Relevant bibliographies by topics / EFFICIENT RECOMMENDER SYSTEMS

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

Academic literature on the topic 'EFFICIENT RECOMMENDER SYSTEMS'

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Published: 11 September 2023

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Journal articles on the topic "EFFICIENT RECOMMENDER SYSTEMS"

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Ribeiro, Marco Tulio, Nivio Ziviani, Edleno Silva De Moura, Itamar Hata, Anisio Lacerda, and Adriano Veloso. "Multiobjective Pareto-Efficient Approaches for Recommender Systems." ACM Transactions on Intelligent Systems and Technology 5, no. 4 (January 23, 2015): 1–20. http://dx.doi.org/10.1145/2629350.

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Huang, Zhen Hua, Dong Wang, and Sheng Li Sun. "Efficient Mining of Skyrank Items in Recommender Systems." Advanced Materials Research 472-475 (February 2012): 3450–54. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.3450.

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Mining of skyrank items has recently received a lot of attention in recommender system community. Literature [3] presents an efficient algorithm ZHYX to produce the skyrank items in one single subspace. However, in multi-user environments, recommender systems generally receive multiple subspace skyrank queries simultaneously. Hence, in this paper, we propose the first efficient sound and complete algorithm, i.e. AMMSSI(Algorithm for Mining Multiple Subsapce Skyrank Items), to markedly reduce the total response time. The detailed theoretical analyses and extensive experiments demonstrate that our proposed algorithm is both efficient and effective.
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Hawashin, Bilal, Shadi Alzubi, Tarek Kanan, and Ayman Mansour. "An efficient semantic recommender method forArabic text." Electronic Library 37, no. 2 (April 1, 2019): 263–80. http://dx.doi.org/10.1108/el-12-2018-0245.

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PurposeThis paper aims to propose a new efficient semantic recommender method for Arabic content.Design/methodology/approachThree semantic similarities were proposed to be integrated with the recommender system to improve its ability to recommend based on the semantic aspect. The proposed similarities are CHI-based semantic similarity, singular value decomposition (SVD)-based semantic similarity and Arabic WordNet-based semantic similarity. These similarities were compared with the existing similarities used by recommender systems from the literature.FindingsExperiments show that the proposed semantic method using CHI-based similarity and using SVD-based similarity are more efficient than the existing methods on Arabic text in term of accuracy and execution time.Originality/valueAlthough many previous works proposed recommender system methods for English text, very few works concentrated on Arabic Text. The field of Arabic Recommender Systems is largely understudied in the literature. Aside from this, there is a vital need to consider the semantic relationships behind user preferences to improve the accuracy of the recommendations. The contributions of this work are the following. First, as many recommender methods were proposed for English text and have never been tested on Arabic text, this work compares the performance of these widely used methods on Arabic text. Second, it proposes a novel semantic recommender method for Arabic text. As this method uses semantic similarity, three novel base semantic similarities were proposed and evaluated. Third, this work would direct the attention to more studies in this understudied topic in the literature.
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Pasdar, Amirmohammad, Young Choon Lee, Tahereh Hassanzadeh, and Khaled Almi’ani. "Resource Recommender for Cloud-Edge Engineering." Information 12, no. 6 (May 25, 2021): 224. http://dx.doi.org/10.3390/info12060224.

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The interaction between artificial intelligence (AI), edge, and cloud is a fast-evolving realm in which pushing computation close to the data sources is increasingly adopted. Captured data may be processed locally (i.e., on the edge) or remotely in the clouds where abundant resources are available. While many emerging applications are processed in situ due primarily to their data intensiveness and short-latency requirement, the capacity of edge resources remains limited. As a result, the collaborative use of edge and cloud resources is of great practical importance. Such collaborative use should take into account data privacy, high latency and high bandwidth consumption, and the cost of cloud usage. In this paper, we address the problem of resource allocation for data processing jobs in the edge-cloud environment to optimize cost efficiency. To this end, we develop Cost Efficient Cloud Bursting Scheduler and Recommender (CECBS-R) as an AI-assisted resource allocation framework. In particular, CECBS-R incorporates machine learning techniques such as multi-layer perceptron (MLP) and long short-term memory (LSTM) neural networks. In addition to preserving privacy due to employing edge resources, the edge utility cost plus public cloud billing cycles are adopted for scheduling, and jobs are profiled in the cloud-edge environment to facilitate scheduling through resource recommendations. These recommendations are outputted by the MLP neural network and LSTM for runtime estimation and resource recommendation, respectively. CECBS-R is trained with the scheduling outputs of Facebook and grid workload traces. The experimental results based on unseen workloads show that CECBS-R recommendations achieve a ∼65% cost saving in comparison to an online cost-efficient scheduler (BOS), resource management service (RMS), and an adaptive scheduling algorithm with QoS satisfaction (AsQ).
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Jabbar, Muhammad, Qaisar Javaid, Muhammad Arif, Asim Munir, and Ali Javed. "An Efficient and Intelligent Recommender System for Mobile Platform." October 2018 37, no. 4 (October 1, 2018): 463–80. http://dx.doi.org/10.22581/muet1982.1804.02.

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Recommender Systems are valuable tools to deal with the problem of overloaded information faced by most of the users in case of making purchase decision to buy any item. Recommender systems are used to provide recommendations in many domains such as movies, books, digital equipment’s, etc. The massive collection of available books online presents a great challenge for users to select the relevant books that meet their preferences. Users usually read few pages or contents to decide whether to buy a certain book or not. Recommender systems provide different value addition factors such as similar user ratings, users past history, user profiles, etc. to facilitate the users in terms of providing relevant recommendations according to their preferences. Recommender systems are broadly categorized into content based approach and collaborative filtering approach. Content based or collaborative filtering approaches alone are not sufficient to provide most accurate and relevant recommendations under diverse scenarios. Therefore, hybrid approaches are also designed by combining the features of both the content based and collaborative filtering approaches to provide more relevant recommendations. This paper proposes an efficient hybrid recommendation scheme for mobile platform that includes the traits of content based and collaborative filtering approaches in addition of the context based approach that is included to provide the latest books recommendations to user.Objective and subjective evaluation measures are used to compute the performance of the proposed system. Experimental results are promising and signify the effectiveness of our proposed hybrid scheme in terms of most relevant and latest books recommendations.
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Radlinski, Filip, Craig Boutilier, Deepak Ramachandran, and Ivan Vendrov. "Subjective Attributes in Conversational Recommendation Systems: Challenges and Opportunities." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (June 28, 2022): 12287–93. http://dx.doi.org/10.1609/aaai.v36i11.21492.

