Relevant bibliographies by topics / RECOMMENDATION TECHNIQUES

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'RECOMMENDATION TECHNIQUES'

Author: Grafiati
Published: 11 September 2023

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Journal articles on the topic "RECOMMENDATION TECHNIQUES"

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Dondekar, Anupama D., and Balwant A. Sonkamble. "Tag Recommendation Techniques for Images: A Survey." International Journal of Signal Processing Systems 5, no. 4 (December 2017): 116–22. http://dx.doi.org/10.18178/ijsps.5.4.116-122.

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Gorli, Ravi, and Bagusetty Ajay Ram. "MRML-Movie Recommendation Model with Machine Learning Techniques." International Journal of Science and Research (IJSR) 12, no. 5 (May 5, 2023): 298–302. http://dx.doi.org/10.21275/sr23322101301.

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TR, Mahesh, and V Vinoth Kumar. "Clustering Techniques for Recommendation of Movies." International Journal of Data Informatics and Intelligent Computing 1, no. 2 (December 21, 2022): 16–22. http://dx.doi.org/10.59461/ijdiic.v1i2.17.

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A recommendation system employs a variety of algorithms to provide users with recommendations of any kind. The most well-known technique, collaborative filtering, involves users with similar preferences although it is not always as effective when dealing with large amounts of data. Improvements to this approach are required as the dataset size increases. Here, in our suggested method, we combine a hierarchical clustering methodology with a collaborative filtering algorithm for making recommendations. Additionally, the Principle Component Analysis (PCA) method is used to condense the dimensions of the data to improve the accuracy of the outcomes. The dataset will receive additional benefits from the clustering technique when using hierarchical clustering, and the PCA will help redefine the dataset by reducing its dimensionality as needed. The primary elements utilized for recommendations can be enhanced by applying the key elements of these two strategies to the conventional collaborative filtering recommendation algorithm. The suggested method will unquestionably improve the precision of the findings received from the conventional CFRA and significantly increase the effectiveness of the recommendation system. The total findings will be applied to the combined dataset of TMDB and Movie Lens, which is utilized to suggest movies to the user in accordance with the rating patterns that each individual user has generated.
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Tewari, Anand Shanker, and Asim Gopal Barman. "Sequencing of items in personalized recommendations using multiple recommendation techniques." Expert Systems with Applications 97 (May 2018): 70–82. http://dx.doi.org/10.1016/j.eswa.2017.12.019.

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Al-Absi, Mohammed Abdulhakim, and Hind R’bigui. "Process Discovery Techniques Recommendation Framework." Electronics 12, no. 14 (July 17, 2023): 3108. http://dx.doi.org/10.3390/electronics12143108.

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In a competitive environment, organizations need to continuously understand, analyze and improve the behavior of processes to maintain their position in the market. Process mining is a set of techniques that allows organizations to have an X-ray view of their processes by extracting process related knowledge from the information recorded in today’s process aware information systems such as ‘Enterprise Resource Planning’ systems, ‘Business Process Management’ systems, ‘Supply Chain Management’ systems, etc. One of the major categories of process mining techniques is the process of discovery. This later allows for automatically constructing process models just from the information stored in the system representing the real behavior of the process discovered. Many process discovery algorithms have been proposed today which made users and businesses, in front of many techniques, unable to choose or decide the appropriate mining algorithm for their business processes. Moreover, existing evaluation and recommendation frameworks have several important drawbacks. This paper proposes a new framework for recommending the most suitable process discovery technique to a given process taking into consideration the limitations of existing frameworks.
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Gaurkhede, Miss Pratiksha P. "Review Paper on various Recommendation Techniques of Friends Recommendation System." International Journal for Research in Applied Science and Engineering Technology 9, no. 4 (April 30, 2021): 894–97. http://dx.doi.org/10.22214/ijraset.2021.33770.

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Nazema, Syeda. "A Survey on Feature Recommendation Techniques." International Journal on Recent and Innovation Trends in Computing and Communication 3, no. 3 (2015): 1662–68. http://dx.doi.org/10.17762/ijritcc2321-8169.1503167.

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Ko, Hyeyoung, Suyeon Lee, Yoonseo Park, and Anna Choi. "A Survey of Recommendation Systems: Recommendation Models, Techniques, and Application Fields." Electronics 11, no. 1 (January 3, 2022): 141. http://dx.doi.org/10.3390/electronics11010141.

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This paper reviews the research trends that link the advanced technical aspects of recommendation systems that are used in various service areas and the business aspects of these services. First, for a reliable analysis of recommendation models for recommendation systems, data mining technology, and related research by application service, more than 135 top-ranking articles and top-tier conferences published in Google Scholar between 2010 and 2021 were collected and reviewed. Based on this, studies on recommendation system models and the technology used in recommendation systems were systematized, and research trends by year were analyzed. In addition, the application service fields where recommendation systems were used were classified, and research on the recommendation system model and recommendation technique used in each field was analyzed. Furthermore, vast amounts of application service-related data used by recommendation systems were collected from 2010 to 2021 without taking the journal ranking into consideration and reviewed along with various recommendation system studies, as well as applied service field industry data. As a result of this study, it was found that the flow and quantitative growth of various detailed studies of recommendation systems interact with the business growth of the actual applied service field. While providing a comprehensive summary of recommendation systems, this study provides insight to many researchers interested in recommendation systems through the analysis of its various technologies and trends in the service field to which recommendation systems are applied.
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Das, Joydeep, Subhashis Majumder, and Kalyani Mali. "Clustering Techniques to Improve Scalability and Accuracy of Recommender Systems." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, no. 04 (August 2021): 621–51. http://dx.doi.org/10.1142/s0218488521500276.

