Search Results for author:
Found 8 papers, 7 papers with code
GLEN: Generative Retrieval via Lexical Index Learning
For training, GLEN effectively exploits a dynamic lexical identifier using a two-phase index learning strategy, enabling it to learn meaningful lexical identifiers and relevance signals between queries and documents.
ConQueR: Contextualized Query Reduction using Search Logs
One extracts core terms from an original query at the term level, and the other determines whether a sub-query is a suitable reduction for the original query at the sequence level.
SpaDE: Improving Sparse Representations using a Dual Document Encoder for First-stage Retrieval
Sparse document representations have been widely used to retrieve relevant documents via exact lexical matching.
S-Walk: Accurate and Scalable Session-based Recommendationwith Random Walks
Session-based recommendation (SR) predicts the next items from a sequence of previous items consumed by an anonymous user.
MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories
Automated metaphor detection is a challenging task to identify metaphorical expressions of words in a sentence.
Session-aware Linear Item-Item Models for Session-based Recommendation
Session-based recommendation aims at predicting the next item given a sequence of previous items consumed in the session, e. g., on e-commerce or multimedia streaming services.
Local Collaborative Autoencoders
Top-N recommendation is a challenging problem because complex and sparse user-item interactions should be adequately addressed to achieve high-quality recommendation results.
Collaborative Distillation for Top-N Recommendation
Knowledge distillation (KD) is a well-known method to reduce inference latency by compressing a cumbersome teacher model to a small student model.