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The ubiquity of recommender systems has increased the need for higher-bandwidth, natural and efficient communication with users. This need is increasingly filled by recommenders that support natural language interaction, often conversationally. Given the inherent semantic subjectivity present in natural language, we argue that modeling subjective attributes in recommenders is a critical, yet understudied, avenue of AI research. We propose a novel framework for understanding different forms of subjectivity, examine various recommender tasks that will benefit from a systematic treatment of subjective attributes, and outline a number of research challenges.
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Luo, Chenhong, Yong Wang, Bo Li, Hanyang Liu, Pengyu Wang, and Leo Yu Zhang. "An Efficient Approach to Manage Natural Noises in Recommender Systems." Algorithms 16, no. 5 (April 27, 2023): 228. http://dx.doi.org/10.3390/a16050228.

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Recommender systems search the underlying preferences of users according to their historical ratings and recommend a list of items that may be of interest to them. Rating information plays an important role in revealing the true tastes of users. However, previous research indicates that natural noises may exist in the historical ratings and mislead the recommendation results. To deal with natural noises, different methods have been proposed, such as directly removing noises, correcting noise by re-predicting, or using additional information. However, these methods introduce some new problems, such as data sparsity and introducing new sources of noise. To address the problems, we present a new approach to managing natural noises in recommendation systems. Firstly, we provide the detection criteria for natural noises based on the classifications of users and items. After the noises are detected, we correct them with threshold values weighted by probabilities. Experimental results show that the proposed method can effectively correct natural noise and greatly improve the quality of recommendations.
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Cui, Zeyu, Feng Yu, Shu Wu, Qiang Liu, and Liang Wang. "Disentangled Item Representation for Recommender Systems." ACM Transactions on Intelligent Systems and Technology 12, no. 2 (March 2021): 1–20. http://dx.doi.org/10.1145/3445811.

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Item representations in recommendation systems are expected to reveal the properties of items. Collaborative recommender methods usually represent an item as one single latent vector. Nowadays the e-commercial platforms provide various kinds of attribute information for items (e.g., category, price, and style of clothing). Utilizing this attribute information for better item representations is popular in recent years. Some studies use the given attribute information as side information, which is concatenated with the item latent vector to augment representations. However, the mixed item representations fail to fully exploit the rich attribute information or provide explanation in recommender systems. To this end, we propose a fine-grained Disentangled Item Representation (DIR) for recommender systems in this article, where the items are represented as several separated attribute vectors instead of a single latent vector. In this way, the items are represented at the attribute level, which can provide fine-grained information of items in recommendation. We introduce a learning strategy, LearnDIR, which can allocate the corresponding attribute vectors to items. We show how DIR can be applied to two typical models, Matrix Factorization (MF) and Recurrent Neural Network (RNN). Experimental results on two real-world datasets show that the models developed under the framework of DIR are effective and efficient. Even using fewer parameters, the proposed model can outperform the state-of-the-art methods, especially in the cold-start situation. In addition, we make visualizations to show that our proposition can provide explanation for users in real-world applications.
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Vaidhehi, V., and R. Suchithra. "A Systematic Review of Recommender Systems in Education." International Journal of Engineering & Technology 7, no. 3.4 (June 25, 2018): 188. http://dx.doi.org/10.14419/ijet.v7i3.4.16771.

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Recommender system (RS)s are widely used in different walks of life. This research work is to explore the usage of RS in the field of education. This review is performed in five dimensions which includes, Purpose of RS in Education, various techniques to build RS, input parameters used in design of RS, type of students involved in design of RS and Modelling strategies for RS to represent the data. The outcome of the research work is to facilitate the efficient design of the recommender system in education which will help the students by generating the appropriate recommendations.
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Hawashin, Bilal, Darah Aqel, Shadi Alzubi, and Mohammad Elbes. "Improving Recommender Systems Using Co-Appearing and Semantically Correlated User Interests." Recent Advances in Computer Science and Communications 13, no. 2 (June 3, 2020): 240–47. http://dx.doi.org/10.2174/2213275912666190115162311.

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Background: Recommender Systems use user interests to provide more accurate recommendations according to user actual interests and behavior. Methods: This work aims at improving recommender systems by discovering hidden user interests from the existing interests. User interest expansion would contribute in improving the accuracy of recommender systems by finding more user interests using the given ones. Two methods are proposed to perform the expansion: Expanding interests using correlated interests’ extractor and Expanding interests using word embeddings. Results: Experimental work shows that such expanding is efficient in terms of accuracy and execution time. Conclusion: Therefore, expanding user interests proved to be a promising step in the improvement of the recommender systems performance.
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Dissertations / Theses on the topic "EFFICIENT RECOMMENDER SYSTEMS"

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Ribeiro, Marco Tulio Correia. "Multi-objective pareto-efficient algorithms for recommender systems." Universidade Federal de Minas Gerais, 2013. http://hdl.handle.net/1843/ESSA-9CHG5H.

