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Applying user feedback and query destination learning method to multiple communities - an Evaluation
2011
Transactions of the Japanese society for artificial intelligence
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This paper proposes a novel Peer-to-Peer Information Retrieval (P2PIR) method using user feedback and query-destination-learning. ...
Using query-destination-learning, the method can not only accumulate relevant information from all the member agents in a community, but also reduce communication loads by caching queries and their sender-responder ...
1. 1 Peer-to-Peer (P2P) [ (1) (2) PA 2(N agent − 1) Q D Sim d (Q, D) = T ∈Q w (k 1 + 1)tf K + tf (1) T Q tf D T K = k 1 ((1 − b) + b dl avdl ) K dl avdl D D Q IR agent k 1 , b k 1 = 2.5, b = 0.85 w ...
doi:10.1527/tjsai.26.97
fatcat:vnfg4dawdvdwnjnxwvgspxpdom
LMS: a Long-term knowledge-based Multimedia retrieval System for region-based image databases
2007
International Journal of Applied Systemic Studies
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However, as a CBIR system continues to receive user queries and user feedbacks, the information of user preferences across query sessions are often lost at the end of search, thus requiring the feedback ...
However, in a large system where no pre-defined knowledge from expert is available, we may learn from the users of the system, i.e. through users' queries and their feedbacks on the query results. ...
{Maron and Lozano-Perez, 1998} applied Multiple Instance Learning to natural scene image classification. ...
doi:10.1504/ijass.2007.019304
fatcat:exha6tnryrbkhdyqv4o6end2nu
Integrating relevance feedback techniques for image retrieval using reinforcement learning
2005
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Various integration schemes are presented and a long-term shared memory is used to exploit the retrieval experience from multiple users. ...
Relevance feedback (RF) is an interactive process which refines the retrievals to a particular query by utilizing the user's feedback on previously retrieved results. ...
CONCLUSIONS Most researchers in the relevance learning community strive to develop a new relevance feedback approach. ...
doi:10.1109/tpami.2005.201
pmid:16237990
fatcat:fjeehgr4vrem3ctrv4zxamf374
Towards Active Learning Interfaces for Multi-Inhabitant Activity Recognition
2020
2020 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
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While active learning has been mostly proposed in single-inhabitant settings, several questions arise when such a system has to be implemented in a realistic environment with multiple users. ...
Those methods only require a small training set in order to be initialized, and the model is continuously updated and improved over time. ...
The approach consists of applying natural language processing algorithms to each vocal feedback f provided by the users. ...
doi:10.1109/percomworkshops48775.2020.9156075
dblp:conf/percom/BettiniC20
fatcat:oydg32abyvcfrikjamgc57mbie
Relevance Feedback in CBIR
[chapter]
2002
Visual and Multimedia Information Management
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A new focus in content-based image retrieval (CBIR) research is applying relevance feedback originally developed for text document retrieval, to improve the retrieval performance. ...
In this paper, we present a brief overview of the current stateof-the-art of this topic, and present a framework of integrated relevance feedback and semantic learning in CBIR. ...
The system will refine the query based on the feedback and retrieves a new list of images and presents to user. ...
doi:10.1007/978-0-387-35592-4_3
fatcat:s56mbelgvzgvfcxhjlbnldkwma
Learning to Reinforce Search Effectiveness
2015
Proceedings of the 2015 International Conference on Theory of Information Retrieval - ICTIR '15
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In this paper, we propose a novel reinforcement learning style information retrieval framework and develop a new feedback learning algorithm to model user feedback, including clicks and query reformulations ...
The algorithm infers user feedback models by an EM algorithm from the query logs. ...
The research is supported by DARPA FA8750-14-2-0226, NSF IIS-1453721, and NSF CNS-1223825. ...
doi:10.1145/2808194.2809468
dblp:conf/ictir/LuoDY15a
fatcat:7ui67ulsojhmpo6v4ipfotunwq
Online Learning to Rank for Information Retrieval
2016
Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16
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Such methods learn from user interactions rather than from a set of labeled data that is fully available for training up front. ...
Recently, as the limitations of offline learning to rank for information retrieval have become apparent, there is increased attention for online learning to rank methods for information retrieval in the ...
When learning from user interactions, a system has no control over which queries it receives, it only receives feedback on the result lists it presents to users, and it has to present high quality result ...
doi:10.1145/2911451.2914798
dblp:conf/sigir/GrotovR16
fatcat:oygfkqjorvcfjke2ugj4blhe3i
Learning to Rank from Relevance Feedback for e-Discovery
[chapter]
2012
Lecture Notes in Computer Science
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We present the results of applying a learning to rank algorithm to the 2011 TREC Legal dataset. The learning to rank algorithm we use was designed to maximize NDCG, MAP, and AUC scores. ...
We find query expansion and learning to rank improve scores beyond standard language model retrieval, however learning to rank does not outperform query expansion. ...
