论文标题
项目建议使用用户反馈数据和项目配置文件
Item Recommendation Using User Feedback Data and Item Profile
论文作者
论文摘要
基质分解（MS）是一种基于协作过滤（CF）方法，该方法被广泛用于推荐系统（RS）。在这项研究工作中，我们根据用户的反馈数据来处理内容管理系统（CMS）中用户的内容建议问题。 CMS用于向公司或组织的员工发布和推销精选的内容。在这里，我们使用用户的反馈数据和内容数据来解决内容建议问题。我们准备单个用户配置文件，然后根据不同类别（包括直接互动，社交共享和阅读统计信息）来生成建议结果。随后，我们分析了不同类别对建议结果的影响。结果表明，不同类别的反馈数据对建议准确性有不同的影响。如果我们在推荐任务中包括所有类型的数据，则最佳性能就可以实现。我们还将内容相似性作为正规化项纳入了用于设计混合模型的MF模型。实验结果表明，与传统的基于MF的模型相比，提出的混合模型表现出更好的性能。
Matrix factorization (MS) is a collaborative filtering (CF) based approach, which is widely used for recommendation systems (RS). In this research work, we deal with the content recommendation problem for users in a content management system (CMS) based on users' feedback data. The CMS is applied for publishing and pushing curated content to the employees of a company or an organization. Here, we have used the user's feedback data and content data to solve the content recommendation problem. We prepare individual user profiles and then generate recommendation results based on different categories, including Direct Interaction, Social Share, and Reading Statistics, of user's feedback data. Subsequently, we analyze the effect of the different categories on the recommendation results. The results have shown that different categories of feedback data have different impacts on recommendation accuracy. The best performance achieves if we include all types of data for the recommendation task. We also incorporate content similarity as a regularization term into an MF model for designing a hybrid model. Experimental results have shown that the proposed hybrid model demonstrates better performance compared with the traditional MF-based models.