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Recommender systems are quickly becoming ubiquitous in applications such as ecommerce, social media channels and content providers, acting as enabling mechanisms designed to overcome the information overload problem by improving browsing and consumption experience. A typical task in recommender systems is to output a ranked list of items, so that items placed higher in the rank are more likely to be interesting to the users. Interestingness measures include how accurate, novel and diverse the suggested items are, and the objective is usually to produce ranked lists optimizing one of these measures. Suggesting items that are simultaneously accurate, novel and diverse is much more challenging, since this may lead to a conflicting-objective problem, in which the attempt to improve a measure further may result in worsening other measures. In this thesis we propose new approaches for multi-objective recommender systems based on the concept of Pareto-efficiency -- a state achieved when the system is devised in the most efficient manner in the sense that there is no way to improve one of the objectives without making any other objective worse off. Given that existing recommendation algorithms differ in their level of accuracy, diversity and novelty, we exploit the Pareto-efficiency concept in two distinct manners: (i) the aggregation of ranked lists produced by existing algorithms into a single one, which we call Paretoefficient ranking, and (ii) the weighted combination of existing algorithms resulting in a hybrid one, which we call Pareto-efficient hybridization. Our evaluation involves two real application scenarios: music recommendation with implicit feedback (i.e., Last.fm) and movie recommendation with explicit feedback (i.e., MovieLens). We show that the proposed approaches are effective in optimizing each of the metrics without hurting the others, or optimizing all three simultaneously. Further, for the Pareto-efficient hybridization, we allow for adjusting the compromise between the metrics, so that the recommendation emphasis can be set dinamically according to the needs of different users.
Sistemas de recomendação tem se tornado cada vez mais populares em aplicações como e-commerce, mídias sociais e provedores de conteúdo. Esses sistemas agem como mecanismos para lidar com o problema da sobrecarga de informação. Uma tarefa comum em sistemas de recomendação é a de ordenar um conjunto de itens, de forma que os itens no topo da lista sejam de interesse para os usuários. O conceito de interesse pode ser medido observando a acurácia, novidade e diversidade dos itens sugeridos. Geralmente, o objetivo de um sistema de recomendação é gerar listas ordenadas de forma a otimizar uma dessas métricas. Um problema mais difícil é tentar otimizar as três métricas (ou objetivos) simultaneamente, o que pode levar ao caso onde a tentativa de melhorar em uma das métricas pode piorar o resultado nas outras métricas. Neste trabalho, propomos novas abordagens para sistemas de recomendaççao multi-objetivo, baseadas no conceito de Eficiência de Pareto -- um estado obtido quando o sistema é de tal forma que não há como melhorar em algum objetivo sem piorar em outro objetivo. Dado que os algoritmos de recomendação existentes diferem em termos de acurácia, diversidade e novidade, exploramos o conceito de Eficiência de Pareto de duas formas distintas: (i) agregando listas ordenadas produzidas por algoritmos existentes de forma a obter uma lista única - abordagem que chamamos de ranking Pareto-eficiente, e (ii), a combinação linear ponderada de algoritmos existentes, resultado em um híbrido, abordagem que chamamos de hibridização Pareto-eficiente. Nossa avaliação envolve duas aplicações reais: recomendação de música com feedback implícito (i.e., Last.fm) e recomendação de filmes com feedback explícito (i.e., Movielens). Nós mostramos que as abordagens Pareto-eficientes são efetivas em recomendar items com bons niveis de acurácia, novidade e diversidade (simultaneamente), ou uma das métricas sem piorar as outras. Além disso, para a hibridização Pareto-eficiente, provemos uma forma de ajustar o compromisso entre acurácia, novidade e diversidade, de forma que a ênfase da recomendação possa ser ajustada dinamicamente para usuários diferentes.
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Esfandiar, Pooya. "Efficient approximation of social relatedness over large social networks and application to query enabled recommender systems." Thesis, University of British Columbia, 2010. http://hdl.handle.net/2429/27778.

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Social relatedness measures such as the Katz score and the commute time between pairs of nodes have been subject of significant research effort motivated by social network problems including link prediction, anomalous link detection, and collaborative filtering. In this thesis, we are interested in computing: (1) the score for a given pair of nodes, and (2) the top-k nodes with the highest scores from a specific source node. Unlike most traditional approaches, ours scale to large networks with hundreds of thousands of nodes. We introduce an efficient iterative algorithm which calculates upper and lower bounds for the pairwise measures. For the top-k problem, we propose an algorithm that only has access to a small subset of nodes. Our approaches rely on techniques developed in numerical linear algebra and personalized PageRank computing. Using three real-world networks, we examine scalability and accuracy of our algorithms as in a short time as milliseconds to seconds. We also hypothesize that incorporating item based tags into a recommender system will improve its performance. We model such a system as a tri-partite graph of users, items and tags and use this graph to define a scoring function making use of graph-based proximity measures. Exactly calculating the item scores is computationally expensive, so we use the proposed top-k algorithm to calculate the scores. The usefulness and efficiency of the approaches are compared to a simple, non-graph based, approach. We evaluate these approaches on a combination of the Netflix ratings data and the IMDb tag data.
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Broccolo, Daniele <1984&gt. "Query log based techniques to improve the performance of a web search engine." Doctoral thesis, Università Ca' Foscari Venezia, 2014. http://hdl.handle.net/10579/4635.

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Every user leaves traces of her/his behaviour when she/he surfs the Web. All the usage data generated by users is stored in logs of several web applications, and such logs can be used to extract useful knowledge for enhancing and improving performance of online services. Also Search Engines (SEs) store usage information in so-called query logs, which can be used in different ways to improve the SE user experience. In this thesis we focus on improving the performance of a SE, in particular its effectiveness and efficiency, through query log mining. We propose to enhance the performance of SEs by discussing a novel Query Recommender System. We prove that is possible to decrease the length of a user's query session by unloading the SE of part of the queries that the user submits in order to refine his initial search. This approach helps the user find what she/he is searching in a shorter period of time, while at the same time decreasing the number of queries that the SE must process, and thus decreasing the overall server load. We also discuss how to enhance the SE efficiency by optimizing the use of its computational resources. The knowledge extracted from a query log is used to dynamically adjust the query processing method by adapting the pruning strategy to the SE load. In particular query logs permit to build a regressive model used to predict the response time for any query, when different pruning strategies are applied during query processing. The prediction is used to ensure a minimum quality of service when the system is heavily loaded, by trying to process the various enqueued queries by a given deadline. Our study also addresses the problem of the effectiveness of query results by comparing their quality when dynamic pruning is adopted to reduce the query processing times. Finally, we also study how response times and results vary when, in presence of high loads, processing is either interrupted after a fixed time threshold elapses or dropped completely. Moreover, we introduce a novel query dropping strategy based on the same query performance predictors discussed above.
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Liao, Chih-lun, and 廖志倫. "Efficiency Improvement for Collaborative Filtering Recommender System." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/927hk5.

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碩士
國立中山大學
電機工程學系研究所
104
In collaborative filtering based recommender systems, products are regarded as features and users are required to provide rating scores to the products they have purchased. By learning from the rating scores, such a recommender system can recommend interesting products to the users. However, there are usually quite a lot of products involved and it would be very inefficient if every product needs to be considered before making any recommendations. We propose a novel approach which applies a self-constructing clustering algorithm to reduce the dimensionality related to the number of products. Similar products are grouped in a cluster and dissimilar products are dispatched in different clusters. Recommendation work is then done with the resulting clusters. Finally, re-transformation is performed and a preference list about the products is offered to each user. With the proposed approach, the processing time for making recommendations is much reduced. Experimental results show that the efficiency of the recommender systems is greatly improved without the degradation of the recommendation quality.
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Chang, I.-Lung, and 張益龍. "Algorithms for Improving the Efficiency and Visualization of Library Recommender System." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/40716404095291144017.