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Recommender systems have emerged as a class of essential tools in the success of modern e-commerce applications. These applications typically handle large datasets and often face challenges like data sparsity and scalability. Clustering techniques help to reduce the computational time needed for recommendation as well as handle the sparsity problem more efficiently. Traditional clustering based recommender systems create partitions (clusters) of the user-item rating matrix and execute the recommendation algorithm in the clusters separately in order to decrease the overall runtime of the system. Each user or item generally belong to at most one cluster. However, it may so happen that some users (boundary users) present in a particular cluster exhibit higher similarity with the preferences of the users residing in the nearby clusters than the ones present in their own cluster. Therefore, we propose a clustering based scalable recommendation algorithm that has a provision for switching a user from its original cluster to another cluster in order to provide more accurate recommendations. For a user belonging to multiple clusters, we aggregate recommendations from those clusters to which the user belongs in order to produce the final set of recommendations to that user. In this work, we propose two types of clustering, one on the basis of rating and the other on the basis of frequency and then compare their performances. Finally, we explore the applicability of cluster ensembles techniques in the proposed method. Our aim is to develop a recommendation framework that can scale well to handle large datasets without much affecting the recommendation quality. The outcomes of our experiments clearly demonstrate the scalability as well as efficacy of our method. It reduces the runtime of the baseline CF algorithm by a minimum of 58% and a maximum of 90% for MovieLens-10M dataset, and a minimum of 42% and a maximum of 84% for MovieLens-20M dataset. The accuracies of recommendations in terms of F1, MAP and NDCG metrics are also better than the existing clustering based recommender systems.
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Kumar, Praveen, Mukesh Kumar Gupta, Channapragada Rama Seshagiri Rao, M. Bhavsingh, and M. Srilakshmi. "A Comparative Analysis of Collaborative Filtering Similarity Measurements for Recommendation Systems." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 3s (March 11, 2023): 184–92. http://dx.doi.org/10.17762/ijritcc.v11i3s.6180.

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Collaborative Filtering (CF) is a widely used technique in recommendation systems to suggest items to users based on their previous interactions with the system. CF involves finding correlations between the preferences of different users and using those correlations to provide recommendations. This technique can be divided into user-based and item-based CF, both of which utilize similarity metrics to generate recommendations. Content-based filtering is another commonly used recommendation technique that analyzes the attributes of items to suggest similar items. To enhance the accuracy of recommendation systems, hybrid algorithms that combine CF and content-based filtering techniques have been developed. These hybrid systems leverage the strengths of both approaches to provide more accurate and personalized recommendations. In conclusion, collaborative filtering is an essential technique in recommendation systems, and the use of various similarity metrics and hybrid techniques can enhance the quality of recommendations.
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Dissertations / Theses on the topic "RECOMMENDATION TECHNIQUES"

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Martins, Diogo Marques. "Trajectory clustering techniques with application to route recommendation." Master's thesis, Universidade de Aveiro, 2015. http://hdl.handle.net/10773/18581.

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Mestrado em Engenharia de Computadores e Telemática
O uso generalizado de dispositivos capazes de obter e transmitir dados sobre a localização de objetos ao longo do tempo tem permitido recolher grandes volumes de dados espácio-temporais. Por isso, tem-se assistido a uma procura crescente de técnicas e ferramentas para a análise de grandes volumes de dados espácio-temporais com o intuito de disponibilizar uma gama variada de serviços baseados na localização. Esta dissertação centra-se no desenvolvimento de um sistema para recomendaSr trajetos com base em dados históricos sobre a localização de objetos móveis ao longo do tempo. O principal problema estudado neste trabalho consiste no agrupamento de trajetórias e na extração de informação a partir dos grupos de trajetórias. Este estudo, não se restringe a dados provenientes apenas de veículos, podendo ser aplicado a outros tipos de trajetórias, por exemplo, percursos realizados por pessoas a pé ou de bicicleta. O agrupamento baseia-se numa medida de similaridade. A extração de informação consiste em criar uma trajetória representativa para cada grupo de trajetórias. As trajetórias representativas podem ser visualizadas usando uma aplicação web, sendo também possível configurar cada módulo do sistema com parâmetros desejáveis, na sua maioria distâncias limiares. Por fim, são apresentados casos de teste para avaliar o desempenho global do sistema desenvolvido.
The widespread use of devices to capture and transmit data about the location of objects over time allows collecting large volumes of spatio-temporal data. Consequently, there has been in recent years a growing demand for tools and techniques to analyze large volumes of spatio-temporal data aiming at providing a wide range of location-based services. This dissertation focuses on the development of a system for recommendation of trajectories based on historical data about the location of moving objects over time. The main issues covered in this work are trajectory clustering and extracting information from trajectory clusters. This study is not restricted to data from vehicles and can also be applied to other kinds of trajectories, for example, the movement of runners or bikes. The clustering is based on a similarity measure. The information extraction consists in creating a representative trajectory for the trajectories clusters. Finally, representative trajectories are displayed using a web application and it is also possible to configure each system module with desired parameters, mostly distance thresholds. Finally, case studies are presented to evaluate the developed system.
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Vahabi, Hossein. "Recommendation techniques for Web search and social media." Thesis, IMT Alti Studi Lucca, 2012. http://e-theses.imtlucca.it/86/1/Vahabi_phdthesis.pdf.

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Recommenders aim to solve the information and interaction overload. A recommender system is a tool that identifies highly relevant items of interest for a user. In this thesis we aim to harness the information available through Web to build recommender systems. In particularwe study on a number of different recommendation problems: "correct tag recommendation", "query recommendation", "tweet recommendation", and "tourist points of interest recommendation". For each problem, we describe and evaluate effective solutions using novel efficient techniques.
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Alabdulrahman, Rabaa. "Towards Personalized Recommendation Systems: Domain-Driven Machine Learning Techniques and Frameworks." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41012.

