Personalized Search
By James E. Pitkow, Hinrich Schütze, T. Cass, Robert Cooley, Don Turnbull, A. Edmonds, Eytan Adar, Thomas Breuel. 2015
212 citations recorded by the source corpus.
As the volume of electronically available information grows, relevant items become harder to find. This work presents an approach to personalizing search results in scientific publication databases. This work focuses on re-ranking search results from existing search engines like Solr or ElasticSearch. This work also includes the development of Obelix, a new recommendation system used to re-rank search results. The project was proposed and performed at CERN, using the scientific publications available on the CERN Document Server (CDS). This work experiments with re-ranking using offline and online evaluation of users and documents in CDS. The experiments conclude that the personalized search result outperform both latest first and word similarity in terms of click position in the search result for global search in CDS.
Subfields: Information Systems
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems
- Alan Mislove
- Christo Wilson
- Ankur Gupta
- Shunbao Chen
- Jim Kleban
- Balachander Krishnamurthy
- Lihong Li
- Nicholas A. Diakopoulos
Citations in the corpus
- Measuring personalization of web search (2013, background): “Personalized search has been extensively studied in the literature [8, 19, 23, 27, 29, 36, 39, 43].”
- Counterfactual Estimation and Optimization of Click Metrics in Search Engines: A Case Study (2015, background): “Second, in some applications like personalized search [24], judges often lack necessary information to provide sensible labels.”