基于张量分解和深度学习的混合推荐算法

Blending recommendation algorithm based on tensor decompositions and deep learning

张量分解和深度学习已被应用于推荐系统,并取得了较好的效果.张量分解较好地从用户对推荐对象评分中提取用户、推荐对象以及其他影响因素的隐性的特征,将这些特征进行匹配,给出推荐策略,但这种方法忽略了用户、推荐对象以及其他影响因素现有辅助数据信息中的显性特征.深度学习是从辅助信息中提取用户、推荐对象以及其他影响因素的特征,并进行匹配给出推荐策略,却忽略了用户评分数据中用户、推荐对象以及其他影响因素的隐性特征.将张量分解和深度学习两种推荐方法相融合,提出一种基于张量分解和深度学习的混合推荐算法.使用张量分解算法和深度学习分别从三阶用户评分数据和多源异构辅助信息中提取用户特征和推荐对象特征,并将它们匹配得出用户对推荐对象的需求或喜爱的预测评分,再将两种算法的预测评分进行融合给出最终综合评分,从而提高个性化推荐的精准度.对比实验证明混合推荐算法与传统的协同过滤算法相比误差降低了34.0%.

The tensor decomposition and deep learning have been applied to the recommendation systems and better results have become true.The tensor decomposition algorithm better extracts the hidden features of the users,recommended objects and other influencing factors from the user rating data,and the features are matched each other to give recommendation strategies.But the algorithm ignores the features in the auxiliary data information of the user,recommended objects and other influencing factors.Deep learning extracts the features of users,recommended objects and other influencing factors from auxiliary information,and matches them to give recommendation strategies,but ignores the implicit characteristics of users,recommended objects and other influencing factors in user rating data.A blending recommendation algorithm based on tensor decomposition and deep learning is proposed,which blends the two recommendation methods of tensor decomposition and deep learning.Tensor decomposition algorithm and deep learning are used to extract user features and recommendation object features from third order user rating data and multi source heterogeneous auxiliary information respectively,and then match them to obtain prediction ratings of user's demand or preference for recommendation objects,and the prediction ratings from the two algorithms are blended to give the final comprehensive ratings.The blending recommendation algorithm will improve the accuracy of personalized recommendation.Compared with traditional collaborative filtering algorithm,the error of blending recommendation algorithm is reduced by 34.0%.