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20152024
most citedContent-aware Neural Hashing for Cold-start Recommendation

29 citations · 145 across the 25 of their papers we have counts for

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12 papers · 1 filter

cs.IR202225 cited

Principled Multi-Aspect Evaluation Measures of Rankings

Maria Maistro, Lucas Chaves Lima, Jakob Grue Simonsen +1

Information Retrieval evaluation has traditionally focused on defining principled ways of assessing the relevance of a ranked list of documents with respect to a query. Several met…

cs.IR20218 cited

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…

cs.IR20216 cited

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…

cs.IR20204 cited

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…

cs.IR202015 cited

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…

cs.IR202029 cited

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…