Contextual and sequential user embeddings for large-scale music recommendation
Recommender systems play an important role in providing an engaging experience on
online music streaming services. However, the musical domain presents distinctive …
online music streaming services. However, the musical domain presents distinctive …
Content-aware neural hashing for cold-start recommendation
Content-aware recommendation approaches are essential for providing meaningful
recommendations for new (ie, cold-start) items in a recommender system. We present a …
recommendations for new (ie, cold-start) items in a recommender system. We present a …
[PDF][PDF] Neural Weakly Supervised Fact Check-Worthiness Detection with Contrastive Sampling-Based Ranking Loss.
This paper describes the winning approach used by the Copenhagen team in the CLEF-
2019 CheckThat! lab. Given a political debate or speech, the aim is to predict which …
2019 CheckThat! lab. Given a political debate or speech, the aim is to predict which …
An efficient and robust semantic hashing framework for similar text search
Similar text search aims to find texts relevant to a given query from a database, which is
fundamental in many information retrieval applications, such as question search and …
fundamental in many information retrieval applications, such as question search and …
Interactive learning for multimedia at large
Interactive learning has been suggested as a key method for addressing analytic multimedia
tasks arising in several domains. Until recently, however, methods to maintain interactive …
tasks arising in several domains. Until recently, however, methods to maintain interactive …
Search-Efficient Computerized Adaptive Testing
Computerized Adaptive Testing (CAT) arises as a promising personalized test mode in
online education, targeting at revealing students' latent knowledge state by selecting test …
online education, targeting at revealing students' latent knowledge state by selecting test …
Unsupervised semantic hashing with pairwise reconstruction
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 (ie, hash …
datasets. In Semantic Hashing, documents are encoded as short binary vectors (ie, hash …
Unsupervised multi-index semantic hashing
Semantic hashing represents documents as compact binary vectors (hash codes) and
allows both efficient and effective similarity search in large-scale information retrieval. The …
allows both efficient and effective similarity search in large-scale information retrieval. The …
Efficient document retrieval by end-to-end refining and quantizing BERT embedding with contrastive product quantization
Efficient document retrieval heavily relies on the technique of semantic hashing, which
learns a binary code for every document and employs Hamming distance to evaluate …
learns a binary code for every document and employs Hamming distance to evaluate …
Long-tail hashing
Hashing, which represents data items as compact binary codes, has been becoming a more
and more popular technique, eg, for large-scale image retrieval, owing to its super fast …
and more popular technique, eg, for large-scale image retrieval, owing to its super fast …