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pro vyhledávání: '"Parambath, Shameem A Puthiya"'
We propose an algorithm for next query recommendation in interactive data exploration settings, like knowledge discovery for information gathering. The state-of-the-art query recommendation algorithms are based on sequence-to-sequence learning approa
Externí odkaz:
http://arxiv.org/abs/2205.06297
Autor:
Parambath, Shameem A. Puthiya, Anagnostopoulos, Christos, Murray-Smith, Roderick, MacAvaney, Sean, Zervas, Evangelos
Publikováno v:
Asian Conference on Machine Learning 2021
We consider the query recommendation problem in closed loop interactive learning settings like online information gathering and exploratory analytics. The problem can be naturally modelled using the Multi-Armed Bandits (MAB) framework with countably
Externí odkaz:
http://arxiv.org/abs/2108.13810
Autor:
Parambath, Shameem A Puthiya
Publikováno v:
Proceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval
We analyze different re-ranking algorithms for diversification and show that majority of them are based on maximizing submodular/modular functions from the class of parameterized concave/linear over modular functions. We study the optimality of such
Externí odkaz:
http://arxiv.org/abs/1906.11285
Given an incomplete ratings data over a set of users and items, the preference completion problem aims to estimate a personalized total preference order over a subset of the items. In practical settings, a ranked list of top-$k$ items from the estima
Externí odkaz:
http://arxiv.org/abs/1904.05325
Akademický článek
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Autor:
Thirumuruganathan, Saravanan, Parambath, Shameem A Puthiya, Ouzzani, Mourad, Tang, Nan, Joty, Shafiq
Entity resolution (ER) is one of the fundamental problems in data integration, where machine learning (ML) based classifiers often provide the state-of-the-art results. Considerable human effort goes into feature engineering and training data creatio
Externí odkaz:
http://arxiv.org/abs/1809.11084
In this paper, we propose a unified framework and an algorithm for the problem of group recommendation where a fixed number of items or alternatives can be recommended to a group of users. The problem of group recommendation arises naturally in many
Externí odkaz:
http://arxiv.org/abs/1712.09123
Non-linear performance measures are widely used for the evaluation of learning algorithms. For example, $F$-measure is a commonly used performance measure for classification problems in machine learning and information retrieval community. We study t
Externí odkaz:
http://arxiv.org/abs/1505.00199
Autor:
Parambath, Shameem A Puthiya
Automatic classification of scientific articles based on common characteristics is an interesting problem with many applications in digital library and information retrieval systems. Properly organized articles can be useful for automatic generation
Externí odkaz:
http://arxiv.org/abs/1212.5423
Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 32
In this paper, we propose a unified framework and an algorithm for the problem of group recommendation where a fixed number of items or alternatives can be recommended to a group of users. The problem of group recommendation arises naturally in many
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::faaffd9deb845dce9b276813fd36d4df
https://doi.org/10.1609/aaai.v32i1.11650
https://doi.org/10.1609/aaai.v32i1.11650
