Find-similar: similarity browsing as a search tool

By Mark D. Smucker, James Allan. 2006

69 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Information Retrieval & Search

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jimmy J. Lin
2. Andrew Yates
3. Rodrigo Nogueira
4. Zeynep Akkalyoncu Yilmaz
5. Sebastian Hofstätter
6. Allan Hanbury
7. Zhuyun Dai
8. Jamie Callan

In Information Systems

1. Rodrigo Nogueira
2. Andrew Yates
3. Jimmy J. Lin
4. Sebastian Hofstätter
5. Allan Hanbury
6. Sean MacAvaney
7. Jamie Callan
8. Nazli Goharian

In Information Retrieval & Search

1. Jimmy J. Lin
2. Rodrigo Nogueira
3. Andrew Yates
4. Zeynep Akkalyoncu Yilmaz
5. Sebastian Hofstätter
6. Allan Hanbury
7. Zhuyun Dai
8. Wei Yang

Citations in the corpus

• Pretrained Transformers for Text Ranking: BERT and Beyond (2020, background): “sidered a text ranking problem. Text Recommendation. When a system is displaying a search result, it might suggest other texts that may be of interest to the user, for example, to assist in browsing [Smucker and Allan, 2006]. This is frequently encountered on news sites, where related articles of in”
• PubMed related articles: a probabilistic topic-based model for content similarity (2007, unknown): “Consider the case of repeatedly browsing related articles–the user views a citation, examines related articles, selects an interesting one, and repeats (cf. the simulation studies in [17,18]).”