Zobrazeno 1 - 10
of 13
pro vyhledávání: '"Bhargav Kanagal"'
Autor:
Li Yang, Qifan Wang, Zac Yu, Anand Kulkarni, Sumit Sanghai, Bin Shu, Jon Elsas, Bhargav Kanagal
Publikováno v:
Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining.
Attribute value extraction refers to the task of identifying values of an attribute of interest from product information. Product attribute values are essential in many e-commerce scenarios, such as customer service robots, product ranking, retrieval
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0c2d203b00a1ebd1c87bb75e850b8532
https://doi.org/10.1145/3488560.3498377
https://doi.org/10.1145/3488560.3498377
Autor:
Ilya Eckstein, Yury Zemlyanskiy, Sudeep Gandhe, Anirudh Ravula, Juraj Gottweis, Bhargav Kanagal, Ruining He, Fei Sha
Publikováno v:
EACL
This paper explores learning rich self-supervised entity representations from large amounts of the associated text. Once pre-trained, these models become applicable to multiple entity-centric tasks such as ranked retrieval, knowledge base completion,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::094c10a9de68867679c891bef703316a
https://doi.org/10.18653/v1/2021.eacl-main.217
https://doi.org/10.18653/v1/2021.eacl-main.217
Autor:
Sumit Sanghai, Bin Shu, Zac Yu, Qifan Wang, Jon Elsas, Bhargav Kanagal, Li Yang, Dandapani Sivakumar
Publikováno v:
KDD
Attribute value extraction refers to the task of identifying values of an attribute of interest from product information. It is an important research topic which has been widely studied in e-Commerce and relation learning. There are two main limitati
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a9706e6daaf531f9b8a8329d2316ecde
https://doi.org/10.1145/3394486.3403047
https://doi.org/10.1145/3394486.3403047
Publikováno v:
ACL/IJCNLP (Findings)
Transformer is the backbone of modern NLP models. In this paper, we propose RealFormer, a simple and generic technique to create Residual Attention Layer Transformer networks that significantly outperform the canonical Transformer and its variants (B
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::939879ffc7bf7e8ce2e742205150eea9
Publikováno v:
CIKM
In this paper, we consider the problem of constructing a comprehensive database of events taking place around the world. Events include small hyper-local events like farmer's markets, neighborhood garage sales, as well as larger concerts and festival
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::27c3a89c770409705a2e000760ab3a74
https://doi.org/10.1145/3357384.3357986
https://doi.org/10.1145/3357384.3357986
Autor:
Bhargav Kanagal, Sandeep Tata
Publikováno v:
ICDE
Recommender systems are a key technology for many online services including e-commerce, movies, music, and news. Online retailers use product recommender systems to help users discover items that they may like. However, building a large-scale product
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6ac6d00d7a7a2bb285d2d0f2c4e41bab
https://doi.org/10.1109/icde.2018.00159
https://doi.org/10.1109/icde.2018.00159
Publikováno v:
WWW
In recent years, interest in recommender research has shifted from explicit feedback towards implicit feedback data. A diversity of complex models has been proposed for a wide variety of applications. Despite this, learning from implicit feedback is
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0a5471f70dd80808d8feee393009b2e5
http://arxiv.org/abs/1611.04666
http://arxiv.org/abs/1611.04666
Publikováno v:
ICDE
Audience selection is a key problem in display advertising systems in which we need to select a list of users who are interested (i.e., most likely to buy) in an advertising campaign. The users' past feedback on this campaign can be leveraged to cons
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::cdb2af51697370b347b04f5e0bc85e8d
https://doi.org/10.1109/icde.2013.6544841
https://doi.org/10.1109/icde.2013.6544841
Recommender systems based on latent factor models have been effectively used for understanding user interests and predicting future actions. Such models work by projecting the users and items into a smaller dimensional space, thereby clustering simil
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::06aa39aa50f4a7844ff37ad3e57f11ee
Autor:
Amol Deshpande, Bhargav Kanagal
Publikováno v:
SIGMOD Conference
In this paper, we address the problem of scalably evaluating conjunctive queries over correlated probabilistic databases containing tuple or attribute uncertainties. Like previous work, we adopt a two-phase approach where we first compute lineages of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eedb7df462a245dc43c051a7f5d121fa
https://doi.org/10.1145/1807167.1807241
https://doi.org/10.1145/1807167.1807241