测试语义和结构对推荐准确性和多样性的影响
Testing the Impact of Semantics and Structure on Recommendation Accuracy and Diversity
摘要：
异构信息网络（HIN）形式主义非常灵活，并支持复杂的推荐模型。我们评估了HIN的不同部分对推荐的准确性和多样性的影响，然后调查这些影响是否仅是由于网络中编码的语义内容。我们使用最近提出的多样性措施，这是基于网络结构，更适合HIN形式主义。最后，我们随机打乱HIN的某些部分的边缘，以清空网络的语义内容，同时使其结构相对不受影响。我们表明，编码在网络数据中的语义内容的推荐系统的性能有一个有限的重要性和结构是至关重要的。
英文摘要：
The Heterogeneous Information Network (HIN) formalism is very flexible and enables complex recommendations models. We evaluate the effect of different parts of a HIN on the accuracy and the diversity of recommendations, then investigate if these effects are only due to the semantic content encoded in the network. We use recently-proposed diversity measures which are based on the network structure and better suited to the HIN formalism. Finally, we randomly shuffle the edges of some parts of the HIN, to empty the network from its semantic content, while leaving its structure relatively unaffected. We show that the semantic content encoded in the network data has a limited importance for the performance of a recommender system and that structure is crucial.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
