The recommendation system is an effective means to solve the information overload problem that exists in social networks, which is also one of the most common applications of big data technology. Thus, the matrix decomposition recommendation model based on scoring data has been extensively studied and applied in recent years, but the data sparsity problem affects the recommendation quality of the model. To this end, this paper proposes a hybrid recommendation model based on deep emotion analysis and multi-source view fusion which makes a personalized recommendation with user-post interaction ratings, implicit feedback and auxiliary information in a hybrid recommendation system. Specifically, the HITS algorithm is used to process the data set, which can filter out the users and posts with high influence and eliminate most of the low-quality users and posts. Secondly, the calculation method of measuring the similarity of candidate posts and the method of calculating K nearest neighbors are designed, which solves the problem that the text description information of post content in the recommendation system is difficult to mine and utilize. Then, the cooperative training strategy is used to achieve the fusion of two recommended views, which eliminates the data distribution deviation added to the training data pool in the iterative training. Finally, the performance of the DMHR algorithm proposed in this paper is compared with other state-of-art algorithms based on the Twitter dataset. The experimental results show that the DMHR algorithm has significant improvements in score prediction and recommendation performance.

中文翻译：

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基于深度情感分析和多源视图融合的社交媒体混合推荐模型

推荐系统是解决社交网络中存在的信息过载问题的有效手段，而社交网络也是大数据技术最普遍的应用之一。因此，近年来对基于评分数据的矩阵分解推荐模型进行了广泛的研究和应用，但是数据稀疏性问题影响了模型的推荐质量。为此，本文提出了一种基于深度情感分析和多源视图融合的混合推荐模型，该模型在混合推荐系统中提供了具有用户岗位互动评分，隐式反馈和辅助信息的个性化推荐。具体来说，HITS算法用于处理数据集，这样可以过滤掉具有较高影响力的用户和帖子，并消除大多数低质量的用户和帖子。其次，设计了候选职位相似度的计算方法和K个最近邻的计算方法，解决了推荐系统中帖子内容的文本描述信息难以挖掘和利用的问题。然后，使用协作训练策略来实现两个推荐视图的融合，从而消除了迭代训练中添加到训练数据库中的数据分布偏差。最后，将本文提出的DMHR算法的性能与其他基于Twitter数据集的最新算法进行比较。