sampling and Expectation Maximization is developed for model learning and inference. Through extensive experiments conducted on a real dataset, the proposed model demonstrates superior recommendation performances and good interpretability compared with state-of-art recommendation methods. Moreover, empirical analyses on exploratory behavior find that individuals with a strong exploratory tendency exhibit behavioral patterns of variety seeking, risk taking, and higher involvement. Besides, mobile apps that are less popular or in the long tail possess greater potential of arousing exploratory behavior in individuals.

Multi-objective mobile app recommendation: A system-level collaboration approach

The tremendous increase of mobile apps has given rise to the significant challenge of app discovery. To alleviate such a challenge, recommender systems are employed. However, the development of recommender systems for mobile apps is at a slow pace. One main reason is that a general framework for efficient development is still missing. Meanwhile, most existing systems mainly focus on single objective recommendations, which only reflect monotonous app needs of users. For such reasons, we initially present a general framework for developing mobile app recommender systems, which leverages the multi-objective approach and the system-level collaboration strategy. Our framework thus can satisfy ranges of app needs of users by integrating the strengths of various recommender systems. To implement the framework, we originally introduce the method of swarm intelligence to the recommendation of mobile apps. To be detailed, we firstly present a new set based optimization problem which is originated from the collaborative app recommendation. We then propose a novel set based Particle Swarm Optimization (PSO) algorithm, namely, the Cylinder Filling Set based PSO, to address such a problem. Furthermore, we implement the algorithm based on three popular mobile app recommender systems and conduct evaluations. Results verify that our framework and algorithm are with promising performance from both the effectiveness and efficiency.