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"Recommendation": models, code, and papers

Improving Scientific Article Visibility by Neural Title Simplification

Apr 05, 2019
Alexander Shvets

The rapidly growing amount of data that scientific content providers should deliver to a user makes them create effective recommendation tools. A title of an article is often the only shown element to attract people's attention. We offer an approach to automatic generating titles with various levels of informativeness to benefit from different categories of users. Statistics from ResearchGate used to bias train datasets and specially designed post-processing step applied to neural sequence-to-sequence models allow reaching the desired variety of simplified titles to gain a trade-off between the attractiveness and transparency of recommendation.

* Contribution to the Proceedings of the 8th International Workshop on Bibliometric-enhanced Information Retrieval (BIR 2019) as part of the 41th European Conference on Information Retrieval (ECIR 2019), Cologne, Germany, April 14, 2019. CEUR Workshop Proceedings, CEUR-WS.org 2019. Keywords: Scientific Text Summarization, Machine Translation, Recommender Systems, Personalized Simplification 

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Position-Based Multiple-Play Bandits with Thompson Sampling

Sep 28, 2020
Camille-Sovanneary Gauthier, Romaric Gaudel, Elisa Fromont

Multiple-play bandits aim at displaying relevant items at relevant positions on a web page. We introduce a new bandit-based algorithm, PB-MHB, for online recommender systems which uses the Thompson sampling framework. This algorithm handles a display setting governed by the position-based model. Our sampling method does not require as input the probability of a user to look at a given position in the web page which is, in practice, very difficult to obtain. Experiments on simulated and real datasets show that our method, with fewer prior information, deliver better recommendations than state-of-the-art algorithms.


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What Factors Should Paper-Reviewer Assignments Rely On? Community Perspectives on Issues and Ideals in Conference Peer-Review

May 03, 2022
Terne Sasha Thorn Jakobsen, Anna Rogers

Both scientific progress and individual researcher careers depend on the quality of peer review, which in turn depends on paper-reviewer matching. Surprisingly, this problem has been mostly approached as an automated recommendation problem rather than as a matter where different stakeholders (area chairs, reviewers, authors) have accumulated experience worth taking into account. We present the results of the first survey of the NLP community, identifying common issues and perspectives on what factors should be considered by paper-reviewer matching systems. This study contributes actionable recommendations for improving future NLP conferences, and desiderata for interpretable peer review assignments.

* NAACL 2022 camera-ready Replacement note: formatting mistake on pages 4-5 

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Energy saving in smart homes based on consumer behaviour: A case study

Sep 18, 2015
Michael Zehnder, Holger Wache, Hans-Friedrich Witschel, Danilo Zanatta, Miguel Rodriguez

This paper presents a case study of a recommender system that can be used to save energy in smart homes without lowering the comfort of the inhabitants. We present an algorithm that uses consumer behavior data only and uses machine learning to suggest actions for inhabitants to reduce the energy consumption of their homes. The system mines for frequent and periodic patterns in the event data provided by the Digitalstrom home automation system. These patterns are converted into association rules, prioritized and compared with the current behavior of the inhabitants. If the system detects an opportunities to save energy without decreasing the comfort level it sends a recommendation to the residents.

* To be presented on IEEE International Smart Cities Conference 2015 

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Freshness-Aware Thompson Sampling

Sep 29, 2014
Djallel Bouneffouf

To follow the dynamicity of the user's content, researchers have recently started to model interactions between users and the Context-Aware Recommender Systems (CARS) as a bandit problem where the system needs to deal with exploration and exploitation dilemma. In this sense, we propose to study the freshness of the user's content in CARS through the bandit problem. We introduce in this paper an algorithm named Freshness-Aware Thompson Sampling (FA-TS) that manages the recommendation of fresh document according to the user's risk of the situation. The intensive evaluation and the detailed analysis of the experimental results reveals several important discoveries in the exploration/exploitation (exr/exp) behaviour.

