Collaborative Filtering Recommendation Algorithm Based on Rating Matrix Filling and Item Predictability
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摘要: 针对传统矩阵填充算法忽略了预测评分与真实评分之间的可信度差异和传统Top-N方法推荐精度低等问题，提出了一种改进的协同过滤算法.该算法首先利用置信系数C区分评分值之间的可信度；然后提出物品可预测性的概念，综合物品的预测评分与物品的可预测性进行物品推荐并将其转化为0-1背包问题，从而筛选出最优化的推荐列表.实验结果表明：该算法能有效缓解稀疏性的影响，提高推荐性能，并且算法具有良好的可扩展性.Abstract: The traditional matrix filling algorithm ignores the difference between true rating and predictive rating, and there is only one standard on the traditional Top-N recommended method. In order to solve these two problems, an improved collaborative filtering algorithm is proposed. Firstly, the confidence coefficient is used to distinguish the credibility of the ratings. Then, a concept of item predictability is proposed. The program recommends items by comprehensively considering the item's predictive ratings and the predictability, and transforming the program into the 0-1 knapsack problem so as to select the optimized recommended list. Experimental results show that the algorithm can effectively alleviate the effect of sparsity and improve the performance of the recommendation, and that the optimization algorithm has good expansibility.
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Key words:
- Collaborative filtering /
- recommendation system /
- predictive ratings /
- similarity /
- 0-1 knapsack problem
1) 本文责任编委 周涛 -
图 4 Movielens_100k中 $k$ 与MAE的关系
Fig. 4 The relationship between $k$ and MAE in Movielens_100k
图 5 Movielens_100k中 $k$ 与precision的关系
Fig. 5 The relationship between $k$ and precision in Movielens_100k
图 6 Movielens_100k中 $k$ 与Coverage的关系
Fig. 6 The relationship between $k$ and Coverage in Movielens_100k
图 7 Movielens_100k中稀疏度与MAE的关系
Fig. 7 The relationship between sparsity and MAE in Movielens_100k
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