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碩士
國立中興大學
資訊科學與工程學系所
98
In the era of information explosion, personalized recommendation services have become an important component for most information systems. Since 2006, the research team mainly consisting of researchers at National Chung-Hsing University (NCHU) proposed a recommender system called PORE (Personal Ontology REcommender System) for recommending library collections. The system is the first one in using personal ontology for library collection recommendation. However, there is still some weakness which should be improved in the PORE system. This thesis proposed methods on improving visualization of recommended results and the performance of collaborative filtering recommendation. For visualization of recommended results, this study developed a 3D interface using an open source tool, OntoSphere 3D, to show personal ontology, the relationship between personal ontology, and the recommended library collections. In the 3D model, users can rotate, shrink, or move recommended objects. As a result, the 3D interface provides a more user-friendly interface to the users of the PORE system. For the performance improvement of collaborative filtering recommendation, we proposed a method that clusters users with overlapping personal ontology and then finds similar users in the group. The experimental results show that the proposed method can save up to 96% of computation.
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CHU, HUI CHUN, and 曲惠君. "A Study on the Efficiency and Satisfaction of the Library Recommends System." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/63691369948340444917.

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碩士
國立臺灣師範大學
圖書資訊學研究所在職進修碩士班
99
In today's digital era, the library is also the purpose of business to be able to meet the needs of readers, and the library is the personal service has become an important research topic in recent years. Libraries in this research is based on recommendation system as the core of the system performance and user satisfaction assessment research. Readers training data to the source of the theme by hidden technology and social network analysis to explore (SNA) in the process, readers and readers to explore the similarity between the loan interest, information on Readers set the weights of the relevance of the level, readers to know the most adaptive book recommendations list. In addition, through the performance assessment system and reader satisfaction survey questionnaire, the results of two assessments produced by the middle of the gap, and find out how to take the initiative to explore the needs of readers, and provide the information readers need. Through this study, to explore the behavior of readers use the library, not only for library managers in decision-making collection development policies, recommended books, libraries can be provided in individual subject areas of more extensive and practical service performance.
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Videira, Jorge Diogo Fontanete. "Recommended KPIs to monitor and improve CMVM organizational performance." Master's thesis, 2019. http://hdl.handle.net/10400.14/29763.

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Performance management systems are a valuable tool that organizations can use to ensure that all the processes and activities are being fulfilled according to the predefined objectives. Once aligned with well-structure and precise key performance indicators (KPIs), it safeguards a positive long-lasting effect in the external effectiveness and internal efficiency of the firms. Being CMVM, an institution which regulates and oversees the Portuguese securities markets, it is indispensable for them to adopt measures and initiatives to monitor its performance. After scrutinizing the different departments and interviewing people from different hierarchical levels of the organization, this research identified six objectives which are explained throughout the paper. For each objective it is recommended and provided several KPIs retrieved from the most suitable and recognized financial regulators around the world. CMVM is expected to enhance its organizational performance by regularly monitoring and supervising its operations and activities through the KPIs presented in this research, given the ongoing need for benchmarking with the best financial authorities and institutions in the market.
Os sistemas de gestão de desempenho são uma ferramenta valiosa a que as organizações podem recorrer para garantir que todos os processos e atividades sejam cumpridos de acordo com os objetivos predefinidos. Quando alinhados e bem estruturados, os indicadores-chave de desempenho (KPIs), garantem um efeito positivo duradouro na eficácia externa e na eficiência interna das organizações. Para a CMVM, enquanto instituição que regula e supervisiona o mercado de valores mobiliários portugueses, é indispensável que estes incluam medidas e iniciativas para monitorizar o seu desempenho. Depois de estudar e analisar os diferentes departamentos, bem como tendo recorrido a entrevistas personalizadas em diferentes níveis hierárquicos da organização, esta investigação identificou seis objetivos que são explicados ao longo desta dissertação. Para cada objetivo é recomendado e são fornecidos vários KPIs inspirados nos mais eminentes e reconhecidos reguladores financeiros de todo o mundo. Espera-se assim que a CMVM, com recurso à utilização dos KPIs apresentados nesta pesquisa, possa, através da monitorização e supervisionando regularmente as suas operações e atividades, melhorar o seu desempenho organizacional, sem esquecer a necessidade contínua de benchmarking com as melhores práticas em instituições internacionais similares.
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Books on the topic "EFFICIENT RECOMMENDER SYSTEMS"

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Veshkurtsev, Yury. THE FOUNDATIONS OF THE THEORY OF CONSTRUCTION OF NEW-GENERATION MODEMS. au: AUS PUBLISHERS, 2022. http://dx.doi.org/10.26526/monography_628a8925151ca0.71125494.

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The monography presents the fundamentals of the theory of construction new-generation modems. Modems are built on the principles of statistical communication theory, based on the use of a random signal (chaos) as a carrier of information. In such a signal, a characteristic function is modulated, which is a fundamental characteristic of a random process. The signal modulation and demodulation method is patented and allows you to create modems with efficiency and noise immunity indicators several orders of magnitude higher than those of the known devices of the same name. New-generation modems immediately improve the technical characteristics of digital IT equipment by several orders of magnitude, since they work without errors in wired and radio channels when receiving one hundred duodecillion of binary symbols. The book is recommended for scientists and specialists in the field of digital communication systems, statistical radio engineering and instrumentation, and may be useful for graduate students, masters and students of relevant specialties.
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Connellan, Geoff. Water Use Efficiency for Irrigated Turf and Landscape. CSIRO Publishing, 2013. http://dx.doi.org/10.1071/9780643106888.

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Achieving high water use efficiency in maintaining turf, trees and landscape areas is a core responsibility of open space managers. Water Use Efficiency for Irrigated Turf and Landscape provides a logical and scientifically sound approach to irrigation in urban areas in Australia. It is based on green space delivering defined outcomes using the principles of water sensitive urban design and irrigation efficiency. The book covers all stages of the water pathway – from the source to delivery into the plant root zone. Major topics include system planning, estimating water demand, water quality, irrigation systems, soil management and irrigation performance evaluation. Clearly presented explanations are included, as well as line drawings and worked examples, and a plant water use database covering more than 250 plant species. A Water Management Planning template is included to guide water managers and operators through a process that will deliver a sound plan to achieve sustainable turf, urban trees and landscapes. Best Management Practice Irrigation principles are outlined and their implementation in open space turf and landscape situations is explained. The benefits and limitations of the various methods of delivering water to plants are covered, together with case studies and guidelines for specific horticultural situations. Methodologies to evaluate irrigated sites are included along with recommended benchmark values. The book presents the latest irrigation technology, including developments in water application, control technology and environmental sensors such as weather stations, soil moisture sensors and rain sensors.
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Book chapters on the topic "EFFICIENT RECOMMENDER SYSTEMS"

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Ishwarya, M. V., G. Swetha, S. Saptha Maaleekaa, and R. Anu Grahaa. "Efficient Recommender System by Implicit Emotion Prediction." In Advances in Intelligent Systems and Computing, 173–78. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1882-5_15.

