Relevant bibliographies by topics / Large Scale Recommendation

Academic literature on the topic 'Large Scale Recommendation'

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Published: 1 June 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Journal articles on the topic "Large Scale Recommendation":

1

Laddha, Abhishek, Mohamed Hanoosh, Debdoot Mukherjee, Parth Patwa, and Ankur Narang. "Large Scale Multilingual Sticker Recommendation In Messaging Apps." AI Magazine 42, no. 4 (January 12, 2022): 16–28. http://dx.doi.org/10.1609/aimag.v42i4.15098.

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Stickers are popularly used while messaging to visually express nuanced thoughts. We describe a real-time sticker recommendation (SR) system. We decompose SR into two steps: predict the message that is likely to be sent, and substitute that message with an appropriate sticker. To address the challenges caused by transliteration of message from users’ native language to the Roman script, we learn message embeddings by employing character-level CNN in an unsupervised manner. We use them to cluster semantically similar messages. Next, we predict the message cluster instead of the message. Except for validation, our system does not require human labeled data, leading to a fully auto-matic tuning pipeline. We propose a hybrid message prediction model, which can easily run on low-end phones. We discuss message cluster to sticker mapping, addressing the multilingual needs of our users, automated tuning of the system and also propose a novel application of community detection algorithm. As of November 2020, our system contains 100k+ stickers, has been deployed for 15+ months, and is being used by millions of users.
2

Zhou, Wang, Yongluan Zhou, Jianping Li, and Muhammad Hammad Memon. "LsRec: Large-scale social recommendation with online update." Expert Systems with Applications 162 (December 2020): 113739. http://dx.doi.org/10.1016/j.eswa.2020.113739.

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3

Sakhi, Otmane, David Rohde, and Alexandre Gilotte. "Fast Offline Policy Optimization for Large Scale Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (June 26, 2023): 9686–94. http://dx.doi.org/10.1609/aaai.v37i8.26158.

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Personalised interactive systems such as recommender systems require selecting relevant items from massive catalogs dependent on context. Reward-driven offline optimisation of these systems can be achieved by a relaxation of the discrete problem resulting in policy learning or REINFORCE style learning algorithms. Unfortunately, this relaxation step requires computing a sum over the entire catalogue making the complexity of the evaluation of the gradient (and hence each stochastic gradient descent iterations) linear in the catalogue size. This calculation is untenable in many real world examples such as large catalogue recommender systems, severely limiting the usefulness of this method in practice. In this paper, we derive an approximation of these policy learning algorithms that scale logarithmically with the catalogue size. Our contribution is based upon combining three novel ideas: a new Monte Carlo estimate of the gradient of a policy, the self normalised importance sampling estimator and the use of fast maximum inner product search at training time. Extensive experiments show that our algorithm is an order of magnitude faster than naive approaches yet produces equally good policies.
4

Laddha, Abhishek, Mohamed Hanoosh, Debdoot Mukherjee, Parth Patwa, and Ankur Narang. "Large Scale Multilingual Sticker Recommendation In Messaging Apps." AI Magazine 42, no. 4 (January 18, 2022): 16–28. http://dx.doi.org/10.1609/aaai.12023.

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Stickers are popularly used while messaging to visually express nuanced thoughts. We describe a real-time sticker recommendation (SR) system. We decompose SR into two steps: predict the message that is likely to be sent, and substitute that message with an appropriate sticker. To address the challenges caused by transliteration of message from users’ native language to the Roman script, we learn message embeddings by employing character-level CNN in an unsupervised manner. We use them to cluster semantically similar messages. Next, we predict the message cluster instead of the message. Except for validation, our system does not require human labeled data, leading to a fully auto-matic tuning pipeline. We propose a hybrid message prediction model, which can easily run on low-end phones. We discuss message cluster to sticker mapping, addressing the multilingual needs of our users, automated tuning of the system and also propose a novel application of community detection algorithm. As of November 2020, our system contains 100k+ stickers, has been deployed for 15+ months, and is being used by millions of users.
5

Liu, Yang, Cheng Lyu, Zhiyuan Liu, and Jinde Cao. "Exploring a large-scale multi-modal transportation recommendation system." Transportation Research Part C: Emerging Technologies 126 (May 2021): 103070. http://dx.doi.org/10.1016/j.trc.2021.103070.

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E, HaiHong, JianFeng WANG, MeiNa SONG, Qiang BI, and YingYi LIU. "Incremental weighted bipartite algorithm for large-scale recommendation systems." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 24 (2016): 448–63. http://dx.doi.org/10.3906/elk-1307-91.

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Chen, Haokun, Xinyi Dai, Han Cai, Weinan Zhang, Xuejian Wang, Ruiming Tang, Yuzhou Zhang, and Yong Yu. "Large-Scale Interactive Recommendation with Tree-Structured Policy Gradient." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3312–20. http://dx.doi.org/10.1609/aaai.v33i01.33013312.

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Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for long-run performance. As IRS is always with thousands of items to recommend (i.e., thousands of actions), most existing RL-based methods, however, fail to handle such a large discrete action space problem and thus become inefficient. The existing work that tries to deal with the large discrete action space problem by utilizing the deep deterministic policy gradient framework suffers from the inconsistency between the continuous action representation (the output of the actor network) and the real discrete action. To avoid such inconsistency and achieve high efficiency and recommendation effectiveness, in this paper, we propose a Tree-structured Policy Gradient Recommendation (TPGR) framework, where a balanced hierarchical clustering tree is built over the items and picking an item is formulated as seeking a path from the root to a certain leaf of the tree. Extensive experiments on carefully-designed environments based on two real-world datasets demonstrate that our model provides superior recommendation performance and significant efficiency improvement over state-of-the-art methods.
8

HASHIMOTO, T. "Recommendation for Large Scale Intervention Study on Industrial Population." Sangyo Igaku 34, no. 4 (1992): 309. http://dx.doi.org/10.1539/joh1959.34.309.

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Khan, Muhammad Usman Shahid, Osman Khalid, Ying Huang, Rajiv Ranjan, Fan Zhang, Junwei Cao, Bharadwaj Veeravalli, Samee U. Khan, Keqin Li, and Albert Y. Zomaya. "MacroServ: A Route Recommendation Service for Large-Scale Evacuations." IEEE Transactions on Services Computing 10, no. 4 (July 1, 2017): 589–602. http://dx.doi.org/10.1109/tsc.2015.2497241.

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Bathla, Gourav, Himanshu Aggarwal, and Rinkle Rani. "Scalable Recommendation Using Large Scale Graph Partitioning With Pregel and Giraph." International Journal of Cognitive Informatics and Natural Intelligence 14, no. 4 (October 2020): 42–61. http://dx.doi.org/10.4018/ijcini.2020100103.

