Zobrazeno 1 - 10
of 226
pro vyhledávání: '"Wang, Haixun"'
Autor:
Wang, Haixun, Na, Taesik
Publikováno v:
SIGIR Forum 57, 2, Article 24 (December 2023), 19 pages
E-commerce search and recommendation usually operate on structured data such as product catalogs and taxonomies. However, creating better search and recommendation systems often requires a large variety of unstructured data including customer reviews
Externí odkaz:
http://arxiv.org/abs/2312.03217
Autor:
Wang, Xiaochen, Xiao, Xiao, Zhang, Ruhan, Zhang, Xuan, Na, Taesik, Tenneti, Tejaswi, Wang, Haixun, Ma, Fenglong
Efficient and accurate product relevance assessment is critical for user experiences and business success. Training a proficient relevance assessment model requires high-quality query-product pairs, often obtained through negative sampling strategies
Externí odkaz:
http://arxiv.org/abs/2311.06444
Autor:
Xie, Yuqing, Na, Taesik, Xiao, Xiao, Manchanda, Saurav, Rao, Young, Xu, Zhihong, Shu, Guanghua, Vasiete, Esther, Tenneti, Tejaswi, Wang, Haixun
The key to e-commerce search is how to best utilize the large yet noisy log data. In this paper, we present our embedding-based model for grocery search at Instacart. The system learns query and product representations with a two-tower transformer-ba
Externí odkaz:
http://arxiv.org/abs/2209.05555
With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications. However, many of them remain difficult to diagnose what aspects of the dee
Externí odkaz:
http://arxiv.org/abs/2110.14844
Recently, there is an effort to extend fine-grained entity typing by using a richer and ultra-fine set of types, and labeling noun phrases including pronouns and nominal nouns instead of just named entity mentions. A key challenge for this ultra-fine
Externí odkaz:
http://arxiv.org/abs/2106.04098
Adversarial Robustness through Bias Variance Decomposition: A New Perspective for Federated Learning
Federated learning learns a neural network model by aggregating the knowledge from a group of distributed clients under the privacy-preserving constraint. In this work, we show that this paradigm might inherit the adversarial vulnerability of the cen
Externí odkaz:
http://arxiv.org/abs/2009.09026
Publikováno v:
27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL '19), November 5-8, 2019, Chicago, IL, USA
A natural language interface (NLI) to databases is an interface that translates a natural language question to a structured query that is executable by database management systems (DBMS). However, an NLI that is trained in the general domain is hard
Externí odkaz:
http://arxiv.org/abs/1908.10917
Publikováno v:
Proceedings of the VLDB Endowment, Volume 10 Issue 5, January 2017
Question answering (QA) has become a popular way for humans to access billion-scale knowledge bases. Unlike web search, QA over a knowledge base gives out accurate and concise results, provided that natural language questions can be understood and ma
Externí odkaz:
http://arxiv.org/abs/1903.02419
Relational database management systems (RDBMSs) are powerful because they are able to optimize and answer queries against any relational database. A natural language interface (NLI) for a database, on the other hand, is tailored to support that speci
Externí odkaz:
http://arxiv.org/abs/1809.02649
Autor:
Cui, Wanyun, Zhou, Xiyou, Lin, Hangyu, Xiao, Yanghua, Wang, Haixun, Hwang, Seung-won, Wang, Wei
Verbs are important in semantic understanding of natural language. Traditional verb representations, such as FrameNet, PropBank, VerbNet, focus on verbs' roles. These roles are too coarse to represent verbs' semantics. In this paper, we introduce ver
Externí odkaz:
http://arxiv.org/abs/1710.07695