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Kruglov, Artem, Giancarlo Succi, and Anna Gorb. "GQM and Recommender System for Relevant Metrics." In Developing Sustainable and Energy-Efficient Software Systems, 39–46. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-11658-2_4.

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AbstractAny software project needs metrics to measure closeness to the goal. However, it is a complicated task to identify appropriate and goal-focused metrics by hand. That is why this chapter presents the GQM-based metrics recommender—a recommender system that can automatically deduce metrics from the project goals and questions.
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Modarresi, Kourosh, and Jamie Diner. "An Efficient Deep Learning Model for Recommender Systems." In Lecture Notes in Computer Science, 221–33. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-93701-4_17.

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Gao, Yixu, Kun Shao, Zhijian Duan, Zhongyu Wei, Dong Li, Bin Wang, Mengchen Zhao, and Jianye Hao. "Efficient Dual-Process Cognitive Recommender Balancing Accuracy and Diversity." In Database Systems for Advanced Applications, 389–400. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-00129-1_33.

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Lunawat, Sonali Sagarmal, Abduttayyeb Rampurawala, Sneha Pujari, Siddhi Thawal, Jui Pangare, Chetana Thorat, and Bhushan Munot. "Efficient Recommender System for Kid’s Hobby Using Machine Learning." In Advances in Intelligent Systems and Computing, 327–36. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7088-6_29.

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Pitsilis, Georgios. "Trust-Enhanced Recommender Systems for Efficient On-Line Collaboration." In Trust Management III, 30–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02056-8_3.

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Kużelewska, Urszula. "Clustering Algorithms for Efficient Neighbourhood Identification in Session-Based Recommender Systems." In New Advances in Dependability of Networks and Systems, 143–52. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06746-4_14.

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Jeckmans, Arjan, Andreas Peter, and Pieter Hartel. "Efficient Privacy-Enhanced Familiarity-Based Recommender System." In Lecture Notes in Computer Science, 400–417. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-40203-6_23.

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Aymen, Ali Taleb Mohammed, and Saidi Imène. "Scientific Paper Recommender Systems: A Review." In Artificial Intelligence and Heuristics for Smart Energy Efficiency in Smart Cities, 896–906. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92038-8_92.

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Luo, Junwei, Xun Yi, Fengling Han, Xuechao Yang, and Xu Yang. "An Efficient Clustering-Based Privacy-Preserving Recommender System." In Network and System Security, 387–405. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-23020-2_22.

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Conference papers on the topic "EFFICIENT RECOMMENDER SYSTEMS"

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Bergemann, D., and D. Ozmen. "Efficient Recommender Systems." In Proceedings. Joint Conference 8th IEEE International Conference on e-Commerce and Technology/3rd IEEE International Conference on Enterprise Computing, e-Commerce and e-Services/3rd IEEE International Workshop on Mobile Commerce and Wireless Services/Joint Workshop 2nd International Workshop on Business Service Networks/2nd International Workshop On Service Oriented Solutions for Cooperative Organizations. IEEE, 2006. http://dx.doi.org/10.1109/cec-eee.2006.42.

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Yuan, Weiwei, Sungyoung Lee, Yongkoo Han, Donghai Guan, and Young-Koo Lee. "Efficient routing on finding recommenders for trust-aware recommender systems." In the 6th International Conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2184751.2184787.

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Kelen, Domokos M., Dániel Berecz, Ferenc Béres, and András A. Benczúr. "Efficient K-NN for Playlist Continuation." In the ACM Recommender Systems Challenge 2018. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3267471.3267477.

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Vu-Thi, Van, Dung Luong-The, and Quan Hoang-Van. "An efficient Privacy-Preserving Recommender System." In 2022 14th International Conference on Knowledge and Systems Engineering (KSE). IEEE, 2022. http://dx.doi.org/10.1109/kse56063.2022.9953800.

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Zhan, Justin, I.-Cheng Wang, Chia-Lung Hsieh, Tsan-Sheng Hsu, Churn-Jung Liau, and Da-Wei Wang. "Towards efficient privacy-preserving collaborative recommender systems." In 2008 IEEE International Conference on Granular Computing (GrC-2008). IEEE, 2008. http://dx.doi.org/10.1109/grc.2008.4664769.

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Faggioli, Guglielmo, Mirko Polato, and Fabio Aiolli. "Efficient Similarity Based Methods For The Playlist Continuation Task." In the ACM Recommender Systems Challenge 2018. New York, New York, USA: ACM Press, 2018. http://dx.doi.org/10.1145/3267471.3267486.

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Ribeiro, Marco Tulio, Anisio Lacerda, Adriano Veloso, and Nivio Ziviani. "Pareto-efficient hybridization for multi-objective recommender systems." In the sixth ACM conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2365952.2365962.

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Shahzad, Ahmad, and Frans Coenen. "Efficient Distributed MST Based Clustering for Recommender Systems." In 2020 International Conference on Data Mining Workshops (ICDMW). IEEE, 2020. http://dx.doi.org/10.1109/icdmw51313.2020.00037.

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Koutsopoulos, Iordanis, and Maria Halkidi. "Efficient and Fair Item Coverage in Recommender Systems." In 2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress(DASC/PiCom/DataCom/CyberSciTech). IEEE, 2018. http://dx.doi.org/10.1109/dasc/picom/datacom/cyberscitec.2018.000-9.

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Verhaert, Danilo, Majid Nateghizad, and Zekeriya Erkin. "An Efficient Privacy-preserving Recommender System for e-Healthcare Systems." In International Conference on Security and Cryptography. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006858501880199.

