融合偏置的动态专家信任推荐算法

针对协同过滤推荐算法中数据稀疏、冷启动与噪声用户对推荐质量的严重影响，该文将用户-项目评分数据与用户信任关系数据相结合；提出一种融合偏置的动态专家信任推荐算法(BDETA)，首先根据用户信任关系数据进行社区划分，获取用户间显式信任值；其次从社区中用户-项目评分数据获取可信度、隐式信任值；通过结合用户间可信度、显式信任值、隐式信任值动态确定专家信任因子，根据用户的推荐能力为每个社区确定专家数据集；最后结合用户不同评分标准进行评分预测。在真实数据集FilmTrust的实验结果中，能够有效地解决协同过滤中冷启动与数据稀疏问题，可更好地满足用户的个性化推荐需求，并且在推荐系统常用评价指标MAE与RMSE中有着不错的表现。

Fusion Bias Dynamic Expert Trust Recommendation Algorithm

In view of the severe influence of sparse data, cold start and irrelevant noise users on recommendation quality in collaborative filtering recommendation algorithm, this paper combines user-project score data with user trust relationship data. A Biased Dynamic Expert Trust Recommendation Algorithm (BDETA) based on fusion bias is proposed. Firstly, the community is divided according to the user trust relationship data and explicit trust values are obtained.Secondly, the credibility and implicit trust values are obtained from the user-project score data in the community. The expert trust factor is dynamically determined by combining the trust between users, explicit trust value and implicit trust value, and the expert data set is determined for each community according to the recommendation ability of the user.Finally, the different scoring criteria of users in the community data set are combined to predict the scoring for the target users. In the experimental results of real data set FilmTrust, it can effectively solve the problem of collaborative filtering cold startup and data sparseness, better meet the personalized recommendation requirements of users, and has a good performance in the commonly used evaluation index MAE and RMSE of the recommendation system.