45,619 research outputs found
Reinforcement learning for combining relevance feedback techniques
Relevance feedback (RF) is an interactive process which refines the retrievals by utilizing user’s feedback history. Most researchers strive to develop new RF techniques and ignore the advantages of existing ones. In this paper, we propose an image relevance reinforcement learning (IRRL) model for integrating existing RF techniques. Various integration schemes are presented and a long-term shared memory is used to exploit the retrieval experience from multiple users. Also, a concept digesting method is proposed to reduce the complexity of storage demand. The experimental results manifest that the integration of multiple RF approaches gives better retrieval performance than using one RF technique alone, and that the sharing of relevance knowledge between multiple query sessions also provides significant contributions for improvement. Further, the storage demand is significantly reduced by the concept digesting technique. This shows the scalability of the proposed model against a growing-size database
A Reinforcement Learning-driven Translation Model for Search-Oriented Conversational Systems
Search-oriented conversational systems rely on information needs expressed in
natural language (NL). We focus here on the understanding of NL expressions for
building keyword-based queries. We propose a reinforcement-learning-driven
translation model framework able to 1) learn the translation from NL
expressions to queries in a supervised way, and, 2) to overcome the lack of
large-scale dataset by framing the translation model as a word selection
approach and injecting relevance feedback in the learning process. Experiments
are carried out on two TREC datasets and outline the effectiveness of our
approach.Comment: This is the author's pre-print version of the work. It is posted here
for your personal use, not for redistribution. Please cite the definitive
version which will be published in Proceedings of the 2018 EMNLP Workshop
SCAI: The 2nd International Workshop on Search-Oriented Conversational AI -
ISBN: 978-1-948087-75-
IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models
This paper provides a unified account of two schools of thinking in
information retrieval modelling: the generative retrieval focusing on
predicting relevant documents given a query, and the discriminative retrieval
focusing on predicting relevancy given a query-document pair. We propose a game
theoretical minimax game to iteratively optimise both models. On one hand, the
discriminative model, aiming to mine signals from labelled and unlabelled data,
provides guidance to train the generative model towards fitting the underlying
relevance distribution over documents given the query. On the other hand, the
generative model, acting as an attacker to the current discriminative model,
generates difficult examples for the discriminative model in an adversarial way
by minimising its discrimination objective. With the competition between these
two models, we show that the unified framework takes advantage of both schools
of thinking: (i) the generative model learns to fit the relevance distribution
over documents via the signals from the discriminative model, and (ii) the
discriminative model is able to exploit the unlabelled data selected by the
generative model to achieve a better estimation for document ranking. Our
experimental results have demonstrated significant performance gains as much as
23.96% on Precision@5 and 15.50% on MAP over strong baselines in a variety of
applications including web search, item recommendation, and question answering.Comment: 12 pages; appendix adde
Deep Learning based Recommender System: A Survey and New Perspectives
With the ever-growing volume of online information, recommender systems have
been an effective strategy to overcome such information overload. The utility
of recommender systems cannot be overstated, given its widespread adoption in
many web applications, along with its potential impact to ameliorate many
problems related to over-choice. In recent years, deep learning has garnered
considerable interest in many research fields such as computer vision and
natural language processing, owing not only to stellar performance but also the
attractive property of learning feature representations from scratch. The
influence of deep learning is also pervasive, recently demonstrating its
effectiveness when applied to information retrieval and recommender systems
research. Evidently, the field of deep learning in recommender system is
flourishing. This article aims to provide a comprehensive review of recent
research efforts on deep learning based recommender systems. More concretely,
we provide and devise a taxonomy of deep learning based recommendation models,
along with providing a comprehensive summary of the state-of-the-art. Finally,
we expand on current trends and provide new perspectives pertaining to this new
exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys.
https://doi.acm.org/10.1145/328502
Combining Experience Replay with Exploration by Random Network Distillation
Our work is a simple extension of the paper "Exploration by Random Network
Distillation". More in detail, we show how to efficiently combine Intrinsic
Rewards with Experience Replay in order to achieve more efficient and robust
exploration (with respect to PPO/RND) and consequently better results in terms
of agent performances and sample efficiency. We are able to do it by using a
new technique named Prioritized Oversampled Experience Replay (POER), that has
been built upon the definition of what is the important experience useful to
replay. Finally, we evaluate our technique on the famous Atari game Montezuma's
Revenge and some other hard exploration Atari games.Comment: 8 pages, 6 figures, accepted as full-paper at IEEE Conference on
Games (CoG) 201
NPRF: A Neural Pseudo Relevance Feedback Framework for Ad-hoc Information Retrieval
Pseudo-relevance feedback (PRF) is commonly used to boost the performance of
traditional information retrieval (IR) models by using top-ranked documents to
identify and weight new query terms, thereby reducing the effect of
query-document vocabulary mismatches. While neural retrieval models have
recently demonstrated strong results for ad-hoc retrieval, combining them with
PRF is not straightforward due to incompatibilities between existing PRF
approaches and neural architectures. To bridge this gap, we propose an
end-to-end neural PRF framework that can be used with existing neural IR models
by embedding different neural models as building blocks. Extensive experiments
on two standard test collections confirm the effectiveness of the proposed NPRF
framework in improving the performance of two state-of-the-art neural IR
models.Comment: Full paper in EMNLP 201
Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous Integration
Testing in Continuous Integration (CI) involves test case prioritization,
selection, and execution at each cycle. Selecting the most promising test cases
to detect bugs is hard if there are uncertainties on the impact of committed
code changes or, if traceability links between code and tests are not
available. This paper introduces Retecs, a new method for automatically
learning test case selection and prioritization in CI with the goal to minimize
the round-trip time between code commits and developer feedback on failed test
cases. The Retecs method uses reinforcement learning to select and prioritize
test cases according to their duration, previous last execution and failure
history. In a constantly changing environment, where new test cases are created
and obsolete test cases are deleted, the Retecs method learns to prioritize
error-prone test cases higher under guidance of a reward function and by
observing previous CI cycles. By applying Retecs on data extracted from three
industrial case studies, we show for the first time that reinforcement learning
enables fruitful automatic adaptive test case selection and prioritization in
CI and regression testing.Comment: Spieker, H., Gotlieb, A., Marijan, D., & Mossige, M. (2017).
Reinforcement Learning for Automatic Test Case Prioritization and Selection
in Continuous Integration. In Proceedings of 26th International Symposium on
Software Testing and Analysis (ISSTA'17) (pp. 12--22). AC
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