29 citations · 116 across the 14 of their papers we have counts for
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Representation Learning for Efficient and Effective Similarity Search and Recommendation
Casper Hansen
How data is represented and operationalized is critical for building computational solutions that are both effective and efficient. A common approach is to represent data objects a…
Unsupervised Multi-Index Semantic Hashing
Christian Hansen, Casper Hansen, Jakob Grue Simonsen +2
Semantic hashing represents documents as compact binary vectors (hash codes) and allows both efficient and effective similarity search in large-scale information retrieval. The sta…
Projected Hamming Dissimilarity for Bit-Level Importance Coding in Collaborative Filtering
Christian Hansen, Casper Hansen, Jakob Grue Simonsen +1
When reasoning about tasks that involve large amounts of data, a common approach is to represent data items as objects in the Hamming space where operations can be done efficiently…
Denmark's Participation in the Search Engine TREC COVID-19 Challenge: Lessons Learned about Searching for Precise Biomedical Scientific Information on COVID-19
Lucas Chaves Lima, Casper Hansen, Christian Hansen +5
This report describes the participation of two Danish universities, University of Copenhagen and Aalborg University, in the international search engine competition on COVID-19 (the…
Unsupervised Semantic Hashing with Pairwise Reconstruction
Casper Hansen, Christian Hansen, Jakob Grue Simonsen +2
Semantic Hashing is a popular family of methods for efficient similarity search in large-scale datasets. In Semantic Hashing, documents are encoded as short binary vectors (i.e., h…
Content-aware Neural Hashing for Cold-start Recommendation
Casper Hansen, Christian Hansen, Jakob Grue Simonsen +2
Content-aware recommendation approaches are essential for providing meaningful recommendations for \textit{new} (i.e., \textit{cold-start}) items in a recommender system. We presen…