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A Personalized Recommendation Algorithm on Integration of Item Semantic Similarity and Item Rating Similarity

机译：项目语义相似性和项目评级相似性集成的个性化推荐算法

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—With the rapid development of the Internet and the wide application of e-commerce, recommender system has become a necessity and collaborative filtering is the most successful technology for building recommendation systems. There are many problems in the recommendation approaches, such as data sparsity problem, the issue of new items and scalability issues. Item-based collaborative filtering algorithms can improve the scalability and the traditional user-based collaborative filtering methods, to avoid the bottlenecks of computing users’ correlations by considering the relationships among items. But it still worked poor in solving the issues of sparsity, predictions for new items. In order to effectively solve several problems, this paper presented a recommendation algorithm on integration of item semantic similarity and item rating similarity. The item semantic similarity is calculated combining Earth Mover's Distance and Proportional Transportation Distance, which can utilize the semantic information to measure the similarity between two items based on a solution to the transportation problem from linear optimization1. Then producing recommendation used item-based collaborative filtering integrating the semantic similarity and rating similarity. The presented approach can effectively alleviate the sparsity problem in e-commerce recommender systems.
机译：- 为互联网的快速发展和广泛应用电子商务，推荐系统已成为必需品，协作过滤是建议推荐系统的最成功的技术。推荐方法中存在许多问题，例如数据稀疏问题，新项目的问题和可扩展性问题。基于项目的协作滤波算法可以通过考虑项目之间的关系来提高可扩展性和传统的基于用户的协作滤波方法，以避免计算用户相关性的瓶颈。但它仍然在解决稀疏性问题，对新物品的预测的问题上致力于穷人。为了有效解决几个问题，本文介绍了项目语义相似性和项目评级相似性的集成推荐算法。项目语义相似度是计算地球移动器的距离和比例传输距离的组合，这可以利用语义信息基于从线性优化1的运输问题的解决方案来测量两个项目之间的相似性。然后生成基于项目的建议，使用项目的协作滤波集成了语义相似性和评级相似性。所提出的方法可以有效地减轻电子商务推荐系统中的稀疏问题。

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