论文标题
测试语义和结构对建议准确性和多样性的影响
Testing the Impact of Semantics and Structure on Recommendation Accuracy and Diversity
论文作者
论文摘要
异构信息网络（HIN）形式主义非常灵活，可以实现复杂的建议模型。我们评估了HIN不同部分对建议的准确性和多样性的影响，然后研究这些效果是否仅由于网络中编码的语义内容而引起的。我们使用基于网络结构并更适合Hin形式主义的最近提供的多样性度量。最后，我们随机地将HIN某些部分的边缘缩小，以将网络从其语义内容中清空，同时使其结构相对不受影响。我们表明，网络数据中编码的语义内容对推荐系统的性能至关重要，并且该结构至关重要。
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.