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Recommendation systems have been widely utilized in e-commerce settings to aid users through their shopping experiences. The principal advantage of these systems is their ability to narrow down the purchase options in addition to marketing items to customers. However, a number of challenges remain, notably those related to obtaining a clearer understanding of users, their profiles, and their preferences in terms of purchased items. Specifically, recommender systems based on collaborative filtering recommend items that have been rated by other users with preferences similar to those of the targeted users. Intuitively, the more information and ratings collected about the user, the more accurate are the recommendations such systems suggest. In a typical recommender systems database, the data are sparse. Sparsity occurs when the number of ratings obtained by the users is much lower than the number required to build a prediction model. This usually occurs because of the users’ reluctance to share their reviews, either due to privacy issues or an unwillingness to make the extra effort. Grey-sheep users pose another challenge. These are users who shared their reviews and ratings yet disagree with the majority in the systems. The current state-of-the-art typically treats these users as outliers and removes them from the system. Our goal is to determine whether keeping these users in the system may benefit learning. Thirdly, cold-start problems refer to the scenario whereby a new item or user enters the system and is another area of active research. In this case, the system will have no information about the new user or item, making it problematic to find a correlation with others in the system. This thesis addresses the three above-mentioned research challenges through the development of machine learning methods for use within the recommendation system setting. First, we focus on the label and data sparsity though the development of the Hybrid Cluster analysis and Classification learning (HCC-Learn) framework, combining supervised and unsupervised learning methods. We show that combining classification algorithms such as k-nearest neighbors and ensembles based on feature subspaces with cluster analysis algorithms such as expectation maximization, hierarchical clustering, canopy, k-means, and cascade k-means methods, generally produces high-quality results when applied to benchmark datasets. That is, cluster analysis clearly benefits the learning process, leading to high predictive accuracies for existing users. Second, to address the cold-start problem, we present the Popular Users Personalized Predictions (PUPP-DA) framework. This framework combines cluster analysis and active learning, or so-called user-in-the-loop, to assign new customers to the most appropriate groups in our framework. Based on our findings from the HCC-Learn framework, we employ the expectation maximization soft clustering technique to create our user segmentations in the PUPP-DA framework, and we further incorporate Convolutional Neural Networks into our design. Our results show the benefits of user segmentation based on soft clustering and the use of active learning to improve predictions for new users. Furthermore, our findings show that focusing on frequent or popular users clearly improves classification accuracy. In addition, we demonstrate that deep learning outperforms machine learning techniques, notably resulting in more accurate predictions for individual users. Thirdly, we address the grey-sheep problem in our Grey-sheep One-class Recommendations (GSOR) framework. The existence of grey-sheep users in the system results in a class imbalance whereby the majority of users will belong to one class and a small portion (grey-sheep users) will fall into the minority class. In this framework, we use one-class classification to provide a class structure for the training examples. As a pre-assessment stage, we assess the characteristics of grey-sheep users and study their impact on model accuracy. Next, as mentioned above, we utilize one-class learning, whereby we focus on the majority class to first learn the decision boundary in order to generate prediction lists for the grey-sheep (minority class). Our results indicate that including grey-sheep users in the training step, as opposed to treating them as outliers and removing them prior to learning, has a positive impact on the general predictive accuracy.
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Aleksandra, Klašnja-Milićević. "Personalized Recommendation Based on Collaborative Tagging Techniques for an e‐Learning System." Phd thesis, Univerzitet u Novom Sadu, Prirodno-matematički fakultet u Novom Sadu, 2013. https://www.cris.uns.ac.rs/record.jsf?recordId=83535&source=NDLTD&language=en.

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The  research  topic  involves  personalization  of  an  e‐learning  system  based  oncollaborative  tagging  techniques  integrated  in  a  recommender  system.  Collaborative  tagging systems allow users to upload their resources, and to label them with arbitrary words, so‐called tags.  The  systems  can  be  distinguished  according  to  what  kind  of  resources  are  supported. Besides helping user to organize his or her personal collections, a tag also can be regarded as a user’s personal opinion expression. The increasing number of users providing information about themselves  through  social  tagging  activities  caused  the  emergence  of  tag‐based  profilingapproaches, which assume that users expose their preferences for certain contents through tag assignments. Thus, the tagging information can be used to make recommendations. Dissertation  research  aims  to  analyze  and  define  an  enhanced  model  to  select  tags  that  reveal the preferences and characteristics of users required to generate personalized recommendations. Options  on  the  use  of  models  for  personalized  tutoring  system  were  also  considered. Personalized  learning  occurs  when  e‐learning  systems  make  deliberate  efforts  to  design educational  experiences  that  fit  the  needs,  goals,  talents,  learning  styles,  interests  of  theirlearners  and  learners  with  similar  characteristics.  In  practice,  models  defined  in  the dissertation were evaluated on tutoring system for teaching Java programming language.
Predmet istraživanja disertacije obuhvata personalizaciju tutorskih sistema za elektronsko učenje primenom tehnika kolaborativnog tagovanja (collaborative tagging techniques) integrisanih u sisteme za generisanje preporuka (recommender systems). Tagovi, kao oblik meta podataka, predstavljaju proizvoljne ključne reči ili fraze koje korisnik može da upotrebi za označavanje različitih sadržaja. Pored toga što tagovi korisnicima pružaju pomoć u organizaciji sadržaja, oni su korisni i u izražavanju mišljenja korisnika. Veliki broj informacija koje korisnici pružaju o sebi kroz aktivnosti tagovanja otvorio je mogućnost primene tagova u generisanju preporuka. Istraživanje disertacije je usmereno na analizu i definisanje poboljšanih modela za odabir tagova koji otkrivaju sklonosti i osobine korisnika potrebne za generisanje personalizovanih preporuka. Razmatrane su i mogućnosti primene tako dobijenih modela za personalizaciju tutorskih sistema. Personalizovani tutorski sistemi korisniku pružaju optimalne putanje kretanja i adekvatne aktivnosti učenja na osnovu njegovih osobina, njegovog stila učenja, znanja koje on poseduje u toj oblasti, kao i prethodnog iskustva korisnika sistema koji imaju slične karakteristike. Modeli definisani u disertaciji u praksi su evaluirani na tutorskom sistemu za učenje programskog jezika Java.
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Nagi, Mohamad. "Integrating Network Analysis and Data Mining Techniques into Effective Framework for Web Mining and Recommendation. A Framework for Web Mining and Recommendation." Thesis, University of Bradford, 2015. http://hdl.handle.net/10454/14200.

