Combining Unstructured Content and Knowledge Graphs into Recommendation Datasets

Popular book and movie recommendation datasets can be associated with Knowledge Graphs (KG) that enable the development of KG-based recommender systems. However, most of these approaches are based on Collaborative Filtering, leaving Content-based Filtering approaches unexploited. This is partially due to the lack of items’ content-based information (e.g. summary texts of movies and books) in datasets. To facilitate the research in achieving both KG-aware and content-aware recommender systems, we contribute to public domain resources through the creation of a large-scale Movie-KG dataset and an extension of the already public Amazon-Book dataset through incorporation of text descriptions crawled from external sources. Both datasets provide items’ descriptive texts that enable recommendations based on unstructured content. We provide benchmark results as well as showing the value of the content-based information in making recommendations.

Duke Scholars

Author Jian Pei Computer Science