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Reports on the topic "EFFICIENT RECOMMENDER SYSTEMS"

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Wallace, Sean, Scott Lux, Constandinos Mitsingas, Irene Andsager, and Tapan Patel. Performance testing and modeling of a transpired ventilation preheat solar wall : performance evaluation of facilities at Fort Drum, NY, and Kansas Air National Guard, Topeka, KS. Engineer Research and Development Center (U.S.), September 2021. http://dx.doi.org/10.21079/11681/42000.

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This work performed measurement and verification of installed, operational solar wall systems at Fort Drum, NY, and Forbes Field, Air National Guard, Topeka, KS. Actual annual savings were compared estimated savings generated by a solar wall modeling tool (RETScreen). A comparison with the RETScreen modeling tool shows that the measured actively heated air provided by the solar wall provides 57% more heat than the RETScreen tool predicted, after accounting for boiler efficiency. The solar wall at Fort Drum yields a net savings of $851/yr, for a simple payback of 146 years and a SIR of 0.16. RETScreen models indicate that the solar wall system at Forbes Field, Kansas Air National Guard, Topeka, KS saves $9,350/yr, for a simple payback of 58.8 years and a SIR of 0.34. Although results showed that, due to low natural gas prices, the Fort Drum system was not economically viable, it was recommended that the system still be used to meet renewable energy and fossil fuel reduction goals. The current system becomes economical (SIR 1.00) at a natural gas rate of $16.00/MMBTU or $1.60 /therm.
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Urban, Angela, Ryan Strange, Andrew Ward, Giselle Rodriguez, and Heidi Howard. Waste management and landfill facilities assessment using unmanned aircraft systems. Engineer Research and Development Center (U.S.), March 2023. http://dx.doi.org/10.21079/11681/46714.

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Finite and decreasing landfill space on Army installations is a significant concern. Efficient waste management is essential for achieving resiliency and extending the lifespan of remaining landfills. The purpose of this demonstration was to conduct independent performance tests of small unmanned aircraft systems (sUAS) and their utility for providing landfill assessments in remote areas where physical presence is either dangerous or inefficient. An active, near capacity construction and demolition (C&D) landfill at Fort Gordon, Georgia, was identified for the demonstration. The flights, data requirements, and outputs generated by the sUAS flyovers were analyzed for efficacy in detecting cell capacity and subsidence. Each flight took 1–2 hours for mobilization, ground marker placement, flight, and postflight analysis. Volumetric and topographic surveys were analyzed in less time than is typical for traditional surveying methods. After initial setup of ground markers and rectification, sUAS flights save a significant amount of time. However, skilled individuals are required for flights and for processing and maintaining data. The technology is widely relevant to the Army, is commercially available, and offers an average of 30% cost savings in terms of manpower, repeatability, and equipment. The use of sUAS technology is recommended for monitoring and surveying Army landfills.
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Muelaner, Jody, ed. Unsettled Issues in Commercial Vehicle Platooning. SAE International, November 2021. http://dx.doi.org/10.4271/epr2021027.

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Platooning has the potential to reduce the energy consumption of commercial vehicles while improving safety; however, both advantages are currently difficult to quantify due to insufficient data and the wide range of variables affecting models. Platooning will significantly reduce the use of energy when compared to trucks driven alone, or at a safe distance for a driver without any automated assistance. Platooning will also reduce stopping distances—multiple states in the US have passed laws authorizing truck platoons to operate at shorter gaps than are authorized for normal, human-driven trucks. However, drivers typically do not currently leave the recommended gaps and, therefore, already gain much of the potential energy savings by drafting lead vehicles, albeit illegally. The automated systems associated with platooning cannot be programmed to flout safety recommendations in the way that human drivers routinely do. Therefore, actual energy savings may be minimal while safety may be greatly improved. More data will be needed to conclusively demonstrate a safety gain. Recommended safe gaps are currently highly generalized and must necessarily assume worst-case braking performance. Using a combination of condition monitoring and vehicle-to-vehicle communications, platooning systems will be able to account for the braking performance of other vehicles within the platoon. If all the vehicles in a platoon have a high level of braking performance, the platoon will be able to operate in a more efficient, tighter formation. Driver acceptance of platooning technology will increase as the systems become more effective and do not displace jobs. The increased loading of infrastructure must also be considered, and there may be requirements for upgrades on bridges or restrictions on platooning operation.
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Harangozó, Dániel. Croatia’s defence policy in the shadow of COVID-19 and the Russia-Ukraine war (2020-2023). Magyar Külügyi Intézet, 2023. http://dx.doi.org/10.47683/kkielemzesek.ke-2023.29.

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This paper reviews some of the current issues in Croatian military and defence policy in the period 2020-2023. It concentrates on three topics: defence spending trends and major defence modernization projects, the conflict between the Prime Minister and the President and its effects on the defence system, and Croatia’s reaction to the Russia-Ukraine war. The paper concludes that after a longer period of stagnation and decrease, defence spending has shown an increasing trend since 2020-2021. Defence spending as a share of GDP approaches, and the share of equipment expenditure within defence spending exceeds the levels recommended by NATO (2 and 20 percent, respectively). This is mainly the result of a one-off transaction, the purchase of French Dassault Rafale fighter aircraft. Due to the changed security situation in Europe after the outbreak of the Russia-Ukraine war, defence spending levels might remain elevated in the future as well. However, the continuous disagreements between the Prime Minister and the President in the defence and security sphere might pose a risk to efficient functioning in this field.
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Mashingia, Jane, S. Maboko, P. I. Mbwiri, A. Okello, S. I. Ahmada, R. Barayandema, R. Tulba, et al. Joint Medicines Regulatory Procedure in the East African Community: Registration Timelines and Way Forward. Purdue University, November 2021. http://dx.doi.org/10.5703/1288284317429.

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A review of the East African Community (EAC) joint regulatory review process was conducted, registration timelines analyzed and key milestones, challenges and opportunities documented for the period of July 2015 to January 2020. A total of 113 applications were submitted for joint scientific review. Among these, 109 applications were assessed, 57 were recommended for marketing authorisation, 52 applications had queries to applicants and four applications were under review. A total median approval time for all products ranged from 53 to 102 days. The maximum time taken by a regulator to review the dossier was 391 days and the minimum time was 44 days. For applicants, the maximum time to respond to queries was 927 days and the minimum time was nine days. The total median time for granting marketing authorisation by the National Medicines Regulatory Authorities (NMRA) decreased from 174 to 39 working days in 2015 and 2019 respectively. However, not all EAC NMRA has granted marketing authorisation to all 57 products due to non-payment of applicable fees by applicants. Long regulatory approval timelines were contributed by limited capacity for timely scientific review of dossier by some NMRA, lack of online portal to share dossiersand assessment reports, delay in responding to queries by applicants and deficiencies in dossier. The metric tool and register of medical products submitted for joint scientific review had incomplete data. Challenges were identified and actions recommended to ensure regional regulatory system optimization, efficiency, transparency, sustainability and accountability.
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Choquette, Gary. PR-000-16209-WEB Data Management Best Practices Learned from CEPM. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2019. http://dx.doi.org/10.55274/r0011568.

