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Diversification for multi-domain result sets

Alessandro Bozzon, Marco Brambilla, Piero Fraternali, Marco Tagliasacchi
2011 Proceedings of the 20th ACM international conference on Information and knowledge management - CIKM '11  
study for evaluating the utility of diversification in multi-domain queries.  ...  This paper explores two different notions of diversity for multi-domain result sets, compares experimentally alternative algorithms for the trade-off between relevance and diversity, and performs a user  ...  Overall, these results support the hypothesis that diversification algorithms can improve the quality of multi-domain result sets.  ... 
doi:10.1145/2063576.2063851 dblp:conf/cikm/BozzonBFT11 fatcat:rxzikjm7l5bypeforojuk7dvmu

Diversification for Multi-domain Result Sets [chapter]

Alessandro Bozzon, Marco Brambilla, Piero Fraternali, Marco Tagliasacchi
2012 Lecture Notes in Computer Science  
study for evaluating the utility of diversification in multi-domain queries.  ...  This paper explores two different notions of diversity for multi-domain result sets, compares experimentally alternative algorithms for the trade-off between relevance and diversity, and performs a user  ...  Overall, these results support the hypothesis that diversification algorithms can improve the quality of multi-domain result sets.  ... 
doi:10.1007/978-3-642-31753-8_10 fatcat:csabsrc4ejdynmp3leyxjr4eui

UFMG at the TREC 2016 Dynamic Domain track

Felipe Moraes, Rodrygo L. T. Santos, Nivio Ziviani
2016 Text Retrieval Conference  
In particular, based on fine-grained real-time feedback obtained from a simulated user, we apply diversification strategies that make use of single-source as well multi-source information for mining flat  ...  To this end, our participation investigates the suitability of multiple diversification approaches for dynamic information retrieval.  ...  For each document d ∈ D, the user provides the system with a feedback set F.  ... 
dblp:conf/trec/MoraesSZZ16 fatcat:vkuhu7xiwzalzlrbp4si5mg7fu

Explicit Query Interpretation and Diversification for Context-Driven Concept Search Across Ontologies [chapter]

Chetana Gavankar, Yuan-Fang Li, Ganesh Ramakrishnan
2016 Lecture Notes in Computer Science  
of a query; and (3) balances the relevance and diversity of search results.  ...  Many millions of concepts are contained in a large number of ontologies across diverse domains.  ...  Paul Alexander of Stanford University for sharing the BioPortal query log. We would like to thank Prof. Gong Cheng for his advice and guidance on working with Falcons.  ... 
doi:10.1007/978-3-319-46523-4_17 fatcat:ix37tkjh4ncdtfygcul6vxa2ky

Defending against adversarial attacks by randomized diversification [article]

Olga Taran, Shideh Rezaeifar, Taras Holotyak, Slava Voloshynovskiy
2019 arXiv   pre-print
We introduce a multi-channel architecture in a gray-box scenario, which assumes that the architecture of the classifier and the training data set are known to the attacker.  ...  for the attacker.  ...  Classification with multi-channel permutations in the direct domain The simplest case of randomized diversification can be constructed for the direct domain with the permutation of input pixels.  ... 
arXiv:1904.00689v1 fatcat:e6bxnxxf4je5regybarwelae6u

Diversifying Multi-aspect Search Results Using Simpson's Diversity Index

Jianghong Zhou, Eugene Agichtein, Surya Kallumadi
2020 Proceedings of the 29th ACM International Conference on Information & Knowledge Management  
In search and recommendation, diversifying the multi-aspect search results could help with reducing redundancy, and promoting results that might not be shown otherwise.  ...  Many previous methods have been proposed for this task.  ...  CONCLUSIONS In this paper, we presented a novel approach to multi-aspect search results diversification, by adapting the Simpson's Diversity Index (SDI) from biology for this task.  ... 
doi:10.1145/3340531.3412163 dblp:conf/cikm/ZhouAK20 fatcat:fybcyot2f5dzvmxst2ugvzbjye

Issues and Trends of Portfolio Diversification

Aminah Shari, Fauziah Mahat, Ahmed Mohamed Dahir
2019 International Journal of Academic Research in Business and Social Sciences  
In addition, the paper highlight some of the latest trends and developments in the area namely multi-asset class investment and comovement of risk and return.  ...  The purpose of this paper is to examine the issues and trends of diversification of risk on capital market.  ...  Over the past decades, diminishing benefits resulting from diversification has been a hot issue for investors and researchers.  ... 
doi:10.6007/ijarbss/v9-i11/6652 fatcat:3toyjhajtfck3iiqrcfqfmcisu

Study of the Applicability of an Itemset-Based Portfolio Planner in a Multi-Market Context

Luca Cagliero, Paolo Garza
2018 International Conference on Extending Database Technology  
This paper investigates the applicability of itemset-based strategies for planning stock portfolios in a multi-market context.  ...  Furthermore, the performance of multi-market strategies with sector-based diversification is on average superior or comparable to single-market ones.  ...  For all the considered timezones we reported the results achieved by using long selling positions (see Section 4) and by setting the minimum diversification threshold to 70% (i.e., at least seven stocks  ... 
dblp:conf/edbt/CaglieroG18 fatcat:f74ffdyqmffvbgavzmisu22wfe

