Fuzzy Discrete Particle Swarm Optimization for Solving Traveling Salesman Problem
CIT '04 Proceedings of the The Fourth International Conference on Computer and Information Technology
Being accurate is not enough: how accuracy metrics have hurt recommender systems
CHI '06 Extended Abstracts on Human Factors in Computing Systems
The Standard Particle Swarm Optimization Algorithm Convergence Analysis and Parameter Selection
ICNC '07 Proceedings of the Third International Conference on Natural Computation - Volume 03
CARD: a decision-guidance framework and application for recommending composite alternatives
Proceedings of the 2008 ACM conference on Recommender systems
A Hybrid Recommender System for Context-aware Recommendations of Mobile Applications
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
Enhancing diversity in Top-N recommendation
Proceedings of the third ACM conference on Recommender systems
The million dollar programming prize
IEEE Spectrum
A novel set-based particle swarm optimization method for discrete optimization problems
IEEE Transactions on Evolutionary Computation
AppAware: which mobile applications are hot?
Proceedings of the 12th international conference on Human computer interaction with mobile devices and services
Recommender Systems Handbook
Utilizing implicit feedback and context to recommend mobile applications from first use
Proceedings of the 2011 Workshop on Context-awareness in Retrieval and Recommendation
AppJoy: personalized mobile application discovery
MobiSys '11 Proceedings of the 9th international conference on Mobile systems, applications, and services
Rank and relevance in novelty and diversity metrics for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
Improving Aggregate Recommendation Diversity Using Ranking-Based Techniques
IEEE Transactions on Knowledge and Data Engineering
Adaptive diversification of recommendation results via latent factor portfolio
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Multiple objective optimization in recommender systems
Proceedings of the sixth ACM conference on Recommender systems
Pareto-efficient hybridization for multi-objective recommender systems
Proceedings of the sixth ACM conference on Recommender systems
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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.