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DATE: Wednesday, May 1, 2019 TIME: 2:00 - 3:30 p.m. ET PRESENTER: Gary Choquette, PRCI CLICK DOWNLOAD/BUY TO ACCESS THE REGISTRATION LINK FOR THIS WEBINAR Systems that manage large sets of data are becoming more common in the energy transportation industry. Having access to the data offers the opportunity to learn from previous experiences to help efficiently manage the future. But how does one manage to digest copious quantities of data to find nuggets within the ore? This webinar will outline some of the data management best practices learned from the research projects associated with CEPM. - Logging/capturing data tips - Techniques to identify 'bad' data - Methods of mapping equipment and associated regressions - Tips for pre-processing data for regressions - Machine learning tips - Establishing alarm limits - Identifying equipment problems - Multiple case studies Who Should Attend? - Data analysts - Equipment support specialists - Those interested in learning more about 'big data' and 'machine learning' Recommended Pre-reading: - PR-309-11202-R01 Field Demonstration Test of Advanced Engine and Compressor Diagnostics for CORE - PR-312-12210-R01 CEPM Monitoring Plan for 2SLB Reciprocating Engines* - PR-309-13208-R01 Field Demonstration of Integrated System and Expert Level Continuous Performance Monitoring for CORE* - PR-309-14209-R01 Field Demo of Integrated Expert Level Continuous Performance Monitoring - PR-309-15205-R01 Continuous Engine Performance Monitoring Technical Specification - PR-000-15208-R01 Reciprocating Engine Speed Stability as a Measure of Combustion Stability - PR-309-15209-R01 Evaluation of NSCR Specific Models for Use in CEPM - PR-000-16209-R01 Demonstration of Continuous Equipment Performance Monitoring - PR-015-17606-Z02 Elbow Meter Test Results* *Documents available to PRCI member only Attendance will be limited to the first 500 registrants to join the webinar. All remaining registrants will receive a link to view the recording after the webinar. Not able to attend? Register anyway to automatically receive a link to the recording after the webinar to view at your convenience! After registering, you will receive a confirmation email containing information about joining the webinar. Please visit our website for other webinars that may be of interest to you!
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Kusiak, Chris, Mark D. Bowman, and Arun Prakash. Legal and Permit Loads Evaluation for Indiana Bridges. Purdue University, 2021. http://dx.doi.org/10.5703/1288284317267.

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According to federal law, routine commercial vehicles must adhere to certain limits on their load configuration in order to operate legally on interstate highways. However, states may allow for heavier or different load configurations provided that bridges on the state and county highway system are load rated and, if necessary, posted with vehicles that appropriately represent these loads. The state of Indiana allows several classes of vehicles to operate with loads that exceed federal limits, and, presently, several LFD design loads are used to represent these exceptions as state legal loads. This study evaluates the MBE rating loads for their ability to encompass Indiana’s exception vehicles and recommends a set of state rating loads which can replace the current state legal loads and, combined with the MBE rating loads, satisfactorily encompass the load effects due to these exceptions. Comparing moment and shear envelopes on a representative set of bridges, the MBE rating vehicles were found to be insufficient for representing Indiana’s exception vehicles. Three new rating loads are proposed which encompass the exception vehicles efficiently and represent realistic legal loads. Conversely, acceptable HS-20 rating factors are also provided as an alternative to the adoption of these new vehicles. These rating factors, all 1.0 or greater, can ensure a similar level of safety by requiring a specific amount of excess capacity for the HS-20 design load.
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Liu, Zhanjiang John, Rex Dunham, and Boaz Moav. Developmental and Evaluation of Advanced Expression Vectors with Both Enhanced Integration and Stable Expression for Transgenic Farmed Fish. United States Department of Agriculture, December 2001. http://dx.doi.org/10.32747/2001.7585196.bard.

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The objectives of the project were to develop expression vectors using the Sleeping Beauty transposon technology and the genetic border elements to provide both enhanced integration rate and stable transgene expression, and to evaluate the application of such vectors in farmed fish such as catfish and carp. The panel recommended adding the objective of evaluating the endogenous transposable elements, particularly in catfish, in order to evaluate the applicability of the expression vectors while reduc1ng efforts in real production of transgenic fish considering the focus of the project was to develop the vector and evaluation of its applicability, not producing transgenic fish. Efficient production of transgenic farmed fish is hindered by two major problems: mosaicism due to delayed integration after single-cell stage, and silencing of transgene expression. In this project, we proposed to combat these problems by coupling the Sleeping Beauty transposon technology that can enhance integration rate and the border elements that can insulate transgene from position effect. Our major objective was to develop a new generation of expression vector that contains both of these elements. We have developed expression vectors containing both the Sleeping Beauty transposon signals, inverted repeats and direct repeats (IR and DR, respectively), and the border elements, scs and scs'. Growth hormone minigene has been cloned into this vector for applications of such vectors in growth enhancement. Luc reporter gene has been also cloned into this vector cascades for relative easy evaluation of transgene expression. Transgenic fish have been produced using these expression vectors in both catfish (US) and carp (Israel). Much effort was also devoted to evaluation of the endogenous transposable elements in catfish as recommended by the BARD grant panel. Multiple families of Tcl-like transposons were identified from catfish. Surprisingly, many Tc I-related transcripts were identified. Among these transcripts, both the sense and antisense transcripts were present. Some of the transcripts may be useful for development of novel transposase-based technology for aquaculture applications in the future. This project has both scientific and aquaculture implications. First, to develop expression vectors containing both IR/DR and scs/scs' repeated elements have been reported being extremely technically difficult due to excision of the repeated sequences by the E. coli host during cloning processes. We have successfully constructed this advanced vector that contained very complex cascades for both gene integration and gene regulation. We have produced transgenic fish using such vectors. This advanced expression vector should be useful for production of transgenic fish. By simply replacing the growth hormone gene, any gene of interest can be readily inserted in this vector. Thus this vector should provide technological possibility for early integration and stable expression of any economically important genes in aquaculture. We have also evaluated the applications of the Sleeping Beauty-based vectors in terms of the impact of gene size and found that the size of trans gene drastically affects transposition. The system will be only useful for transferring genes smaller than 5.6 kb. We have also identified novel transposase-related transcripts that may be useful for the development of novel transposase-based technologies for general scientific research and for aquaculture applications.
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Dahl, Geoffrey E., Sameer Mabjeesh, Thomas B. McFadden, and Avi Shamay. Environmental manipulation during the dry period of ruminants: strategies to enhance subsequent lactation. United States Department of Agriculture, February 2006. http://dx.doi.org/10.32747/2006.7586544.bard.

