A comprehensive survey on trustworthy recommender systems
As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …
people make appropriate decisions in an effective and efficient way, by providing …
Recommendation as instruction following: A large language model empowered recommendation approach
In the past decades, recommender systems have attracted much attention in both research
and industry communities. Existing recommendation models mainly learn the underlying …
and industry communities. Existing recommendation models mainly learn the underlying …
Dense text retrieval based on pretrained language models: A survey
Text retrieval is a long-standing research topic on information seeking, where a system is
required to return relevant information resources to user's queries in natural language. From …
required to return relevant information resources to user's queries in natural language. From …
Lexlip: Lexicon-bottlenecked language-image pre-training for large-scale image-text sparse retrieval
Image-text retrieval (ITR) aims to retrieve images or texts that match a query originating from
the other modality. The conventional dense retrieval paradigm relies on encoding images …
the other modality. The conventional dense retrieval paradigm relies on encoding images …
Large language model based long-tail query rewriting in taobao search
W Peng, G Li, Y Jiang, Z Wang, D Ou, X Zeng… - … Proceedings of the …, 2024 - dl.acm.org
In the realm of e-commerce search, the significance of semantic matching cannot be
overstated, as it directly impacts both user experience and company revenue. Along this …
overstated, as it directly impacts both user experience and company revenue. Along this …
Distill-vq: Learning retrieval oriented vector quantization by distilling knowledge from dense embeddings
Vector quantization (VQ) based ANN indexes, such as Inverted File System (IVF) and
Product Quantization (PQ), have been widely applied to embedding based document …
Product Quantization (PQ), have been widely applied to embedding based document …
{FAERY}: An {FPGA-accelerated} embedding-based retrieval system
Embedding-based retrieval (EBR) is widely used in recommendation systems to retrieve
thousands of relevant candidates from a large corpus with millions or more items. A good …
thousands of relevant candidates from a large corpus with millions or more items. A good …
SPFresh: Incremental In-Place Update for Billion-Scale Vector Search
Approximate Nearest Neighbor Search (ANNS) on high dimensional vector data is now
widely used in various applications, including information retrieval, question answering, and …
widely used in various applications, including information retrieval, question answering, and …
Intelligent request strategy design in recommender system
Waterfall Recommender System (RS), a popular form of RS in mobile applications, is a
stream of recommended items consisting of successive pages that can be browsed by …
stream of recommended items consisting of successive pages that can be browsed by …
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 …