论文标题
基于电影推荐系统中特征因素的矩阵分解模型
A Matrix Decomposition Model Based on Feature Factors in Movie Recommendation System
论文作者
论文摘要
当前，矩阵分解是使用因子分解来有效处理大规模评级矩阵，是使用最广泛使用的协作过滤算法之一。它主要使用用户和项目之间的交互记录来预测评分。根据项目和用户的特征属性，本文提出了一种新的UISVD ++模型，该模型将电影类型属性和用户的年龄属性融合到SVD ++框架中。通过将年龄属性投影到用户的隐式空间以及类型属性中，该模型丰富了用户和项目的侧面信息。最后，我们对两个公共数据集（Movielens-100K和Movielens-1M）进行了比较实验。实验结果表明，在预测分数的任务中，该模型的预测准确性优于其他基线。此外，这些结果还表明，UISVD ++可以有效地减轻寒冷的开始情况。
Currently, matrix decomposition is one of the most widely used collaborative filtering algorithms by using factor decomposition to effectively deal with large-scale rating matrix. It mainly uses the interaction records between users and items to predict ratings. Based on the characteristic attributes of items and users, this paper proposes a new UISVD++ model that fuses the type attributes of movies and the age attributes of users into SVD++ framework. By projecting the age attribute into the user's implicit space and the type attribute into the item's implicit space, the model enriches the side information of the users and items. At last, we conduct comparative experiments on two public data sets, Movielens-100K and Movielens-1M. Experiment results express that the prediction accuracy of this model is better than other baselines in the task of predicting scores. In addition, these results also show that UISVD++ can effectively alleviate the cold start situation.