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Social Big Data is generated by interactions of connected users on social network. Sharing of opinions and contents amongst users, reviews of users for products, result in social Big Data. If any user intends to select products such as movies, books, etc., from e-commerce sites or view any topic or opinion on social networking sites, there are a lot of options and these options result in information overload. Social recommendation systems assist users to make better selection as per their likings. Recent research works have improved recommendation systems by using matrix factorization, social regularization or social trust inference. Furthermore, these improved systems are able to alleviate cold start and sparsity, but not efficient for scalability. The main focus of this article is to improve scalability in terms of locality and throughput and provides better recommendations to users with large-scale data in less response time. In this article, the social big graph is partitioned and distributed on different nodes based on Pregel and Giraph. In the proposed approach ScaleRec, partitioning is based on direct as well as indirect trust between users and comparison with state-of-the-art approaches proves that statistically better partitioning quality is achieved using proposed approach. In ScaleRec, hyperedge and transitive closure are used to enhance social trust amongst users. Experiment analysis on standard datasets such as Epinions and LiveJournal proves that better locality and recommendation accuracy is achieved by using ScaleRec.
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Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Large Scale Recommendation":

1

Nilsen, John Eirik Bjørhovde. "Large-Scale User Click Analysis in News Recommendation." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-23004.

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The Internet and the World Wide Web have taken over as the standard reading and finding news. This makes it possible for news readers to carefully choose the news that is most interesting for them. Due to the large amounts of articles, it can be a challenging and time consuming task to find the wanted information. Simplifying this process for the news readers would be beneficial.This thesis explores the idea of filtering out unwanted news articles and serving the useful ones to the reader through mobile platforms. It is part of a bigger project named SmartMedia that focuses on using complex strategies for delivering news to the users. While the overall strategy is based on using the total context of users to serve new, the specific scope of this thesis is creating user profiles from user acts logged by the system. The motivation is to utilize these profiles in cooperation with information filtering techniques to help reach the overall goal.A big part of this thesis focuses on implementing Hadoop jobs that summarizes the user logs into profiles. In the solution, each user profile consists of two vectors. A category vector that describes the user?s interests in the different news categories and a keyword vector that exploits entities defined in news articles to analyse at a low granularity level. The results are evaluated and discussed at the end.How to evaluate the effectiveness and accuracy of the user profiles is difficult. Little real data was available during this research and actual data is needed. Data that replicates real users is hard to forge and is needed for both evaluation and calibration of the implementation. Thus, the focus of the discussion is on how to perform these two tasks when the system is deployed.
2

Larsson, Carl-Johan. "Movie Recommendation System Using Large Scale Graph-Processing." Thesis, KTH, Skolan för elektro- och systemteknik (EES), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-200601.

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3

Sakhi, Otmane. "Offline Contextual Bandit : Theory and Large Scale Applications." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAG011.

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Cette thèse s'intéresse au problème de l'apprentissage à partir d'interactions en utilisant le cadre du bandit contextuel hors ligne. En particulier, nous nous intéressons à deux sujets connexes : (1) l'apprentissage de politiques hors ligne avec des certificats de performance, et (2) l'apprentissage rapide et efficace de politiques, pour le problème de recommandation à grande échelle. Pour (1), nous tirons d'abord parti des résultats du cadre d'optimisation distributionnellement robuste pour construire des bornes asymptotiques, sensibles à la variance, qui permettent l'évaluation des performances des politiques. Ces bornes nous aident à obtenir de nouveaux objectifs d'apprentissage plus pratiques grâce à leur nature composite et à leur calibrage simple. Nous analysons ensuite le problème d'un point de vue PAC-Bayésien et fournissons des bornes, plus étroites, sur les performances des politiques. Nos résultats motivent de nouvelles stratégies, qui offrent des certificats de performance sur nos politiques avant de les déployer en ligne. Les stratégies nouvellement dérivées s'appuient sur des objectifs d'apprentissage composites qui ne nécessitent pas de réglage supplémentaire. Pour (2), nous proposons d'abord un modèle bayésien hiérarchique, qui combine différents signaux, pour estimer efficacement la qualité de la recommandation. Nous fournissons les outils computationnels appropriés pour adapter l'inférence aux problèmes à grande échelle et démontrons empiriquement les avantages de l'approche dans plusieurs scénarios. Nous abordons ensuite la question de l'accélération des approches communes d'optimisation des politiques, en nous concentrant particulièrement sur les problèmes de recommandation avec des catalogues de millions de produits. Nous dérivons des méthodes d'optimisation, basées sur de nouvelles approximations du gradient calculées en temps logarithmique par rapport à la taille du catalogue. Notre approche améliore le temps linéaire des méthodes courantes de calcul de gradient, et permet un apprentissage rapide sans nuire à la qualité des politiques obtenues
This thesis presents contributions to the problem of learning from logged interactions using the offline contextual bandit framework. We are interested in two related topics: (1) offline policy learning with performance certificates, and (2) fast and efficient policy learning applied to large scale, real world recommendation. For (1), we first leverage results from the distributionally robust optimisation framework to construct asymptotic, variance-sensitive bounds to evaluate policies' performances. These bounds lead to new, more practical learning objectives thanks to their composite nature and straightforward calibration. We then analyse the problem from the PAC-Bayesian perspective, and provide tighter, non-asymptotic bounds on the performance of policies. Our results motivate new strategies, that offer performance certificates before deploying the policies online. The newly derived strategies rely on composite learning objectives that do not require additional tuning. For (2), we first propose a hierarchical Bayesian model, that combines different signals, to efficiently estimate the quality of recommendation. We provide proper computational tools to scale the inference to real world problems, and demonstrate empirically the benefits of the approach in multiple scenarios. We then address the question of accelerating common policy optimisation approaches, particularly focusing on recommendation problems with catalogues of millions of items. We derive optimisation routines, based on new gradient approximations, computed in logarithmic time with respect to the catalogue size. Our approach improves on common, linear time gradient computations, yielding fast optimisation with no loss on the quality of the learned policies
4

Safran, Mejdl Sultan. "EFFICIENT LEARNING-BASED RECOMMENDATION ALGORITHMS FOR TOP-N TASKS AND TOP-N WORKERS IN LARGE-SCALE CROWDSOURCING SYSTEMS." OpenSIUC, 2018. https://opensiuc.lib.siu.edu/dissertations/1511.

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A pressing need for efficient personalized recommendations has emerged in crowdsourcing systems. On the one hand, workers confront a flood of tasks, and they often spend too much time to find tasks matching their skills and interests. Thus, workers want effective recommendation of the most suitable tasks with regard to their skills and preferences. On the other hand, requesters sometimes receive results in low-quality completion since a less qualified worker may start working on a task before a better-skilled worker may get hands on. Thus, requesters want reliable recommendation of the best workers for their tasks in terms of workers' qualifications and accountability. The task and worker recommendation problems in crowdsourcing systems have brought up unique characteristics that are not present in traditional recommendation scenarios, i.e., the huge flow of tasks with short lifespans, the importance of workers' capabilities, and the quality of the completed tasks. These unique features make traditional recommendation approaches (mostly developed for e-commerce markets) no longer satisfactory for task and worker recommendation in crowdsourcing systems. In this research, we reveal our insight into the essential difference between the tasks in crowdsourcing systems and the products/items in e-commerce markets, and the difference between buyers' interests in products/items and workers' interests in tasks. Our insight inspires us to bring up categories as a key mediation mechanism between workers and tasks. We propose a two-tier data representation scheme (defining a worker-category suitability score and a worker-task attractiveness score) to support personalized task and worker recommendation. We also extend two optimization methods, namely least mean square error (LMS) and Bayesian personalized rank (BPR) in order to better fit the characteristics of task/worker recommendation in crowdsourcing systems. We then integrate the proposed representation scheme and the extended optimization methods along with the two adapted popular learning models, i.e., matrix factorization and kNN, and result in two lines of top-N recommendation algorithms for crowdsourcing systems: (1) Top-N-Tasks (TNT) recommendation algorithms for discovering the top-N most suitable tasks for a given worker, and (2) Top-N-Workers (TNW) recommendation algorithms for identifying the top-N best workers for a task requester. An extensive experimental study is conducted that validates the effectiveness and efficiency of a broad spectrum of algorithms, accompanied by our analysis and the insights gained.
5

Yang, Dingqi. "Understanding human dynamics from large-scale location-centric social media data : analysis and applications." Thesis, Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0002/document.