* 21st International Conference on Neural Information Processing. arXiv admin note: text overlap with arXiv:1409.7729 

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A multinomial probabilistic model for movie genre predictions

Mar 25, 2016
Eric Makita, Artem Lenskiy

This paper proposes a movie genre-prediction based on multinomial probability model. To the best of our knowledge, this problem has not been addressed yet in the field of recommender system. The prediction of a movie genre has many practical applications including complementing the items categories given by experts and providing a surprise effect in the recommendations given to a user. We employ mulitnomial event model to estimate a likelihood of a movie given genre and the Bayes rule to evaluate the posterior probability of a genre given a movie. Experiments with the MovieLens dataset validate our approach. We achieved 70% prediction rate using only 15% of the whole set for training.

* 5 pages, 4 figures, 8th International Conference on Machine Learning and Computing, Hong Kong 

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Analyse et structuration automatique des guides de bonnes pratiques cliniques : essai d'Ć©valuation

Dec 16, 2008
Amanda Bouffier, Thierry Poibeau, Catherine Duclos

Health Practice Guideliens are supposed to unify practices and propose recommendations to physicians. This paper describes GemFrame, a system capable of semi-automatically filling an XML template from free texts in the clinical domain. The XML template includes semantic information not explicitly encoded in the text (pairs of conditions and ac-tions/recommendations). Therefore, there is a need to compute the exact scope of condi-tions over text sequences expressing the re-quired actions. We present a system developped for this task. We show that it yields good performance when applied to the analysis of French practice guidelines. We conclude with a precise evaluation of the tool.


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Deep Reinforcement Learning in Lane Merge Coordination for Connected Vehicles

Oct 20, 2020
Omar Nassef, Luis Sequeira, Elias Salam, Toktam Mahmoodi

In this paper, a framework for lane merge coordination is presented utilising a centralised system, for connected vehicles. The delivery of trajectory recommendations to the connected vehicles on the road is based on a Traffic Orchestrator and a Data Fusion as the main components. Deep Reinforcement Learning and data analysis is used to predict trajectory recommendations for connected vehicles, taking into account unconnected vehicles for those suggestions. The results highlight the adaptability of the Traffic Orchestrator, when employing Dueling Deep Q-Network in an unseen real world merging scenario. A performance comparison of different reinforcement learning models and evaluation against Key Performance Indicator (KPI) are also presented.

* 2020 IEEE 31st Annual International Symposium on Personal, Indoor and Mobile Radio Communications 

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Cosine Similarity of Multimodal Content Vectors for TV Programmes

Sep 23, 2020
Saba Nazir. Taner Cagali, Chris Newell, Mehrnoosh Sadrzadeh

Multimodal information originates from a variety of sources: audiovisual files, textual descriptions, and metadata. We show how one can represent the content encoded by each individual source using vectors, how to combine the vectors via middle and late fusion techniques, and how to compute the semantic similarities between the contents. Our vectorial representations are built from spectral features and Bags of Audio Words, for audio, LSI topics and Doc2vec embeddings for subtitles, and the categorical features, for metadata. We implement our model on a dataset of BBC TV programmes and evaluate the fused representations to provide recommendations. The late fused similarity matrices significantly improve the precision and diversity of recommendations.

* 3 pages, 1 figure, Machine Learning for Media Discovery (ML4MD) Workshop at ICML 2020 

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Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads

Sep 17, 2016
Ji He, Mari Ostendorf, Xiaodong He, Jianshu Chen, Jianfeng Gao, Lihong Li, Li Deng

We introduce an online popularity prediction and tracking task as a benchmark task for reinforcement learning with a combinatorial, natural language action space. A specified number of discussion threads predicted to be popular are recommended, chosen from a fixed window of recent comments to track. Novel deep reinforcement learning architectures are studied for effective modeling of the value function associated with actions comprised of interdependent sub-actions. The proposed model, which represents dependence between sub-actions through a bi-directional LSTM, gives the best performance across different experimental configurations and domains, and it also generalizes well with varying numbers of recommendation requests.

* To be published in EMNLP 2016, 11 pages 

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