원문정보
초록
영어
Recommender systems provide users with personalized suggestions for products. A key challenge is how to improve the diversity of recommendation results as much as possible, while maintaining reliably accurate suggestions. Although the bipartite graph based probabilistic spreading algorithm has its advantages of good accuracy and low computational complexity, its diversity is poor. In this paper, we introduce a cost-aware probabilistic spreading algorithm, and show how it can improve both recommendation accuracy and diversity by designing different spreading costs. Comparative experiments on widely used datasets confirm the effectiveness of the cost-aware probabilistic spreading approach in terms of accuracy, aggregate diversity and individual diversity of recommendation results. In addition, the time complexity of the proposed algorithm is also analyzed.
목차
1. Introduction
2. Probabilistic Spreading
3. Cost-Aware Probabilistic Spreading
3.1 Model
3.2 Spreading Probability and Spreading Cost
3.3 Algorithmic Implementation
4. Experiments
4.1 Recommendation Performance Metrics
4.2 Numerical Results
4.3 Complexity Analysis
5. Conclusion
References