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La dynamique humaine est un sujet essentiel de l'informatique centrée sur l’homme. Elle se concentre sur la compréhension des régularités sous-jacentes, des relations, et des changements dans les comportements humains. En analysant la dynamique humaine, nous pouvons comprendre non seulement des comportements individuels, tels que la présence d’une personne à un endroit précis, mais aussi des comportements collectifs, comme les mouvements sociaux. L’exploration de la dynamique humaine permet ainsi diverses applications, entre autres celles des services géo-dépendants personnalisés dans des scénarios de ville intelligente. Avec l'omniprésence des smartphones équipés de GPS, les réseaux sociaux de géolocalisation ont acquis une popularité croissante au cours des dernières années, ce qui rend les données de comportements des utilisateurs disponibles à grande échelle. Sur les dits réseaux sociaux de géolocalisation, les utilisateurs peuvent partager leurs activités en temps réel avec par l'enregistrement de leur présence à des points d'intérêt (POIs), tels qu’un restaurant. Ces données d'activité contiennent des informations massives sur la dynamique humaine. Dans cette thèse, nous explorons la dynamique humaine basée sur les données massives des réseaux sociaux de géolocalisation. Concrètement, du point de vue individuel, nous étudions la préférence de l'utilisateur quant aux POIs avec des granularités différentes et ses applications, ainsi que la régularité spatio-temporelle des activités des utilisateurs. Du point de vue collectif, nous explorons la forme d'activité collective avec les granularités de pays et ville, ainsi qu’en corrélation avec les cultures globales
Human dynamics is an essential aspect of human centric computing. As a transdisciplinary research field, it focuses on understanding the underlying patterns, relationships, and changes of human behavior. By exploring human dynamics, we can understand not only individual’s behavior, such as a presence at a specific place, but also collective behaviors, such as social movement. Understanding human dynamics can thus enable various applications, such as personalized location based services. However, before the availability of ubiquitous smart devices (e.g., smartphones), it is practically hard to collect large-scale human behavior data. With the ubiquity of GPS-equipped smart phones, location based social media has gained increasing popularity in recent years, making large-scale user activity data become attainable. Via location based social media, users can share their activities as real-time presences at Points of Interests (POIs), such as a restaurant or a bar, within their social circles. Such data brings an unprecedented opportunity to study human dynamics. In this dissertation, based on large-scale location centric social media data, we study human dynamics from both individual and collective perspectives. From individual perspective, we study user preference on POIs with different granularities and its applications in personalized location based services, as well as the spatial-temporal regularity of user activities. From collective perspective, we explore the global scale collective activity patterns with both country and city granularities, and also identify their correlations with diverse human cultures
6

Yang, Dingqi. "Understanding human dynamics from large-scale location-centric social media data : analysis and applications." Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0002.

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La dynamique humaine est un sujet essentiel de l'informatique centrée sur l’homme. Elle se concentre sur la compréhension des régularités sous-jacentes, des relations, et des changements dans les comportements humains. En analysant la dynamique humaine, nous pouvons comprendre non seulement des comportements individuels, tels que la présence d’une personne à un endroit précis, mais aussi des comportements collectifs, comme les mouvements sociaux. L’exploration de la dynamique humaine permet ainsi diverses applications, entre autres celles des services géo-dépendants personnalisés dans des scénarios de ville intelligente. Avec l'omniprésence des smartphones équipés de GPS, les réseaux sociaux de géolocalisation ont acquis une popularité croissante au cours des dernières années, ce qui rend les données de comportements des utilisateurs disponibles à grande échelle. Sur les dits réseaux sociaux de géolocalisation, les utilisateurs peuvent partager leurs activités en temps réel avec par l'enregistrement de leur présence à des points d'intérêt (POIs), tels qu’un restaurant. Ces données d'activité contiennent des informations massives sur la dynamique humaine. Dans cette thèse, nous explorons la dynamique humaine basée sur les données massives des réseaux sociaux de géolocalisation. Concrètement, du point de vue individuel, nous étudions la préférence de l'utilisateur quant aux POIs avec des granularités différentes et ses applications, ainsi que la régularité spatio-temporelle des activités des utilisateurs. Du point de vue collectif, nous explorons la forme d'activité collective avec les granularités de pays et ville, ainsi qu’en corrélation avec les cultures globales
Human dynamics is an essential aspect of human centric computing. As a transdisciplinary research field, it focuses on understanding the underlying patterns, relationships, and changes of human behavior. By exploring human dynamics, we can understand not only individual’s behavior, such as a presence at a specific place, but also collective behaviors, such as social movement. Understanding human dynamics can thus enable various applications, such as personalized location based services. However, before the availability of ubiquitous smart devices (e.g., smartphones), it is practically hard to collect large-scale human behavior data. With the ubiquity of GPS-equipped smart phones, location based social media has gained increasing popularity in recent years, making large-scale user activity data become attainable. Via location based social media, users can share their activities as real-time presences at Points of Interests (POIs), such as a restaurant or a bar, within their social circles. Such data brings an unprecedented opportunity to study human dynamics. In this dissertation, based on large-scale location centric social media data, we study human dynamics from both individual and collective perspectives. From individual perspective, we study user preference on POIs with different granularities and its applications in personalized location based services, as well as the spatial-temporal regularity of user activities. From collective perspective, we explore the global scale collective activity patterns with both country and city granularities, and also identify their correlations with diverse human cultures
7

Östlin, Erik. "On Radio Wave Propagation Measurements and Modelling for Cellular Mobile Radio Networks." Doctoral thesis, Karlskrona : Blekinge Institute of Technology, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-00443.