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The main motivation for the study described in this dissertation is to benefit from the development in technology and the huge amount of available data which can be easily captured, stored and maintained electronically. We concentrate on Web usage (i.e., log) mining and Web structure mining. Analysing Web log data will reveal valuable feedback reflecting how effective the current structure of a web site is and to help the owner of a web site in understanding the behaviour of the web site visitors. We developed a framework that integrates statistical analysis, frequent pattern mining, clustering, classification and network construction and analysis. We concentrated on the statistical data related to the visitors and how they surf and pass through the various pages of a given web site to land at some target pages. Further, the frequent pattern mining technique was used to study the relationship between the various pages constituting a given web site. Clustering is used to study the similarity of users and pages. Classification suggests a target class for a given new entity by comparing the characteristics of the new entity to those of the known classes. Network construction and analysis is also employed to identify and investigate the links between the various pages constituting a Web site by constructing a network based on the frequency of access to the Web pages such that pages get linked in the network if they are identified in the result of the frequent pattern mining process as frequently accessed together. The knowledge discovered by analysing a web site and its related data should be considered valuable for online shoppers and commercial web site owners. Benefitting from the outcome of the study, a recommendation system was developed to suggest pages to visitors based on their profiles as compared to similar profiles of other visitors. The conducted experiments using popular datasets demonstrate the applicability and effectiveness of the proposed framework for Web mining and recommendation. As a by product of the proposed method, we demonstrate how it is effective in another domain for feature reduction by concentrating on gene expression data analysis as an application with some interesting results reported in Chapter 5.
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Guerrini, Gabriele. "Analysis, design and implementation of a parking recommendation system applying machine learning techniques." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23162/.

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Nowadays, smart city and urban mobility topics are focusing growing interest for business and R\&D purposes both, and one of its trendy application regards the study and the realization of parking recommendation systems. In our case, a parking recommendation system is going to be developed for the city of Bologna. After a brief revision of similar proposed solutions and their approaches and findings, a first data analysis phase is going to be executed to explore the data and to mine any useful information about the parking behavior. Then, once the adopted forecasting model has been exposed, the design and the development of the system are going to be described. In the end, the performances of the system are going to be evaluated under multiple points of view, considering some project-specific constraints too.
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Cabir, Hassane Natu Hassane. "A Comparison Of Different Recommendation Techniques For A Hybrid Mobile Game Recommender System." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12615173/index.pdf.

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As information continues to grow at a very fast pace, our ability to access this information effectively does not, and we are often realize how harder is getting to locate an object quickly and easily. The so-called personalization technology is one of the best solutions to this information overload problem: by automatically learning the user profile, personalized information services have the potential to offer users a more proactive and intelligent form of information access that is designed to assist us in finding interesting objects. Recommender systems, which have emerged as a solution to minimize the problem of information overload, provide us with recommendations of content suited to our needs. In order to provide recommendations as close as possible to a user&rsquo
s taste, personalized recommender systems require accurate user models of characteristics, preferences and needs. Collaborative filtering is a widely accepted technique to provide recommendations based on ratings of similar users, But it suffers from several issues like data sparsity and cold start. In one-class collaborative filtering, a special type of collaborative filtering methods that aims to deal with datasets that lack counter-examples, the challenge is even greater, since these datasets are even sparser. In this thesis, we present a series of experiments conducted on a real-life customer purchase database from a major Turkish E-Commerce site. The sparsity problem is handled by the use of content-based technique combined with TFIDF weights, memory based collaborative filtering combined with different similarity measures and also hybrids approaches, and also model based collaborative filtering with the use of Singular Value Decomposition (SVD). Our study showed that the binary similarity measure and SVD outperform conventional measures in this OCCF dataset.
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Videla, Cavieres Iván Fernando. "Improvement of recommendation system for a wholesale store chain using advanced data mining techniques." Tesis, Universidad de Chile, 2015. http://repositorio.uchile.cl/handle/2250/133522.

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Magíster en Gestión de Operaciones
Ingeniero Civil Industrial
En las empresas de Retail, las áreas de Customer Intelligence tienen muchas oportunidades de mejorar sus decisiones estratégicas a partir de la información que podrían obtener de los registros de interacciones con sus clientes. Sin embargo se ha convertido en un desafío poder procesar estos grandes volúmenes de datos. Uno de los problemas que se enfrentan día a día es segmentar o agrupar clientes. La mayoría de las empresas generan agrupaciones según nivel de gasto, no por similitud en sus canastas de compra, como propone la literatura. Otro desafío de estas empresas es aumentar las ventas en cada visita del cliente y fidelizar. Una de las técnicas utilizadas para lograrlo es usar sistemas de recomendación. En este trabajo se proceso ́ alrededor de medio billón de registros transaccionales de una cadena de supermercados mayorista. Al aplicar las técnicas tradicionales de Clustering y Market Basket Analysis los resultados son de baja calidad, haciendo muy difícil la interpretación, además no se logra identificar grupos que permitan clasificar a un cliente de acuerdo a sus compras históricas. Entendiendo que la presencia simultánea de dos productos en una misma boleta implica una relación entre ellos, se usó un método de graph mining basado en redes sociales que permitió obtener grupos de productos identificables que denominamos comunidades, a las que puede pertenecer un cliente. La robustez del modelo se comprueba por la estabilidad de los grupos generados en distintos periodos de tiempo. Bajo las mismas restricciones que la empresa exige, se generan recomendaciones basadas en las compras históricas y en la pertenencia de los clientes a los distintos grupos de productos. De esta manera, los clientes reciben recomendaciones mucho más pertinentes y no solo son basadas en los que otros clientes también compraron. La novedosa forma de resolver el problema de segmentar clientes ayuda a mejorar en un 140% el actual método de recomendaciones que utiliza la cadena Chilena de supermercados mayoristas. Esto se traduce en un aumento de más de 430% de los ingresos posibles.
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Holländer, John. "Investigating the performance of matrix factorization techniques applied on purchase data for recommendation purposes." Thesis, Malmö högskola, Fakulteten för teknik och samhälle (TS), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-20624.

