Creating High Level Content Descriptors for Recommender Systems Datasets
2018 37th International Conference of the Chilean Computer Science Society (SCCC)(2018)
摘要
Information Retrieval and Recommender Systems have been frequently evaluated using indexes based on variants and extensions of precision-like measures. Likewise, approaches for diversity evaluation have been proposed. However, these measures are usually defined in terms of a set of high level content descriptors known as information nuggets that are hard to obtain. We propose a method to create these nuggets using social tags, providing datasets with annotations to evaluate content diversity in recommender systems. Since recommending items to a target user is analogous to searching documents from a query, this method might be extended to Information Retrieval.
更多查看译文
关键词
Recommender systems,Cultural differences,Motion pictures,Rocks,Encyclopedias
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要