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To support the continuously increasing number of mobile telephone users around the world, mobile communication systems have become more advanced and sophisticated in their designs. As a result of the great success with the second generation mobile radio networks, deployment of the third and development of fourth generations, the demand for higher data rates to support available services, such as internet connection, video telephony and personal navigation systems, is ever growing. To be able to meet the requirements regarding bandwidth and number of users, enhancements of existing systems and introductions of conceptually new technologies and techniques have been researched and developed. Although new proposed technologies in theory provide increased network capacity, the backbone of a successful roll-out of a mobile telephone system is inevitably the planning of the network’s cellular structure. Hence, the fundamental aspect to a reliable cellular planning is the knowledge about the physical radio channel for wide sets of different propagation scenarios. Therefore, to study radio wave propagation in typical Australian environments, the Commonwealth Scientific and Industrial Research Organisation (CSIRO) and the Australian Telecommunications Cooperative Research Centre (ATcrc) in collaboration developed a cellular code division multiple access (CDMA) pilot scanner. The pilot scanner measurement equipment enables for radio wave propagation measurements in available commercial CDMA mobile radio networks, which in Australia are usually deployed for extensive rural areas. Over time, the collected measurement data has been used to characterise many different types of mobile radio environments and some of the results are presented in this thesis. The thesis is divided into an introduction section and four parts based on peer-reviewed international research publications. The introduction section presents the reader with some relevant background on channel and propagation modelling. Also, the CDMA scanner measurement system that was developed in parallel with the research results founding this thesis is presented. The first part presents work on the evaluation and development of the different revisions of the Recommendation ITU-R P.1546 point-to-area radio wave propagation prediction model. In particular, the modified application of the terrain clearance angle (TCA) and the calculation method of the effective antenna height are scrutinized. In the second part, the correlation between the smallscale fading characteristics, described by the Ricean K-factor, and the vegetation density in the vicinity of the mobile receiving antenna is investigated. The third part presents an artificial neural network (ANN) based technique incorporated to predict path loss in rural macrocell environments. Obtained results, such as prediction accuracy and training time, are presented for different sized ANNs and different training approaches. Finally, the fourth part proposes an extension of the path loss ANN enabling the model to also predict small-scale fading characteristics.
8

Arrascue, Ayala Victor Anthony [Verfasser], and Georg [Akademischer Betreuer] Lausen. "Towards an effective consumption of large-scale knowledge graphs for recommendations." Freiburg : Universität, 2020. http://d-nb.info/1223366189/34.

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9

Ardekani, Kamyar. "Feature Recommender : a large-scale in-situ study of proactive software feature recommendations." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/59761.

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In this thesis, we describe our design of Feature Recommender, a Mozilla Firefox browser extension, which proactively recommends features that it predicts will benefit users based on their individual usage behaviors. The goal of these pop-up notifications is to help users discover new features. How to maximize the effectiveness of such notifications while minimizing user interruptions remains a difficult open problem. One approach is to carefully time when the notifications are delivered. In our deployment of Feature Recommender, we study the effect of two delivery timing parameters: delivery rate and the user's context at the moment of delivery. We also investigate the effect of prediction algorithm sensitivity. We conducted three field studies, each about 4 weeks: (1) A preliminary study (N=10) to determine reasonable interruptible-moments; (2) A qualitative study (N=20) to assess the design and effectiveness of our extension; and (3) A near-identical study (N= ~3K) to assess quantitatively the effect of the timing parameters. Across all conditions Feature Recommender helped users adopt on average 18% of the features they were recommended, and as many as 24% when they were delivered at random times with a 1-per-day delivery rate limit. We show that lack of trust in recommendations is a key factor in hindering their effectiveness.
Science, Faculty of
Computer Science, Department of
Graduate
10

Richardson, James Rutherford. "Accommodating existing settlements in large scale development : recommendations for Sha Tin New Town Hong Kong." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/69533.

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MICROFICHE COPY AVAILABLE IN ARCHIVES AND ROTCH.
Thesis (M. Arch.)--Massachusetts Institute of Technology, Dept. of Architecture; and, (M.C.P.)--Massachusetts Institute of Technology, Dept. of Urban Studies and Planning, 1981.
Bibliography: leaves 173-176.
by James Rutherford Richardson, IV.
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Books on the topic "Large Scale Recommendation":

1

Mellina, Eric. Overview of large-scale ecological experimental designs and recommendations for the British Columbia Watershed Restoration Program. Vancouver, B.C: Ministry of Environment, Lands, and Parks, 1995.

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Lukanin, Alleksandr. Cleaning of gas and air emissions. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1070340.

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The monograph examines the currently existing industrial gas emissions in the chemical, petrochemical, microbiological, pharmaceutical and related industries, methods for calculating their quantity and methods for protecting the air basin from them. The materials are based on an in-depth analysis of methods for cleaning frequently occurring, most dangerous substances that enter the Earth's atmosphere with waste gases of large-scale production. Recommendations are given on methods for calculating gross emissions of harmful substances for a large number of specific industries. The subject of the monograph is related to the scientific areas "Technosphere safety" and "Engineering environmental protection", training profiles: engineering environmental protection of localities, engineering environmental protection of industrial enterprises and environmental protection and resource conservation. It will be of interest to engineering and technical staff, graduate students and teachers.
3

Berman, Amy, Edward Haertel, and James Pellegrino, eds. Comparability of Large-Scale Educational Assessments: Issues and Recommendations. National Academy of Education, 2020. http://dx.doi.org/10.31094/2020/1.

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Holden, Melanie A., Martin J. Thomas, and Krysia S. Dziedzic. Miscellaneous physical therapies. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199668847.003.0026.

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Miscellaneous physical therapies, such as assistive devices, thermotherapy, manual therapy, and electrotherapy are commonly used to treat patients with osteoarthritis (OA) in addition to written information and exercise. However, the evidence underpinning specific miscellaneous physical therapies is often limited, with small study sizes, heterogeneous populations, and differing study designs making it difficult to draw firm conclusions about their effectiveness. One or more miscellaneous physical therapies feature within 15 current clinical guidelines for OA. The specific types of physical therapies addressed are variable, as are their recommendations. There is most agreement for miscellaneous physical therapies in hand OA, with multiple guidelines addressing and consistently recommending joint protection, splinting, and thermotherapy in addition to core treatment. However these recommendations are predominantly based on a small number of randomized controlled trials (RCTs). Use of walking aids and footwear is commonly addressed and recommended for patients with hip and knee OA, although recommendations are predominantly based on expert opinion. Other physical therapies recommended for hip and knee OA range from orthoses to less conventional leech therapy. When a recommendation for a miscellaneous physical therapy is not made, it is commonly due to limited clinical evidence, rather than evidence of harm. Due to limited evidence and lack of consensus between clinical guidelines, for some therapies, use of specific miscellaneous physical therapies in clinical practice should be based upon the best available evidence, a holistic, individualized clinical assessment and shared decision-making with the patient. Further large-scale, high-quality RCTs would be useful to inform future guideline recommendations and clinical practice.
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Cawthon, Stephanie W. Large-Scale Survey Design in Deaf Education Research. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190455651.003.0009.

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Large-scale surveys are an appealing research design option for those wishing to collect data from many participants dispersed across different settings. This chapter describes several critical issues that must be considered when developing and conducting large-scale surveys in deaf education: aligning with a theoretical rationale, considering sample characteristics and potential confounds, piloting study measures, and developing an analysis plan. The chapter provides examples of ways to capture the heterogeneous demographics inherent within deaf education, ranging from individual characteristics such as identity, language use, and professional experience to educational setting characteristics such as program models and available accommodations. The chapter provides recommendations for how to instill trust and be mindful of participant fatigue during the recruitment process. The chapter ends with strategies for making survey recruitment materials, test directions, and items accessible for a diverse study population.
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Recommendations for ground effects research for V/STOL and STOL aircraft and associated equipment for large scale testing. Moffett Field, Calif: National Aeronautics and Space Administration, Ames Research Center, 1986.

