基于slope-one算法改进评分矩阵填充的协同过滤算法研究

Research on collaborative filtering algorithm based on slope-one algorithm to improve score matrix filling

为解决协同过滤算法中的数据稀疏性问题,提出了一种改进的协同过滤算法。该算法使用slope-one算法计算出来的评分预测值来填充评分矩阵中的未评分项目,然后在填充后的用户—项目评分矩阵上通过基于用户的协同过滤方法给出推荐。利用slope-one算法计算出来的评分预测值作为回填值,既能降低评分矩阵的稀疏性,也保证了回填值的多样性,从而减少均值、中值等单一填充值造成的推荐误差。在MovieLens-1M数据集上对该改进算法和协同过滤算法及均值中心化处理的算法作五折交叉实验,结果表明,基于评分预测值填充数据后的协同过滤算法有效地缓解了数据稀疏性问题,并且有更好的推荐效果。

In order to solve the problem of data sparsity in the collaborative filtering algorithm,this paper proposed an improved collaborative filtering algorithm.The algorithm filled the unrated items in the scoring matrix using the prediction value calculated by the slope-one algorithm and then gave recommendations based on the user-based collaborative filtering method based on the filled user-item scoring matrix.Using the predictive value of slope-one algorithm as the backfill value could not only reduce the sparsity of the scoring matrix,but also ensured the diversity of backfill values,so as to reduce the recommended error caused by the single fill value such as mean value and median value.It performed half off cross-validation experiments on the MovieLens-1M dataset.The results show that the collaborative filtering algorithm based on the score prediction data effectively mitigates data sparsity and has better performance recommended effect.