Exploiting Regression Trees as User Models for Intent-Aware Multi-attribute Diversity

Paolo Tomeo, Tommaso Di Noia, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro, Eugenio Di Sciascio
2015 ACM Conference on Recommender Systems  
The model is based on regression trees and is used to improve personalized diversification of the recommendation list in a multi-attribute setting.  ...  We tested the proposed approach and showed its effectiveness in two different domains, i.e. books and movies.  ...  CONCLUSIONS AND FUTURE WORK This paper addresses the problem of intent-aware diversification in recommender systems in multi-attribute settings.  ... 
dblp:conf/recsys/TomeoNGLSS15 fatcat:xxrnydfq7nag7krgl7zyssub5e

Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection [article]

Taekyung Kim, Minki Jeong, Seunghyeon Kim, Seokeon Choi, Changick Kim
2019 arXiv   pre-print
Our approach is composed of two stages, i.e., Domain Diversification (DD) and Multi-domain-invariant Representation Learning (MRL).  ...  We introduce a novel unsupervised domain adaptation approach for object detection.  ...  Figure 7 . 7 Comparison results for the class-wise AP of Water-color2k test set and Comic2k test set [22] .  ... 
arXiv:1905.05396v1 fatcat:ejwjkxllozbrnjovydl7kk2sjy

Using Domain Knowledge to Boost Case-Based Diagnosis: An Experimental Study in a Domain with Very Poor Data Quality [chapter]

Lu Zhang, Frans Coenen, Paul Leng
2004 Applications and Innovations in Intelligent Systems XI  
Our experimental results show that the domain knowledge can significantly improve the performance of both the non-diversified and the diversified approaches.  ...  The quality of case data can be an important factor for a Case Based Reasoning (CBR) system.  ...  Experimental Results Using Domain Knowledge without Diversification The results of the first experiment on using domain knowledge without diversification are depicted in Fig. 3 .  ... 
doi:10.1007/978-1-4471-0643-2_10 dblp:conf/sgai/0023CL03 fatcat:5rl6dmirijfohhalv4lymc5wiy

Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection

Taekyung Kim, Minki Jeong, Seunghyeon Kim, Seokeon Choi, Changick Kim
2019 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
At the MRL stage, we apply adversarial learning with a multi-domain discriminator to encourage feature to be indistinguishable among the domains.  ...  We introduce a novel unsupervised domain adaptation approach for object detection.  ...  Figure 7 . 7 Comparison results for the class-wise AP of Water-color2k test set and Comic2k test set [22] .  ... 
doi:10.1109/cvpr.2019.01274 dblp:conf/cvpr/KimJKCK19 fatcat:bcdpw7upv5akxh3tql3ux3mh3q

DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images [article]

Onur Tasar, Alain Giros, Yuliya Tarabalka, Pierre Alliez, Sébastien Clerc
2020 arXiv   pre-print
We propose a novel approach, coined DAugNet, for unsupervised, multi-source, multi-target, and life-long domain adaptation of satellite images. It consists of a classifier and a data augmentor.  ...  Most of the existing approaches seek for adapting the model from one domain to another.  ...  In such a multi-source and multi-target segmentation setting, our goal is to generate maps for N t unlabeled target domains.  ... 
arXiv:2005.06216v2 fatcat:zxtbryqz4zgxzn7bx4724wr52e

USEMP: Finding Diverse Images at MediaEval 2015

Eleftherios Spyromitros Xioufis, Adrian Popescu, Symeon Papadopoulos, Ioannis Kompatsiaris
2015 MediaEval Benchmarking Initiative for Multimedia Evaluation  
the multi-topic part.  ...  Our best results in terms of the official ranking metric on the one-topic part of the test set came by the runs that combine visual and textual information while the textual-only run performed better on  ...  This resulted into setting {w = 0.4, N = 210, M = 2} for the one-topic instantiation and {w = 0.4, N = 300, M = 5} for the mutli-topic instantiation.  ... 
dblp:conf/mediaeval/XioufisPPK15 fatcat:cieewjw27vgapd4gpw54mphmqy

DoS

Hina A. Khan, Marina Drosou, Mohamed A. Sharaf
2013 Proceedings of the 25th International Conference on Scientific and Statistical Database Management - SSDBM  
Data diversification provides users with a concise and meaningful view of the results returned by search queries.  ...  The explosion of big data emphasizes the need for data diversification in modern data management platforms, especially for applications based on web, scientific, and business databases.  ...  We would like to thank the anonymous reviewers as well as Abdullah Albarrak for their valuable comments and suggestions.  ... 
doi:10.1145/2484838.2484858 dblp:conf/ssdbm/KhanDS13 fatcat:qwvuv5iro5e6biqmm3i3pdll2y
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