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Cook, Harry, and Michael Newson. Yemeni Irregular Migrants in the Kingdom of Saudi Arabia and the Implications of Large Scale Return. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190608873.003.0007.

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In 2013, the Saudi government embarked on a nationwide strategy to restructure its labor market and its policies towards the recruitment of foreign workers. These changes are in line with the implementation of Saudi Arabia’s Nitaqat system which aims to better regulate foreign labor in the country and to reduce the number of irregular workers in the Kingdom. As a result of these changes in policy and implementation, there have been large-scale deportations of irregular workers—along with their family members, in some cases—from KSA beginning in mid-2013 and continuing up to the time of writing. Yemeni workers in KSA have been particularly hard hit by these policy changes due to the largely informal nature of labor migration flows that have existed between KSA and Yemen for the past few decades. This chapter explores the possible implications of the recent labor policy changes in KSA for Yemeni and host communities in KSA, as well as for returning workers, their families, and communities of origin in Yemen. The chapter concludes with several recommendations on how to effectively address the challenges these disruptions will cause and how to build new avenues to support the transnational linkages between Yemeni migrant workers in KSA and their communities in Yemen.
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Kornell, Nate, and Bridgid Finn. Self-Regulated Learning. Edited by John Dunlosky and Sarah (Uma) K. Tauber. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199336746.013.23.

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Effective self-regulated studying can influence students’ learning in school and beyond. This chapter reviews research on two key decisions: when to study and how to study. It first reviews the decisions people make about when to start and stop studying—that is, when to study—and the metacognitive judgments that underlie those decisions. It distinguishes between small-scale and large-scale decisions, such as which problem to work on next and whether to study today at all, respectively. It then discusses decisions about how to study, for example, whether or not to take notes, underline, test oneself, or reread. It then discusses key areas for future research, with an emphasis on student-centric research and research in digital learning environments. It offers practical recommendations for studiers about how to avoid overconfidence and procrastination and how to choose study strategies that increase short-term difficulty and long term success.
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Bellosta-López, Pablo, Priscila de Brito Silva, Palle S. Jensen, Morten S. Hoegh, Thorvaldur S. Palsson, Steffan Wittrup Mc Phee Christensen, Julia Blasco-Abadía, et al. Recommendations for implementation of the topic musculoskeletal disorders in the occupational health and safety postgraduate programmes at European Universities. Prevent4Work, 2021. http://dx.doi.org/10.54391/123456789/672.

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Historically, the role of professionals specialized in occupational health and safety (OHS) has emerged from the need to protect employers working in major risk industries such as nuclear plants and large-scale chemical industries in Europe. More recently, a few studies highlighted that the range of activities linked to safety management responsibilities includes monitor and prepare reports, inspection and auditing, regulatory compliance, emergency response, incident investigation, hazard and risk assessment, and training. Additionally, there are some supplementary non-safety related duties, such as including environmental responsibility. Considering that work-related musculoskeletal disorders (WRMD) are a major burden worldwide, adding up to 1.3 billion cases, more than 100 million years loss of disability-adjusted life years and that such disorders are common causes of disability and sick leave, this topic is highly relevant to OHS professionals. In EU Member States for which data are available, a large majority of all workers report complaints related to musculoskeletal disorders as their most serious work-related health problems. The percentage of workers reporting such complaints as their most serious health problem ranges from 40 % in Luxembourg to 70 % in Czech Republic and Finland. Furthermore, more than half of workers with musculoskeletal disorders reported taking time off work in a 12-month period. In the EU, 26 % of workers with musculoskeletal long-lasting disorders, that is lasting over 3 months, combined with other health problems report more than 8 days of absence per year. Higher Education Institutions (HEI) have a key role in disseminating and increasing accessibility to the most up-to-date evidence available regarding the impact and management of musculoskeletal disorders, to facilitate translation of knowledge to implementation in practice. This way, the Knowledge Alliance Prevent4Work for Preventing Work-Related Musculoskeletal Disorders has elaborated this document with the most recent and relevant knowledge within the topic. HEI that offer courses within OHS as well as graduation and post-graduation courses for health professionals that work within the area, may benefit from the recommendations presented here.
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Dennis, Faber, and Vermunt Niels, eds. Bank Failure: Lessons from Lehman Brothers. Oxford University Press, 2017. http://dx.doi.org/10.1093/law/9780198755371.001.0001.

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This new book analyses the legal and practical issues experienced during the Lehman Brothers litigation, the largest and most complex bankruptcy proceedings in history. By examining the issues the work provides a useful reference source for future large scale and cross-border bankruptcy proceedings of multinational groups. The contributors include experts from the various jurisdictions in which Lehman Brothers was operative, many of whom were involved in the litigation. The chapters set out practical solutions to the issues faced, concerning, for example, the use of existing payment and settlement systems for consent solicitation, filing instructions, and insolvency distributions. Economic challenges, such as the valuation of distressed financial instruments, are also considered. Additionally, the book provides a critique of the current law, analysis of the interpretation and scope of core legal principles and makes recommendations for regulatory reform and judicial cooperation. In this book first-hand accounts by key parties in the insolvency proceedings with expertise on the main issues are complemented by the views of selected independent experts. It is also enhanced by three chapters which further reflect on the Andean legal order.
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Book chapters on the topic "Large Scale Recommendation":

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Abbasi, Rabeeh, Marcin Grzegorzek, and Steffen Staab. "Large Scale Tag Recommendation Using Different Image Representations." In Semantic Multimedia, 65–76. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10543-2_8.

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Benouaret, Idir, and Dominique Lenne. "A Package-to-Group Recommendation Framework." In Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXIX, 43–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-58415-6_2.

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Prokofyev, Roman, Alexey Boyarsky, Oleg Ruchayskiy, Karl Aberer, Gianluca Demartini, and Philippe Cudré-Mauroux. "Tag Recommendation for Large-Scale Ontology-Based Information Systems." In The Semantic Web – ISWC 2012, 325–36. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35173-0_22.

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Draidi, Fady, Esther Pacitti, and Bettina Kemme. "P2Prec: A P2P Recommendation System for Large-Scale Data Sharing." In Transactions on Large-Scale Data- and Knowledge-Centered Systems III, 87–116. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23074-5_4.

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Yu, Ruiguo, Jianrong Wang, Tianyi Xu, Jie Gao, Kunyu Cao, and Mei Yu. "Communities Mining and Recommendation for Large-Scale Mobile Social Networks." In Wireless Algorithms, Systems, and Applications, 266–77. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60033-8_24.

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Chen, Ming, Chunying Li, Jiwei Liu, Dejie Meng, and Yong Tang. "Scholar Recommendation Model in Large Scale Academic Social Networking Platform." In Human Centered Computing, 453–64. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74521-3_48.

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Fu, Huazheng, Kang Chen, and Jianbing Ding. "An Empirical Study of a Large Scale Online Recommendation System." In Web Technologies and Applications, 15–25. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28121-6_2.