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Automated systems for producing product recommendations to users is a relatively new area within the field of machine learning. Matrix factorization techniques have been studied to a large extent on data consisting of explicit feedback such as ratings, but to a lesser extent on implicit feedback data consisting of for example purchases.The aim of this study is to investigate how well matrix factorization techniques perform compared to other techniques when used for producing recommendations based on purchase data. We conducted experiments on data from an online bookstore as well as an online fashion store, by running algorithms processing the data and using evaluation metrics to compare the results. We present results proving that for many types of implicit feedback data, matrix factorization techniques are inferior to various neighborhood- and association rules techniques for producing product recommendations. We also present a variant of a user-based neighborhood recommender system algorithm \textit{(UserNN)}, which in all tests we ran outperformed both the matrix factorization algorithms and the k-nearest neighbors algorithm regarding both accuracy and speed. Depending on what dataset was used, the UserNN achieved a precision approximately 2-22 percentage points higher than those of the matrix factorization algorithms, and 2 percentage points higher than the k-nearest neighbors algorithm. The UserNN also outperformed the other algorithms regarding speed, with time consumptions 3.5-5 less than those of the k-nearest neighbors algorithm, and several orders of magnitude less than those of the matrix factorization algorithms.
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Bambia, Meriam. "Jointly integrating current context and social influence for improving recommendation." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30110/document.

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La diversité des contenus recommandation et la variation des contextes des utilisateurs rendent la prédiction en temps réel des préférences des utilisateurs de plus en plus difficile mettre en place. Toutefois, la plupart des approches existantes n'utilisent que le temps et l'emplacement actuels séparément et ignorent d'autres informations contextuelles sur lesquelles dépendent incontestablement les préférences des utilisateurs (par exemple, la météo, l'occasion). En outre, ils ne parviennent pas considérer conjointement ces informations contextuelles avec les interactions sociales entre les utilisateurs. D'autre part, la résolution de problèmes classiques de recommandation (par exemple, aucun programme de télévision vu par un nouvel utilisateur connu sous le nom du problème de démarrage froid et pas assez d'items co-évalués par d'autres utilisateurs ayant des préférences similaires, connu sous le nom du problème de manque de donnes) est d'importance significative puisque sont attaqués par plusieurs travaux. Dans notre travail de thèse, nous proposons un modèle probabiliste qui permet exploiter conjointement les informations contextuelles actuelles et l'influence sociale afin d'améliorer la recommandation des items. En particulier, le modèle probabiliste vise prédire la pertinence de contenu pour un utilisateur en fonction de son contexte actuel et de son influence sociale. Nous avons considérer plusieurs éléments du contexte actuel des utilisateurs tels que l'occasion, le jour de la semaine, la localisation et la météo. Nous avons utilisé la technique de lissage Laplace afin d'éviter les fortes probabilités. D'autre part, nous supposons que l'information provenant des relations sociales a une influence potentielle sur les préférences des utilisateurs. Ainsi, nous supposons que l'influence sociale dépend non seulement des évaluations des amis mais aussi de la similarité sociale entre les utilisateurs. Les similarités sociales utilisateur-ami peuvent être établies en fonction des interactions sociales entre les utilisateurs et leurs amis (par exemple les recommandations, les tags, les commentaires). Nous proposons alors de prendre en compte l'influence sociale en fonction de la mesure de similarité utilisateur-ami afin d'estimer les préférences des utilisateurs. Nous avons mené une série d'expérimentations en utilisant un ensemble de donnes réelles issues de la plateforme de TV sociale Pinhole. Cet ensemble de donnes inclut les historiques d'accès des utilisateurs-vidéos et les réseaux sociaux des téléspectateurs. En outre, nous collectons des informations contextuelles pour chaque historique d'accès utilisateur-vidéo saisi par le système de formulaire plat. Le système de la plateforme capture et enregistre les dernières informations contextuelles auxquelles le spectateur est confronté en regardant une telle vidéo.Dans notre évaluation, nous adoptons le filtrage collaboratif axé sur le temps, le profil dépendant du temps et la factorisation de la matrice axe sur le réseau social comme tant des modèles de référence. L'évaluation a port sur deux tâches de recommandation. La première consiste sélectionner une liste trie de vidéos. La seconde est la tâche de prédiction de la cote vidéo. Nous avons évalué l'impact de chaque élément du contexte de visualisation dans la performance de prédiction. Nous testons ainsi la capacité de notre modèle résoudre le problème de manque de données et le problème de recommandation de démarrage froid du téléspectateur. Les résultats expérimentaux démontrent que notre modèle surpasse les approches de l'état de l'art fondes sur le facteur temps et sur les réseaux sociaux. Dans les tests des problèmes de manque de donnes et de démarrage froid, notre modèle renvoie des prédictions cohérentes différentes valeurs de manque de données
Due to the diversity of alternative contents to choose and the change of users' preferences, real-time prediction of users' preferences in certain users' circumstances becomes increasingly hard for recommender systems. However, most existing context-aware approaches use only current time and location separately, and ignore other contextual information on which users' preferences may undoubtedly depend (e.g. weather, occasion). Furthermore, they fail to jointly consider these contextual information with social interactions between users. On the other hand, solving classic recommender problems (e.g. no seen items by a new user known as cold start problem, and no enough co-rated items with other users with similar preference as sparsity problem) is of significance importance since it is drawn by several works. In our thesis work, we propose a context-based approach that leverages jointly current contextual information and social influence in order to improve items recommendation. In particular, we propose a probabilistic model that aims to predict the relevance of items in respect with the user's current context. We considered several current context elements such as time, location, occasion, week day, location and weather. In order to avoid strong probabilities which leads to sparsity problem, we used Laplace smoothing technique. On the other hand, we argue that information from social relationships has potential influence on users' preferences. Thus, we assume that social influence depends not only on friends' ratings but also on social similarity between users. We proposed a social-based model that estimates the relevance of an item in respect with the social influence around the user on the relevance of this item. The user-friend social similarity information may be established based on social interactions between users and their friends (e.g. recommendations, tags, comments). Therefore, we argue that social similarity could be integrated using a similarity measure. Social influence is then jointly integrated based on user-friend similarity measure in order to estimate users' preferences. We conducted a comprehensive effectiveness evaluation on real dataset crawled from Pinhole social TV platform. This dataset includes viewer-video accessing history and viewers' friendship networks. In addition, we collected contextual information for each viewer-video accessing history captured by the plat form system. The platform system captures and records the last contextual information to which the viewer is faced while watching such a video. In our evaluation, we adopt Time-aware Collaborative Filtering, Time-Dependent Profile and Social Network-aware Matrix Factorization as baseline models. The evaluation focused on two recommendation tasks. The first one is the video list recommendation task and the second one is video rating prediction task. We evaluated the impact of each viewing context element in prediction performance. We tested the ability of our model to solve data sparsity and viewer cold start recommendation problems. The experimental results highlighted the effectiveness of our model compared to the considered baselines. Experimental results demonstrate that our approach outperforms time-aware and social network-based approaches. In the sparsity and cold start tests, our approach returns consistently accurate predictions at different values of data sparsity
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Books on the topic "RECOMMENDATION TECHNIQUES"

