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Found 11 papers, 2 papers with code
Report on the SIGIR 2019 Workshop on eCommerce (ECOM19)
The SIGIR 2019 Workshop on eCommerce (ECOM19), was a full day workshop that took place on Thursday, July 25, 2019 in Paris, France.
JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search
In this paper, we introduce Joint Query Intent Understanding (JointMap), a deep learning model to simultaneously learn two different high-level user intent tasks: 1) identifying a query's commercial vs. non-commercial intent, and 2) associating a set of relevant product categories in taxonomy to a product query.
Semantic Product Search for Matching Structured Product Catalogs in E-Commerce
Retrieving all semantically relevant products from the product catalog is an important problem in E-commerce.
APRF-Net: Attentive Pseudo-Relevance Feedback Network for Query Categorization
Query categorization is an essential part of query intent understanding in e-commerce search.
DeepCAT: Deep Category Representation for Query Understanding in E-commerce Search
Mapping a search query to a set of relevant categories in the product taxonomy is a significant challenge in e-commerce search for two reasons: 1) Training data exhibits severe class imbalance problem due to biased click behavior, and 2) queries with little customer feedback (e. g., tail queries) are not well-represented in the training set, and cause difficulties for query understanding.
Diversifying Multi-aspect Search Results Using Simpson's Diversity Index
In search and recommendation, diversifying the multi-aspect search results could help with reducing redundancy, and promoting results that might not be shown otherwise.
De-Biased Modelling of Search Click Behavior with Reinforcement Learning
Our experiments demonstrate the effectiveness of the DRLC model in learning to reduce bias in click logs, leading to improved modeling performance and showing the potential for using DRLC for improving Web search quality.
A Boring-yet-effective Approach for the Product Ranking Task of the Amazon KDD Cup 2022
In this work we describe our submission to the product ranking task of the Amazon KDD Cup 2022.
A Personalized Dense Retrieval Framework for Unified Information Access
Developing a universal model that can efficiently and effectively respond to a wide range of information access requests -- from retrieval to recommendation to question answering -- has been a long-lasting goal in the information retrieval community.
Overview of the TREC 2023 Product Product Search Track
The focus this year was the creation of a reusable collection and evaluation of the impact of the use of metadata and multi-modal data on retrieval accuracy.
Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation
This paper studies retrieval-augmented approaches for personalizing large language models (LLMs), which potentially have a substantial impact on various applications and domains.
