非线性组合的协同过滤推荐算法

协同过滤是目前最流行的个性化推荐技术，但现有算法局限于用户项目评分矩阵，存在稀疏性、冷开始问题，邻居相似性只考虑用户共同评分项目，忽略项目属性、用户特征相关性；同等对待用户不同时间的兴趣偏好，缺乏实时性。针对这些问题，提出一种非线性组合的协同过滤算法，改进基于项目属性、用户特征的邻居相似性计算方法，获得更加准确的最近邻居集；初始预测评分填充矩阵，以增强其稠密性；最终预测评分增加时间权限，使用户最新兴趣权重最大。实验表明，该算法通过有效降低稀疏性、冷开始和实现实时推荐，提高了预测精度。

Nonlinear combinatorial collaborative filtering recommendation algorithm

Collaborative filtering is the most popular personalized recommendation technology at present. However, the existing algorithms are limited to the user-item rating matrix, which suffers from sparsity and cold-start problems. Neighbours' similarity only considers the items which users evaluate together, but ignores the correlation of item attribute and user characteristic. In addition, the traditional ones have taken users' interests in different time into equal consideration. As a result, they lack real-time nature. Concerning the above problems, this paper proposed a nonlinear combinatorial collaborative filtering algorithm consequently. In order to obtain more accurate nearest neighbour sets, it improved neighbours' similarity calculated approach based on item attribute and user characteristic respectively. Furthermore, the initial prediction rating fills in the rating matrix, so makes it much denser. Lastly, it added time weight to the final prediction rating, so then let users' latest interests take the biggest weight. The experimental results show that the optimized algorithm can increase prediction precision, by way of reducing sparsity and cold-start problems, and realizing real-time recommendation effectively.