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Maurya, Chandresh Kumar, Seemandhar Jain, and Vishal Thakre. "Large-Scale Contact Tracing, Hotspot Detection, and Safe Route Recommendation." In Big Data Analytics, 163–82. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93620-4_13.

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Servajean, Maximilien, Esther Pacitti, Miguel Liroz-Gistau, Sihem Amer-Yahia, and Amr El Abbadi. "Increasing Coverage in Distributed Search and Recommendation with Profile Diversity." In Transactions on Large-Scale Data- and Knowledge-Centered Systems XXII, 115–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48567-5_4.

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Shi, Zhenkui. "Privacy-Assured Large-Scale Navigation from Encrypted Approximate Shortest Path Recommendation." In Communications in Computer and Information Science, 195–211. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8890-2_14.

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Conference papers on the topic "Large Scale Recommendation":

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Ji, Houye, Junxiong Zhu, Chuan Shi, Xiao Wang, Bai Wang, Chaoyu Zhang, Zixuan Zhu, Feng Zhang, and Yanghua Li. "Large-scale Comb-K Recommendation." In WWW '21: The Web Conference 2021. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3442381.3449924.

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Zhang, Xindong, Chenguang Zhu, Yi Li, Jianmei Guo, Lihua Liu, and Haobo Gu. "Large-scale patch recommendation at Alibaba." In ICSE '20: 42nd International Conference on Software Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3377812.3390902.

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Swezey, Robin M. E., and Bruno Charron. "Large-scale recommendation for portfolio optimization." In RecSys '18: Twelfth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3240323.3240386.

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Lee, Joonseok, and Sami Abu-El-Haija. "Large-Scale Content-Only Video Recommendation." In 2017 IEEE International Conference on Computer Vision Workshop (ICCVW). IEEE, 2017. http://dx.doi.org/10.1109/iccvw.2017.121.

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Chen, Chaochao, Xinxing Yang, Li Wang, Jun Zhou, and Xiaolong Li. "Large scale app recommendation in Ant Financial." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258524.

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Chamberlain, Benjamin P., Emanuele Rossi, Dan Shiebler, Suvash Sedhain, and Michael M. Bronstein. "Tuning Word2vec for Large Scale Recommendation Systems." In RecSys '20: Fourteenth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3383313.3418486.

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Joglekar, Manas R., Cong Li, Mei Chen, Taibai Xu, Xiaoming Wang, Jay K. Adams, Pranav Khaitan, Jiahui Liu, and Quoc V. Le. "Neural Input Search for Large Scale Recommendation Models." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3403288.

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Lerallut, Romain, Diane Gasselin, and Nicolas Le Roux. "Large-Scale Real-Time Product Recommendation at Criteo." In RecSys '15: Ninth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2792838.2799498.

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Yang, Longqi, Tobias Schnabel, Paul N. Bennett, and Susan Dumais. "Local Factor Models for Large-Scale Inductive Recommendation." In RecSys '21: Fifteenth ACM Conference on Recommender Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3460231.3474276.

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Qian, Shiyou, Yanmin Zhu, and Minglu Li. "Smart recommendation by mining large-scale GPS traces." In 2012 IEEE Wireless Communications and Networking Conference (WCNC). IEEE, 2012. http://dx.doi.org/10.1109/wcnc.2012.6214371.

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Reports on the topic "Large Scale Recommendation":

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Spane, Frank A. Large-Scale Pumping Test Recommendations for the 200-ZP-1 Operable Unit. Office of Scientific and Technical Information (OSTI), September 2010. http://dx.doi.org/10.2172/989035.

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Albornoz, Facundo, Guillermo Cruces, and María Lombardi. Trusting Covid-19 recommendations: The role of experts, markets and governments. Inter-American Development Bank, August 2023. http://dx.doi.org/10.18235/0005097.

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Do individuals trust experts' advice? Does the sector represented by these experts matter for trust and compliance? Do individuals prefer the public or the private sector for large-scale responses to events such as the pandemic? We answer these questions by means of a large-scale survey on a representative sample of 9,444 respondents from Argentina, Bolivia, Colombia, Ecuador, Mexico, Peru and Uruguay. We study if opinions on risk-mitigating actions against Covid-19 are shaped by expert recommendations and the sectors they represent. We identify a backlash against experts' recommendations that is robust across expert sectors and countries, and more pronounced for recommendations that require more effort to implement. We also find that, even for individuals with a low level of trust in the public sector, there is widespread agreement that governments should be preferred over the private sector to lead the production and distribution of vaccines. Most respondents, even those expressing distrust in governments, believe that governments should get involved in producing the vaccine for Covid-19, either exclusively or in a partnership with the private sector. This result is stronger for the distribution of the vaccine than for its production.
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Ford, Adam T., Marcel Huijser, and Anthony P. Clevenger. Long-term responses of an ecological community to highway mitigation measures. Nevada Department of Transportation, June 2022. http://dx.doi.org/10.15788/ndot2022.06.

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In road mitigation systems characterized by multiple wildlife crossing structures (CS) and multiple-focal species, these species-specific design criteria are important to meeting management goals. CS types and locations are fixed in place and cannot be manipulated experimentally; long term studies may offer the best chance to inform evidence-based designs for new CS projects in the future. Long-term data from Banff National Park are uniquely posed to answer these critical questions. More recently, highway mitigation along US93 in Montana provides an additional case study with which to understand the responses of large animals to different CS designs. The purpose of this study is to identify factors affecting movement of large mammals through CS using data sets from both mitigation projects. Year-round monitoring of CS use was used in an analytical framework to address questions regarding species-specific and community level use of CS; design and habitat factors that best explain species-specific variation; and whether importance of design parameters changes over time. Over the 17 years of the Banff study, and the six years of the Montana study, CS facilitated over 200,000 crossing events at 55 locations. There were significant changes in annual crossing events over time. Variables associated with CS passage rates were species specific, but aligned with a few clusters of preference. With the exception of coyotes, all large carnivore species preferred open span bridges or overpasses to other CS types. In Montana, fencing was positively associated with passage rates for black bears and cougars. We found that wider CS tend to be preferred by most species, irrespective of their location. We also found that wider CS tend to have shorter ‘adaptation’ curves than narrower ones for grizzly bears, coyotes, cougars, and moose. Depending on the heterogeneity of the landscape near the highway, more CS may not create more crossing opportunities if local habitat conditions do not favor animals’ access to the road. At the scale of ecological communities, the flows of mass and energy are likely enough to alter the distribution of ecological processes in the Banff and Montana ecosystems. Our results highlight the value of long-term monitoring for assessing the effectiveness of mitigation measures. Our work confirms the species-specific nature of measure CS performance, leading to our primary recommendation that a diversity of CS designs be considered an essential part of a well-designed mitigation system for the large mammals of western North America. Short-term monitoring efforts may fail to accurately portray the ecological benefits of mitigation for populations and ecological communities. Our results will help to inform design and aid in the establishment of robust, long-term performance measures.
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Barba, Ricardo Carlos, Sourav Majumder, Palak Rawal, and Saswati Ghosh Belliappa. Resettling Urban Populations: Learning from the Graduation Approach in India. Asian Development Bank, May 2023. http://dx.doi.org/10.22617/wps230201-2.