Introduction Our approach begins with language modeling and then, as feedback from the user is received, we combine relevance feedback and learning to rank on the query level to improve result rankings ...
doi:10.1007/978-3-642-28997-2_58
fatcat:vanppbe4azfzdb2p4yc2ofigly
Adaptive multiple feedback strategies for interactive video search
2008
Proceedings of the 2008 international conference on Content-based image and video retrieval - CIVR '08
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To cater to the large number of novice users (non-expert users), an adaptive option is built-in to learn the expert user behavior so as to provide recommendations on the next feedback strategy, leading ...
with more flexibility can be performed to cover different search queries and different video corpuses. ...
.$5.00. example: "query point movement" [6] relevance feedback). Due to the complexity and variety of multi-modal features in videos, it is usually insufficient to apply a single feedback method. ...
doi:10.1145/1386352.1386411
dblp:conf/civr/LuanZNZLC08
fatcat:vs45pml57nc7lk2aufxpywcbyu
Kernel-based distance metric learning for content-based image retrieval
2007
Image and Vision Computing
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learning methods significantly due to its higher flexibility in metric learning. ...
Unlike most existing metric learning methods which learn a Mahalanobis metric corresponding to performing linear transformation in the original image space, we define the transformation in * Corresponding ...
Acknowledgments The research described in this paper has been supported by two grants, CA03/04.EG01 (which is part of HKBU2/03/C) and HKUST6174/04E, from the Research Grants Council of the Hong Kong Special ...
doi:10.1016/j.imavis.2006.05.013
fatcat:6cd3utk6tjgqhomwh5cywvuhnq
Interactive search in image retrieval: a survey
2012
International Journal of Multimedia Information Retrieval
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We highlight trends and ideas from over 170 recent research papers aiming to capture the wide spectrum of paradigms and methods in interactive search, including its subarea relevance feedback. ...
Furthermore, we identify promising research directions and several grand challenges for the future. ...
and the source are credited. ...
doi:10.1007/s13735-012-0014-4
fatcat:gm5qzupaivcdbao437fdn6l54i
A Fuzzy Logic based Recommender System for E-Learning System with Multi-Agent Framework
2015
International Journal of Computer Applications
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A fuzzy logic based recommender agent framework is used to give further suggestions to learner to increase his/her satisfaction and provide enhanced and personalized learning experience. ...
In this paper a multi agent based e-learning framework is proposed which is able to provide a personalized experience to the learner by recommending him study material according to his requirements, goals ...
Interface agent:interface agent communicates the user query tothe task agent, and filters the retrieved documents from task agent in order to give the user thoseones that better satisfy his/her needs. ...
doi:10.5120/21793-5140
fatcat:h2eafsaxszeyvpig5e4hebrlkm
Energy beamforming with one-bit feedback
2014
2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
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To tackle this problem, we consider in this paper a multiuser MIMO WET system, and propose a new channel learning method that requires only one feedback bit from each ER to the ET per feedback interval ...
This task is particularly challenging, since existing channel training and feedback methods used for communication receivers may not be implementable at the energy receiver (ER) due to its hardware limitation ...
[24] considered a point-to-point multiple-input single-output (MISO) transmit beamforming system in wireless communication (instead of WET), where ACCPM is applied to estimate the single-user MISO channel ...
doi:10.1109/icassp.2014.6854254
dblp:conf/icassp/XuZ14
fatcat:aafaovooqvf37l7e2x33v7gsxm
Energy Beamforming With One-Bit Feedback
2014
IEEE Transactions on Signal Processing
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This task is particularly challenging for WET systems, since existing channel training and feedback methods used for communication receivers may not be implementable at the energy receiver (ER) due to ...
By taking into account the practical energy harvesting circuits at the ER, we propose a new channel learning method that requires only one feedback bit from each ER to the ET per feedback interval. ...
[24] considered a point-to-point multiple-input single-output (MISO) transmit beamforming system in wireless communication (instead of WET), where ACCPM is applied to estimate the single-user MISO channel ...
doi:10.1109/tsp.2014.2352604
fatcat:bk3w2pvpfzh5nm3h552n2h5jty
Multi-agent learning approach to WWW information retrieval using neural network
1999
Proceedings of the 4th international conference on Intelligent user interfaces - IUI '99
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In this paper, we propose a multi-agent learning approach to information retrieval on the World Wide Web where each agent collaboratively learns its environment from user's relevance feedback using a neural ...
communication channels Results obtained by evaluating the entire set of 50 test queries for various dimensions of the training set are shown in Fig. 7, 8, and 9, where the average values of the results ...
After the above IR subprocedure is done, the relevance feedback mechanism asks the user to mark the pieces of information judged as relevant to his/her query and then extracts the information about the ...
doi:10.1145/291080.291086
dblp:conf/iui/ChoiY99
fatcat:ce4g2fcdjvdn7igczlna74yfry
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