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The project resulted from earlier observations that environmental factors, especially photoperiod and temperature, had profound effects on milk yield in dairy cattle during lactation. More recently we had determined that photoperiod manipulation during the dry period altered milk yield in the next lactation, and this was associated with shifts in circulating concentrations of prolactin; specifically exposure to short days during the dry period decreases prolactin but increases milk yield. Because prolactin is also affected by temperature, with heat stress causing an increase in prolactin similar to that of long day exposure, we focused our efforts on determining prolactin signaling provides a common pathway for generation of environmental effects on mammary growth, development and subsequent function during the dry period of dairy ruminants. Over the project period we made significant progress toward testing our hypotheses that (I): In cows, there is a discrete duration of time during the dry period in which exposure to short days will result in optimal enhancement of mammary development and milk yield in the following lactation, and that this effect is mediated through demonstrable changes in mammary gland development, prolactin signaling, and mammary gene expression; and (II): Modulation of photoperiod and temperature during the dry period will affect milk yield in goats in the subsequent lactation via shifts in nutrient and endocrine partitioning, and mammary gene expression, during the dry period and into lactation. Cows exposed to short days for only the final 21 days of the dry period did not produce more milk that those on long day or natural photoperiod when dry. However, cows on short days for the entire 60 days dry did produce more milk than the other 3 groups. This indicates that there is a duration effect of short day exposure on subsequent milk yield. Results of the second study in cows indicate that mammary growth increases differentially during the dry period under long vs. short days, and that short days drive more extensive growth which is associated with altered prolactin signaling via decreases in an suppressors of cytokine signaling that represent an inhibitory pathway to mammary growth. Evidence from the studies in Israel confirms that goats respond to short days during the dry period in a similar manner to cows. In addition, heat stress effects on during the dry period can be limited by exposure to short days. Here again, shifts in prolactin signaling, along with changes in IGF-I secretion, are associated with the observed changes in mammary function in goats. These results have a number of biological and practical implications. For dairy producers, it is clear that we can recommend that cows and goats should be on reduced light exposure during the dry period, and further, cows and goats should be cooled to avoid heat stress during that time. Environmental influences on mammary growth are apparent during the dry period, and those effects have persistent impact in the subsequent lactation. Prolactin signaling is a consistent mechanism whereby extended light exposure and heat stress may depress mammary growth and development during the dry period. Thus, the prolactin signaling system offers an opportunity for further manipulation to improve production efficiency in dairy ruminants.
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Daudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe, and Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, December 2021. http://dx.doi.org/10.53328/uxuo4751.

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The report provides a review of how risk is conceived of, modelled, and mapped in studies of infectious water, sanitation, and hygiene (WASH) related diseases. It focuses on spatial epidemiology of cholera, malaria and dengue to offer recommendations for the field of WASH-related disease risk mapping. The report notes a lack of consensus on the definition of disease risk in the literature, which limits the interpretability of the resulting analyses and could affect the quality of the design and direction of public health interventions. In addition, existing risk frameworks that consider disease incidence separately from community vulnerability have conceptual overlap in their components and conflate the probability and severity of disease risk into a single component. The report identifies four methods used to develop risk maps, i) observational, ii) index-based, iii) associative modelling and iv) mechanistic modelling. Observational methods are limited by a lack of historical data sets and their assumption that historical outcomes are representative of current and future risks. The more general index-based methods offer a highly flexible approach based on observed and modelled risks and can be used for partially qualitative or difficult-to-measure indicators, such as socioeconomic vulnerability. For multidimensional risk measures, indices representing different dimensions can be aggregated to form a composite index or be considered jointly without aggregation. The latter approach can distinguish between different types of disease risk such as outbreaks of high frequency/low intensity and low frequency/high intensity. Associative models, including machine learning and artificial intelligence (AI), are commonly used to measure current risk, future risk (short-term for early warning systems) or risk in areas with low data availability, but concerns about bias, privacy, trust, and accountability in algorithms can limit their application. In addition, they typically do not account for gender and demographic variables that allow risk analyses for different vulnerable groups. As an alternative, mechanistic models can be used for similar purposes as well as to create spatial measures of disease transmission efficiency or to model risk outcomes from hypothetical scenarios. Mechanistic models, however, are limited by their inability to capture locally specific transmission dynamics. The report recommends that future WASH-related disease risk mapping research: - Conceptualise risk as a function of the probability and severity of a disease risk event. Probability and severity can be disaggregated into sub-components. For outbreak-prone diseases, probability can be represented by a likelihood component while severity can be disaggregated into transmission and sensitivity sub-components, where sensitivity represents factors affecting health and socioeconomic outcomes of infection. -Employ jointly considered unaggregated indices to map multidimensional risk. Individual indices representing multiple dimensions of risk should be developed using a range of methods to take advantage of their relative strengths. -Develop and apply collaborative approaches with public health officials, development organizations and relevant stakeholders to identify appropriate interventions and priority levels for different types of risk, while ensuring the needs and values of users are met in an ethical and socially responsible manner. -Enhance identification of vulnerable populations by further disaggregating risk estimates and accounting for demographic and behavioural variables and using novel data sources such as big data and citizen science. This review is the first to focus solely on WASH-related disease risk mapping and modelling. The recommendations can be used as a guide for developing spatial epidemiology models in tandem with public health officials and to help detect and develop tailored responses to WASH-related disease outbreaks that meet the needs of vulnerable populations. The report’s main target audience is modellers, public health authorities and partners responsible for co-designing and implementing multi-sectoral health interventions, with a particular emphasis on facilitating the integration of health and WASH services delivery contributing to Sustainable Development Goals (SDG) 3 (good health and well-being) and 6 (clean water and sanitation).
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