基于评分偏好和项目属性的协同过滤算法

针对传统的协同过滤推荐由于数据稀疏性导致物品间相似性计算不准确、推荐准确度不高的问题,文中提出了一种基于用户评分偏好模型、融合时间因素和物品属性的协同过滤算法,通过改进物品相似度度量公式来提高推荐的准确度。首先考虑到不同用户的评分习惯存在差异这一客观现象,引入评分偏好模型,通过模型计算出用户对评分类别的偏好,以用户对评分类别的偏好来代替用户对物品的评分,重建用户-物品评分矩阵;其次基于时间效应,引入时间权重因子,将时间因素纳入评分相似度计算中;然后结合物品的属性,将物品属性相似度和评分相似度进行加权,完成物品最终相似度的计算;最后通过用户偏好公式来计算用户对候选物品的偏好,依据偏好对用户进行top-N推荐。在MovieLens-100K和MovieLens-Latest-Small数据集上进行了充分实验。结果表明,相比已有的经典的协同过滤算法,所提算法的准确率和召回率在MovieLens-100K数据集上提高了9%~27%,在MovieLens-Latest-Small数据集上提高了16%~28%。因此,改进的协同过滤算法能有效提高推荐的准确度,有效缓解数据稀疏性问题。

Collaborative Filtering Algorithm Based on Rating Preference and Item Attributes

Aiming at the impact of data sparsity of traditional collaborative filtering algorithm resulting in inaccuracy of item similarity,this paper proposed an improved collaborative filtering algorithm based on user rating preference model by incorporating time factor and item attributes.The algorithm improves the accuracy by modifying item similarity formula.Firstly,a preference model is introduced by considering the differences of user’s rating habits.A user-item rating matrix is rebuilt by replacing user’s rating of item with the preference for rating class.Then time weight function is designed and put into rating similarity based on time effect.What’s more,item similarity is calculated by incorporating item attributes similarity and rating similarity.Finally,top-N recommendation is completed after calculating user preference for item by the user preference formula.The experiment results suggest that the precision and recall of the proposed algorithm is increased by 9%~27%on the MovieLens-100K dataset and 16%~28%on the MovieLens-Latest-Small dataset than classical approaches.Therefore,the improved algorithm can improve recommendation accuracy and mitigate the problem of data sparsity effectively.