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The integration of the Graduation Approach in resettlement planning and implementation under the Tamil Nadu Inclusive, Resilient, and Sustainable Housing for Urban Poor Sector Project presents a unique model for large-scale resettlement. The Graduation approach is a combination of sequenced interventions aimed to lift households out of poverty and into sustainable livelihoods. This working paper explains the contextualized design of the Graduation program for the project, its envisaged impact, lessons learned at the design phase, and recommendations for other projects involving large scale resettlement and provides a model for future resettlement planning across the region.
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Hinrichs, Claudia, and Judith Hauck. Report on skill of CMIP6 models to simulate alkalinity and improved parameterizations for large scale alkalinity distribution. OceanNets, June 2022. http://dx.doi.org/10.3289/oceannets_d4.4.

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In part one of this deliverable, an ensemble of 14 CMIP6 Earth System Models is evaluated regarding their performance in simulating alkalinity and related parameters. The majority of the models and the multi-model-mean underestimate surface alkalinity compared to climatological observations. Alkalinity biases stemming from the parametrization of calcium carbonate formation and dissolution can be as big as biases stemming from model physics. In part two, we test the sensitivity of parametrizations concerning the carbonate chemistry in the FESOM2.1-REcoM3 and give recommendations for addressing alkalinity biases.
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Muldavin, Esteban, Yvonne Chauvin, Teri Neville, Hannah Varani, Jacqueline Smith, Paul Neville, and Tani Hubbard. A vegetation classi?cation and map: Guadalupe Mountains National Park. National Park Service, 2024. http://dx.doi.org/10.36967/2302855.

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A vegetation classi?cation and map for Guadalupe Mountains National Park (NP) is presented as part of the National Park Service Inventory & Monitoring - Vegetation Inventory Program to classify, describe, and map vegetation communities in more than 280 national park units across the United States. Guadalupe Mountains NP lies in far west Texas and contains the highest point in the state, Guadalupe Peak (8,751 ft; 2,667 m). The mountain escarpments descend some 5,000 ft (1,500 m) to the desert basins below forming a complex geologic landscape that supports vegetation communities ranging from montane coniferous forests down to desert grasslands and scrub. Following the US National Vegetation Classi?cation (USNVC) standard, we identi?ed 129 plant associations hierarchically tiered under 29 groups and 17 macrogroups, making it one of the most ecologically diverse National Park Service units in the southwestern United States. An aspect that adds to this diversity is that the park supports communities that extend southward from the Rocky Mountains (?ve macrogroups) and Great Plains (one macrogroup) and northward from the Chihuahuan Desert (two macrogroups) and Sierra Madre Orientale of Mexico (three macrogroups). The remaining six macrogroups are found in the Great Basin (one macrogroup), and throughout the southwestern United States (remaining ?ve macrogroups). Embedded in this matrix are gypsum dunelands and riparian zones and wetlands that add further complexity. We describe in detail this vegetation classi?cation, which is based on 540 vegetation plots collected between 2006 and 2010. Full descriptions and diagnostic keys to the plant associations along with an overall plant species list are provided as appendices. Based on the vegetation classi?cation and associated plot data, the vegetation map was developed using a combined strategy of automated digital object-oriented image classi?cation and direct-analog image interpretation of four-band National Agricultural Imagery Program (NAIP) aerial photography from 2004 and 2008 and Landsat Thematic Mapper satellite imagery. The map is designed to facilitate ecologically-based natural resource management at a 1:24,000 scale with 0.5-ha minimum map unit size. The map legend is hierarchically structured: the upper Level 1 consists of 16 map units corresponding in most cases to the USNVC group level, and an additional map unit describing built-up land and agriculture; Level 2 is composed of 48 nested map units re?ecting various combinations of plant associations. A ?eld-based accuracy assessment using 341 vegetation plots revealed a Level 1 overall accuracy of 79% with 90% CI of 74?84% and 68% with 90% CI of 59?76% at Level 2. An annotated legend with summary descriptions of the units, distribution maps, aerial photo examples of map unit polygons, and representative photos are provided in Appendix D. Large wall-size poster maps at 1:35,000 scale were also produced following NPS cartographic standards. The report, plot data, and spatial layers are available at National Park Service Vegetation Mapping Program https://www.nps.gov/im/vegetation-inventory.htm). Outcomes from this project provide the most detailed vegetation classi?cation and highest resolution mapping for Guadalupe Mountains NP to date to support many uses including ?re, recreation, vegetation, and wildlife management, among others. The upper Level 1 map is particularly suited to landscape-scale, park-wide planning and linkages to its sister park, Carlsbad Caverns NP. The Level 2 mapping provides added detail for use at a more localized project scale. The overall accuracy of the maps was good, but because Guadalupe Mountains NP is primarily wilderness park, there were logistical challenges to map development and testing in remote areas that should be considered in planning management actions. In this context, some map units would bene?t from further development and accuracy assessment. In particular, a higher resolution mapping of McKittrick Creek riparian habitat at 1:6,000 scale or ?ner is recommended for this important habitat in the park. In addition, developing a structural canopy height model from LiDAR imagery would be useful to more accurately quantify woody canopy density and height to support ?re management and other habitat management issues. With respect to understanding vegetation dynamics in this time of rapid environmental change, the 540 vegetation plots themselves are su?ciently georeferenced and have the data resolution to be useful in detecting change at the decadal scales across much of the park. To this end, an additional recommendation would be to install more plots to ?ll the gaps among the main vegetation units of the park, both spatially and thematically. Overall, the Vegetation and Classi?cation Map for Guadalupe Mountains NP will support the park?s management e?orts and enhance regional understanding of vegetation and ecology of ecosystems of the southwestern United States.
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Marcos Morezuelas, Paloma. Gender and Renewable Energy: Wind, Solar, Geothermal and Hydroelectric Energy. Inter-American Development Bank, November 2014. http://dx.doi.org/10.18235/0003068.

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This document focuses on how to incorporate a gender perspective in operations that support the construction, operation and maintenance of medium- and large-scale renewable wind, solar, geothermal and hydroelectric energy installations connected to the grid for purposes of power generation. Additionally, there is also a section on rural energy that is applicable to small installations and mini-grids, or to exceptional cases where medium- and large-scale facilities provide electricity to a community. The document (i) identifies the possible gender equality challenges and opportunities as part of the project assessment, (ii) highlights the risks and potentially negative impacts of the project on gender equality, (iii) offers recommendations for addressing, preventing and mitigating challenges and for maximizing opportunities; and (iv) presents examples of programs that have taken into account gender differences or risks. In addition, the document includes (v) key questions for analyzing gender issues in renewable energy projects, and (vi) examples of indicators for the monitoring and evaluation of operations in the renewable energy sector.
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Zhai, Yuhui, and Yanfeng Ouyang. Effects of Nontraditional Messages in Dynamic Message Signs on Improving Safety, Compliance, and Avoiding Distraction. Illinois Center for Transportation, May 2024. http://dx.doi.org/10.36501/0197-9191/24-014.