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Obuhova, Galina, and Galina Klimova. Fundamentals of public communication skills: practical recommendations. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1090527.

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The textbook discusses the methodological foundations of the preparation and presentation of public speeches in various fields of activity, including business. The factors influencing the skill of the speaker are considered. Recommendations are given on the technique of conducting various types of public speeches and practical techniques of audience ownership are shown. Special attention is paid to the methods of establishing contact between the speaker and the audience and the psychological influence of the speaker on the audience. Practical recommendations and exercises for improving the speaker's speech technique are presented. Special attention is paid to the ways of correcting speech defects. Meets the requirements of the federal state educational standards of higher education of the latest generation. It is intended for teachers, lecturers of the educational system, students and postgraduates, managers, lawyers, as well as for managers of various levels who are aware of the importance of verbal communication in their professional field. It can also be useful for a wide range of readers.
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Council of Europe. Committee of Ministers. Special investigation techniques in relation to serious crimes including acts of terrorism: Recommendation Rec(2005)10 adopted by the Committee of Ministers of the Council of Europe on 20 April 2005 and explanatory memorandum. Strasbourg: Council of Europe Publishing, 2005.

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Wiggers, Willem J. H. Drafting contracts: Techniques, best practice rules and recommendations related to contract drafting. Deventer: Kluwer, 2011.

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author, Maslov D. E., and Russia (Federation). Ministerstvo vnutrennikh del. Nizhegorodskai︠a︡ akademii︠a︡, eds. I︠U︡ridicheskai︠a︡ rekomendat︠s︡ii︠a︡: Doktrina, praktika, tekhnika : monografii︠a︡ = Legal Recommendation : Doctrine, Practice, Technique : Monograph. Moskva: Prospekt, 2021.

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International Telegraph and Telephone Consultative Committee. Plenary Assembly. Red book.: Open systems interconnection (OSI) system description techniques : recommendations X.200-X.250. Geneva: International Telecommunication Union, 1985.

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Rhodes, Tina Gabriele. Investigation of a relaxation technique: Personal control expectancies and adherence to practice recommendations. Roehampton: University of Surrey Roehampton, 2002.

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Guide to integrating forensic techniques into incident response: Recommendations of the National Institute of Standards and Technology. Gaithersburg, MD: U.S. Dept. of Commerce, Technology Administration, National Institute of Standards and Technology, 2006.

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World Health Organization (WHO). WHO recommendations on rabies post-exposure treatment and the correct technique of intradermal immunization against rabies. London: Stationery Office, 1999.

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United States. Dept. of Housing and Urban Development. Office of Policy Development and Research., ed. Manufactured homes: Saving money by saving energy : energy-saving tips, techniques and recommendations for owners of manufactured (mobile) homes. [Washington, D.C: U.S. Dept. of Housing and Urban Development, 2005.

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Beloshistaya, Anna. Mathematics in primary school: teaching methods. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1070170.

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The textbook contains methodological information and recommendations for the course of mathematics, which is studied in elementary school. All types of tasks, computational techniques and computational actions, typical and non-typical tasks, and techniques for working with them are given. The content of the textbook is focused on the mandatory minimum of primary education, current programs and current textbooks. The article presents universal methodological information related to any of the modern systems of teaching mathematics in primary classes. Meets the requirements of the federal state standards of secondary vocational education of the latest generation. It is addressed to students of institutions of secondary vocational education in the specialty "Teaching in primary classes".
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Book chapters on the topic "RECOMMENDATION TECHNIQUES"

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Jingxian, Huang. "Research on Intelligent Recommendation Method of e-commerce Hot Information Based on User Interest Recommendation." In Simulation Tools and Techniques, 153–66. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72795-6_13.

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Raghuwanshi, Sandeep K., and R. K. Pateriya. "Collaborative Filtering Techniques in Recommendation Systems." In Data, Engineering and Applications, 11–21. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-6347-4_2.

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Jiang, Feng, Min Gao, Qingyu Xiong, Junhao Wen, and Yi Zhang. "Robust Social Recommendation Techniques: A Review." In Socially Aware Organisations and Technologies. Impact and Challenges, 53–58. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42102-5_6.

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Singh, Sanika, Aman Anand, Tanupriya Choudhury, Pankaj Sharma, and Ved P. Mishra. "Extensive Review on Product Recommendation Techniques." In Data Driven Approach Towards Disruptive Technologies, 549–58. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9873-9_43.

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Acharya, Soumya S., Nandita Nupur, Priyabrat Sahoo, and Paresh Baidya. "Mood-Based Movie Recommendation System." In Biologically Inspired Techniques in Many Criteria Decision Making, 151–58. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8739-6_13.

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Wang, Xiaofeng, Dongming Tang, Hui Zheng, and Ke Zhang. "Study and Implementation of Minority Mobile Application Recommendation Software." In Simulation Tools and Techniques, 559–66. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32216-8_54.

