考虑显式评分的基于二部图的推荐算法

Research on Network-based Recommendation Algorithm Considering Explicit Ratings

为解决传统网络结构推荐算法的造成信息丢失和数据稀疏而带来的推荐准确性低问题,本文在用户推荐能量流动时充分考虑用户对项目的显式评分,提出用户兴趣相似系数和用户评分代表能力两个概念,构造改进的基于网络结构推荐算法。实验结果表明改进后的算法能有效提高推荐算法的准确性,使推荐服务更好地满足用户的偏好需求。

Recently,personalized recommender systems have become indispensable in a wide variety of commercial applications due to the vast amount of overloaded information.Network-based recommendation algorithms for user-object link predictions have achieved significant developments.But most previous researches on network-based algorithm tend to ignore users' explicit ratings for objects or only select users' higher ratings which lead to loss of information and even sparser data.With this understanding,an improved network-based recommendation algorithm is proposed.In the process of reallocation of user's recommendation power,users' explicit scores are originally transfers to users' interest similarity and user's representativeness in this paper.Finally,the proposed approach is validated by performing large-scale random sub-sampling experiments on a widely used data set(Movielens)and our method is compared with another algorithm by two accuracy criteria.Results show that our approach significantly outperforms the original network-based recommendation algorithm.