Search Results for author:
Found 8 papers, 3 papers with code
PEFA: Parameter-Free Adapters for Large-scale Embedding-based Retrieval Models
For product search, PEFA improves the Recall@100 of the fine-tuned ERMs by an average of 5. 3% and 14. 5%, for PEFA-XS and PEFA-XL, respectively.
MICO: Selective Search with Mutual Information Co-training
In contrast to traditional exhaustive search, selective search first clusters documents into several groups before all the documents are searched exhaustively by a query, to limit the search executed within one group or only a few groups.
On the Value of Behavioral Representations for Dense Retrieval
We consider text retrieval within dense representational space in real-world settings such as e-commerce search where (a) document popularity and (b) diversity of queries associated with a document have a skewed distribution.
Embracing Structure in Data for Billion-Scale Semantic Product Search
When training a dyadic model, one seeks to embed two different types of entities (e. g., queries and documents or users and movies) in a common vector space such that pairs with high relevance are positioned nearby.
A Study of Context Dependencies in Multi-page Product Search
In this paper, we study RF techniques based on both long-term and short-term context dependencies in multi-page product search.
Leverage Implicit Feedback for Context-aware Product Search
However, customers with little or no purchase history do not benefit from personalized product search.
Semantic Product Search
To address these issues, we train a deep learning model for semantic matching using customer behavior data.
Adaptive, Personalized Diversity for Visual Discovery
Search queries are appropriate when users have explicit intent, but they perform poorly when the intent is difficult to express or if the user is simply looking to be inspired.