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This project develops capability for future studies on the effects of nontraditional messages in dynamic message signs on improving driver safety and compliance and avoiding distractions. An online survey and a driving simulation game were designed, and a pilot run was conducted with a small number of students in the University of Illinois Urbana Champaign. Survey responses and simulation data were analyzed to reveal preliminary findings on notable effectiveness across different types of messages (e.g., humorous, emotionless, negative). The pilot study also provides guidelines and recommendations for running a large-scale survey and simulation game in the future.
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Dunlop, Steven R., Satish Ukkusuri, Dutt J. Thakkar, Shagun Mittal, Utkarsh Patil, Jainam Gala, and Thomas Brady. Economic Effect of Active Transportation Features and the Association Between the Healthcare Industry and Transportation. Purdue University, 2024. http://dx.doi.org/10.5703/1288284317655.

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The goal of this project is to help INDOT determine the economic effects of active transportation features, the impact of transportation infrastructure on the healthcare industry, and to explore a potential relationship between active transportation and healthcare. To capture the details thoroughly, the analysis was done at a macro and micro level. We identified relevant attributes based on existing studies and captured the county-level data for these attributes from public sources. A regression analysis was performed at the macro-level to understand relationships and trends. In the micro-view, the analysis aimed to investigate the impact of active transportation investments on business growth and the impact of transportation investments on trip patterns and healthcare growth at a granular level of ZIP Code Tabulation Areas (ZCTAs). Large-scale datasets were analyzed to extract key metrics, which were tested for changes due to investments in the region. A Difference in Differences (DID) model was used to analyze causal effects, and trends of individual ZCTAs were analyzed against their corresponding demographics. Insights were developed and recommendations were made to INDOT based on the analysis regarding macro trends and factors impacting the transportation features. Practical recommendations for transportation investments and their corresponding effects were provided to INDOT based on the micro analyses.
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Buesseler, Buessele, Daniele Bianchi, Fei Chai, Jay T. Cullen, Margaret Estapa, Nicholas Hawco, Seth John, et al. Paths forward for exploring ocean iron fertilization. Woods Hole Oceanographic Institution, October 2023. http://dx.doi.org/10.1575/1912/67120.

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Abstract:
We need a new way of talking about global warming. UN Secretary General António Guterres underscored this when he said the “era of global boiling” has arrived. Although we have made remarkable progress on a very complex problem over the past thirty years, we have a long way to go before we can keep the global temperature increase to below 2°C relative to the pre-industrial times. Climate models suggest that this next decade is critical if we are to avert the worst consequences of climate change. The world must continue to reduce greenhouse gas emissions, and find ways to adapt and build resilience among vulnerable communities. At the same time, we need to find new ways to remove carbon dioxide from the atmosphere in order to chart a “net negative” emissions pathway. Given their large capacity for carbon storage, the oceans must be included in consideration of our multiple carbon dioxide removal (CDR) options. This report focused on ocean iron fertilization (OIF) for marine CDR. This is by no means a new scientific endeavor. Several members of ExOIS (Exploring Ocean Iron Solutions) have been studying this issue for decades, but the emergence of runaway climate impacts has motivated this group to consider a responsible path forward for marine CDR. That path needs to ensure that future choices are based upon the best science and social considerations required to reduce human suffering and counter economic and ecological losses, while limiting and even reversing the negative impacts that climate change is already having on the ocean and the rest of the planet. Prior studies have confirmed that the addition of small amounts of iron in some parts of the ocean is effective at stimulating phytoplankton growth. Through enhanced photosynthesis, carbon dioxide can not only be removed from the atmosphere but a fraction can also be transferred to durable storage in the deep sea. However, prior studies were not designed to quantify how effective this storage can be, or how wise OIF might be as a marine CDR approach. ExOIS is a consortium that was created in 2022 to consider what OIF studies are needed to answer critical questions about the potential efficiency and ecological impacts of marine CDR (http://oceaniron.org). Owing to concerns surrounding the ethics of marine CDR, ExOIS is organized around a responsible code of conduct that prioritizes activities for the collective benefit of our planet with an emphasis on open and transparent studies that include public engagement. Our goal is to establish open-source conventions for implementing OIF for marine CDR that can be assessed with appropriate monitoring, reporting, and verification (MRV) protocols, going beyond just carbon accounting, to assess ecological and other non-carbon environmental effects (eMRV). As urgent as this is, it will still take 5 to 10 years of intensive work and considerable resources to accomplish this goal. We present here a “Paths Forward’’ report that stems from a week-long workshop held at the Moss Landing Marine Laboratories in May 2023 that was attended by international experts spanning atmospheric, oceanographic, and social sciences as well as legal specialists (see inside back cover). At the workshop, we reviewed prior OIF studies, distilled the lessons learned, and proposed several paths forward over the next decade to lay the foundation for evaluating OIF for marine CDR. Our discussion very quickly resulted in a recommendation for the need to establish multiple “Ocean Iron Observatories’’ where, through observations and modeling, we would be able to assess with a high degree of certainty both the durable removal of atmospheric carbon dioxide—which we term the “centennial tonne”—and the ecological response of the ocean. In a five-year phase I period, we prioritize five major research activities: 1. Next generation field studies: Studies of long-term (durable) carbon storage will need to be longer (year or more) and larger (>10,000 km2) than past experiments, organized around existing tools and models, but with greater reliance on autonomous platforms. While prior studies suggested that ocean systems return to ambient conditions once iron infusion is stopped, this needs to be verified. We suggest that these next field experiments take place in the NE Pacific to assess the processes controlling carbon removal efficiencies, as well as the intended and unintended ecological and geochemical consequences. 2. Regional, global and field study modeling Incorporation of new observations and model intercomparisons are essential to accurately represent how iron cycling processes regulate OIF effects on marine ecosystems and carbon sequestration, to support experimental planning for large-scale MRV, and to guide decision making on marine CDR choices. 3. New forms of iron and delivery mechanisms Rigorous testing and comparison of new forms of iron and their potential delivery mechanisms is needed to optimize phytoplankton growth while minimizing the financial and carbon costs of OIF. Efficiency gains are expected to generate responses closer to those of natural OIF events. 4. Monitoring, reporting, and verification: Advances in observational technologies and platforms are needed to support the development, validation, and maintenance of models required for MRV of large-scale OIF deployment. In addition to tracking carbon storage and efficiency, prioritizing eMRV will be key to developing regulated carbon markets. 5. Governance and stakeholder engagement: Attention to social dimensions, governance, and stakeholder perceptions will be essential from the start, with particular emphasis on expanding the diversity of groups engaged in marine CDR across the globe. This feedback will be a critical component underlying future decisions about whether to proceed, or not, with OIF for marine CDR. Paramount in the plan is the need to move carefully. Our goal is to conduct these five activities in parallel to inform decisions steering the establishment of ocean iron observatories at multiple locations in phase II. When completed, this decadal plan will provide a rich knowledge base to guide decisions about if, when, where, and under what conditions OIF might be responsibly implemented for marine CDR. The consensus of our workshop and this report is that now is the time for actionable studies to begin. Quite simply, we suggest that some form of marine CDR will be essential to slow down and reverse the most severe consequences of our disrupted climate. OIF has the potential to be one of these climate mitigation strategies. We have the opportunity and obligation to invest in the knowledge necessary to ensure that we can make scientifically and ethically sound decisions for the future of our planet.

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