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Radja, Priyanka. "Personalized Recommendation Techniques in Social Tagging Systems." In Soft Computing Systems, 35–45. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1936-5_4.

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Kaur, Kamaljit, and Kanwalvir Singh Dhindsa. "Classification of Followee Recommendation Techniques in Twitter." In Advances in Intelligent Systems and Computing, 527–40. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-29516-5_41.

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Dhanalakshmi, P., P. Dileep Kumar Reddy, Sasikumar Gurumurthy, and K. Lalitha. "Web User Clustering Techniques for Recommendation Systems." In Lecture Notes in Electrical Engineering, 1885–95. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1420-3_192.

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Raghuwanshi, Sandeep K., and R. K. Pateriya. "Recommendation Systems: Techniques, Challenges, Application, and Evaluation." In Advances in Intelligent Systems and Computing, 151–64. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1595-4_12.

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Conference papers on the topic "RECOMMENDATION TECHNIQUES"

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Melchiori, Michele. "Hybrid techniques for web APIs recommendation." In the 1st International Workshop. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1966901.1966905.

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D'cunha, Arlina, and Vandana Patil. "Friend recommendation techniques in social network." In 2015 International Conference on Communication, Information & Computing Technology (ICCICT). IEEE, 2015. http://dx.doi.org/10.1109/iccict.2015.7045669.

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Todkar, Omkar, S. Z. Gawali, and Aniket D. Kadam. "Recommendation engine feedback session strategy for mapping user search goals (FFS: Recommendation system)." In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT). IEEE, 2016. http://dx.doi.org/10.1109/iceeot.2016.7755581.

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Koetphrom, Nanthaphat, Panachai Charusangvittaya, and Daricha Sutivong. "Comparing Filtering Techniques in Restaurant Recommendation System." In 2018 2nd International Conference on Engineering Innovation (ICEI). IEEE, 2018. http://dx.doi.org/10.1109/icei18.2018.8448528.

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Ninaus, Gerald, Florian Reinfrank, Martin Stettinger, and Alexander Felfernig. "Content-based recommendation techniques for requirements engineering." In 2014 IEEE 1st International Workshop on Artificial Intelligence for Requirements Engineering (AIRE). IEEE, 2014. http://dx.doi.org/10.1109/aire.2014.6894853.

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Jena, Kartik Chandra, Sushruta Mishra, Soumya Sahoo, and Brojo Kishore Mishra. "Principles, techniques and evaluation of recommendation systems." In 2017 International Conference on Inventive Systems and Control (ICISC). IEEE, 2017. http://dx.doi.org/10.1109/icisc.2017.8068649.

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Kokate, Shrikant, Ashwini Gaikwad, Pranita Patil, Manisha Gutte, and Kalyani Shinde. "Traveler's Recommendation System Using Data Mining Techniques." In 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA). IEEE, 2018. http://dx.doi.org/10.1109/iccubea.2018.8697862.

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Sheeba, J. I., and S. Pradeep Devaneyan. "Recommendation of Keywords using Swarm Intelligence Techniques." In ICIA-16: International Conference on Informatics and Analytics. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2980258.2980286.

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Oliveira, Amanda, and Frederico Durao. "A Group Recommendation Model Using Diversification Techniques." In Hawaii International Conference on System Sciences. Hawaii International Conference on System Sciences, 2021. http://dx.doi.org/10.24251/hicss.2021.326.

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Baltrunas, Linas, Bernd Ludwig, and Francesco Ricci. "Matrix factorization techniques for context aware recommendation." In the fifth ACM conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2043932.2043988.

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Reports on the topic "RECOMMENDATION TECHNIQUES"

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P. D. Mattie, J. A. McNeish, D. S. Sevougian, and R. W. Andrews. Methods and Techniques Used to Convey Total System Performance Assessment Analyses and Results for Site Recommendation at Yucca Mountain, Nevada, USA. Office of Scientific and Technical Information (OSTI), April 2001. http://dx.doi.org/10.2172/786563.

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Eiber. L51786 Development of Optimized Nondestructive Inspection Methods for Hot Tap Branch Connection Welds. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), January 1998. http://dx.doi.org/10.55274/r0010388.

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In the first two phases of this project, the accuracy and reliability of both conventional and more advanced nondestructive inspection techniques were evaluated by way of a round-robin program of blind inspections The results indicate that there is much variability in the accuracy and reliability of discontinuity detection and sizing depending on details of the NDT procedures adopted, which includes specification of equipment, techniques, calibration methods, and reporting requirements. In Phase III, optimized procedures for sleeve fillet welds and a limited number of branch groove welds were developed in the laboratory using fabricated assemblies containing Intentionally placed discontinuities. In response to a recommendation in Phase Ill, the current phase developed optimized procedures for a wide range of branch groove welds. The results of this phase indicate that the reliability and accuracy of nondestructive inspection techniques is not as high as obtained for sleeve fillet welds in the previous phase of the program In particular, the detection of sub-surface discontinuities by ultrasonic inspection is made more difficult by the added complexity of the branch weld geometries. As with sleeve fillet welds however, the probability of detecting weld toe cracks of a significant size can be quite good.
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Ulrich, Timothy J. II. Recommendations for acoustic techniques that meet facility requirements. Office of Scientific and Technical Information (OSTI), September 2012. http://dx.doi.org/10.2172/1052352.

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Balick, Lee K., John R. Hummel, James A. Smith, and Daniel S. Kimes. One-Dimensional Temperature Modeling Techniques. Review and Recommendations. Fort Belvoir, VA: Defense Technical Information Center, August 1990. http://dx.doi.org/10.21236/ada231098.

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Iseley, D. T., and D. H. Cowling. L51697 Obstacle Detection to Facilitate Horizontal Directional Drilling. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), January 1994. http://dx.doi.org/10.55274/r0010134.

