# Contextual Document Similarity for Content-based Literature Recommender Systems

arXiv: https://arxiv.org/abs/2008.00202 · HTML: https://mlsanity.com/paper/2008.00202/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [CSFCube -- A Test Collection of Computer Science Research Articles for Faceted Query by Example](https://mlsanity.com/paper/2103.12906/) (2021)
- [Specialized Document Embeddings for Aspect-based Similarity of Research Papers](https://mlsanity.com/paper/2203.14541/) (2022)
- [DORIS-MAE: Scientific Document Retrieval using Multi-level Aspect-based Queries](https://mlsanity.com/paper/2310.04678/) (2023)
- [Fundamental Limitations on Subquadratic Alternatives to Transformers](https://mlsanity.com/paper/2410.04271/) (2024)
- [The Critical Role of Aspects in Measuring Document Similarity](https://mlsanity.com/paper/2601.03435/) (2026)

## Abstract (arXiv)

To cope with the ever-growing information overload, an increasing number of digital libraries employ content-based recommender systems. These systems traditionally recommend related documents with the help of similarity measures. However, current document similarity measures simply distinguish between similar and dissimilar documents. This simplification is especially crucial for extensive documents, which cover various facets of a topic and are often found in digital libraries. Still, these similarity measures neglect to what facet the similarity relates. Therefore, the context of the similarity remains ill-defined. In this doctoral thesis, we explore contextual document similarity measures, i.e., methods that determine document similarity as a triple of two documents and the context of their similarity. The context is here a further specification of the similarity. For example, in the scientific domain, research papers can be similar with respect to their background, methodology, or findings. The measurement of similarity in regards to one or more given contexts will enhance recommender systems. Namely, users will be able to explore document collections by formulating queries in terms of documents and their contextual similarities. Thus, our research objective is the development and evaluation of a recommender system based on contextual similarity. The underlying techniques will apply established similarity measures and as well as neural approaches while utilizing semantic features obtained from links between documents and their text.