基于路径融合的多图层推荐算法

Path Integration-Based Multiple Layers Recommendation

图推荐算法中资源分配矩阵的计算和存储占用大量开销,为了提高基于图推荐的效率,提出一种基于路径融合的多图层混合策略推荐算法(PIML).该算法中,为了提高推荐精度并综合考虑多种因素以给出全面推荐,基于路径融合方法,利用分流策略优化资源分配,并对时间和评分因素加权,将人口统计学和物品内容信息融入到多图层,实现基于二部图推荐.实验结果表明:该算法没有增加时间开销,提高了推荐精度,使推荐更全面更灵活,并可实时推荐.

In graph-based recommendation, the calculation and storage of resource allocation matrix take up a lot of overhead, in order to improve efficiency of algorithm. Path Integration-Based Multiple Layers Recommendation（PIML） was proposed. In the algorithm, path integration-based shunt strategy was used to dynamically allocate resources, and timestamp and ratings were added to the graph. On the other hand, demographic information and item content information were added to the multiple layers structure, considering a variety of factors, it gave more comprehensive recommendation. Experimental results show that the algorithm improves the accuracy without increasing time cost; and makes recommendation more comprehensive and can give real-time recommendation.