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The horizontal directional drilling (HDD) technique is specially suited for pipeline crossings of waterways, beaches, roads, vulnerable natural regions, railroads and airports. The HDD method is a two-stage process consisting of navigating a drill stem underground along a predetermined design route and the pulling back of the product pipe through the prepared hole. One of the major problems faced in HDD projects is subsurface exploration and locating of existing underground obstacles. HDD equipment must avoid these obstacles if at all possible. This study was conducted to: 1. Determine the state-of-the-art for obstacle detection in horizontal directional drilling technology. 2. Examine all possible techniques for obstacle detection. 3. Evaluate the most promising and suitable techniques for further development. 4. Determine further work necessary to reach a 100-foot (30 m) target. 5. Make recommendations for HDD contractors.
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Josten, N. E., S. T. Marts, and G. S. Carpenter. Use of noninvasive geophysical techniques for the in situ vitrification program. Volume 3, Discussion and recommendations. Office of Scientific and Technical Information (OSTI), November 1991. http://dx.doi.org/10.2172/10139473.

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Gray. L51594 Review Pipe Integrity--Stress State Measurement Techniques. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 1989. http://dx.doi.org/10.55274/r0010566.

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Presents a state-of-the-art review of the technologies available for stress measurement in buried pipelines and recommendations regarding further development of such technologies appearing to have favorable application for natural gas transmission service. The technologies are grouped in terms of internal inspection devices, devices requiring external access to the pipe, and those that may be inserted from the ground on the pipeline right of way.
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TABUNOV, I. A., A. P. LAPINA, M. M. KOSTYCHEV, P. S. BEREZINA, and A. V. NIKIFOROVA. METHODOLOGICAL RECOMMENDATIONS FOR COACHES WORKING WITH CHILD ATHLETES ENGAGED IN ROCK CLIMBING. SIB-Expertise, December 2022. http://dx.doi.org/10.12731/er0621.06122022.

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The methodological guide will present aspects that will be useful for coaches in working with their students, in particular psychological work with athletes during the training process and during the competition, and specifically in the pre-start period. It is important for the coach to teach the athlete the techniques of psychological protection, including restoring the stability control system, reducing feelings of anxiety and countering it. It is important to carry out special psychological training. Including effective preparation for competition, based on: social values; formation of mental "internal support"; overcoming psychological barriers. Every day the degree of development and influence of sports reaches a new level. Also, the requirements for athletes in technical, physical and tactical readiness are increasing, respectively, the result of competitive activity will already be determined by readiness and psychological attitude. Psychological preparation is a process aimed at creating a state of mental readiness for competition in athletes. This should be considered the subject of psychological preparation for competitions in sports.
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Miller, Mr Michael J. DTPH56-06-T-000017 In-Field Welding and Coating Protocols. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), May 2009. http://dx.doi.org/10.55274/r0012117.

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Gas Technology Institute (GTI) and Edison Welding Institute (EWI) created both laboratory and infield girth weld samples to evaluate the effects of weld geometry and hydrogen off-gassing on the performance of protective coatings. Laboratory-made plate welds were used to tightly control geometric differences and in-field welds were created to mimic real-world welding conditions and hydrogen off-gassing rates. These welds were then coated and tested with accelerated corrosion techniques to evaluate the coatings' effectiveness. Simulated girth welds investigated geometric effects on the performance of a liquid-applied coating. Welds were created, coated, and testing in a salt-fog environment to accelerate corrosion. Undercuts up to 0.03 inches were found to have no significant effect on coatings' resistance to corrosion. On the contrary, the undercut tended to add to the coating thickness and therefore increased corrosion resistance. Increasing cap height of a weld was found to thin the coating making it more susceptible to chipping but no more susceptible to corrosion. If applying proper coating procedures, especially surface profiling, the weld geometries investigated here had no strong negative effects on a liquid applied two-part epoxy coating's performance. Since fusion-bonded epoxy (FBE) coatings are applied in a different manner, these results cannot be extended from liquid to FBE coatings. If the FBE provides the same wetting of the undercut and similar coating thickness on the cap height one would expect similar results. In-field welds were created to test the effects of hydrogen off-gassing on coating performance. Two different welding mediums were used, one with a high hydrogen content and one with low hydrogen content. These different welds were then held for 2 or 5 hours to vary the amount of time allowed for hydrogen off-gassing and then coated in either FBE or a liquid 2 part epoxy. All other variables were held constant. Cross-sectional analysis of coated 24-inch diameter pipes showed no increase of voids above the welded area, indicating there was little off-gassing in these samples. Cathodic Disbondment Testing, per ASTM G-95, was performed to evaluate the coating's adhesion properties. No detectable adhesion differences were found that could be attributed to the hydrogen off-gassing from the weld, instead, the results were more dependent on the coating thickness. Within the scope/boundary of the completed research, a hold time of two hours is sufficient to minimize any hydrogen off-gassing effects. Within the parameters of the in-field welds and simulated welds, no major detrimental effects were found from hydrogen off-gassing and weld geometries. However, the higher cap-height did make coatings more susceptible to damage when handling. This confirms previous GTI research which indicated that coatings often accrue damage during handling. GTI and EWI, taking into consideration the survey and testing results produced a recommendation to be distributed to various stakeholders in the pipeline industry. The summary document to be disturbed is located in the Recommendation section of this report.
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Thompson, Marshall, and Ramez Hajj. Flexible Pavement Recycling Techniques: A Summary of Activities. Illinois Center for Transportation, July 2021. http://dx.doi.org/10.36501/0197-9191/21-022.

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Cold in-place recycling (CIR) involves the recycling of the asphalt portions (including hot-mix asphalt and chip, slurry, and cape seals, as well as others) of a flexible or composite pavement with asphalt emulsion or foamed asphalt as the binding agent. Full-depth reclamation (FDR) includes the recycling of the entire depth of the pavement and, in some cases, a portion of the subgrade with asphalt, cement, or lime products as binding agents. Both processes are extensively utilized in Illinois. This project reviewed CIR and FDR projects identified by the Illinois Department of Transportation (IDOT) from the Transportation Bulletin and provided comments on pavement designs and special provisions. The researchers evaluated the performance of existing CIR/FDR projects through pavement condition surveys and analysis of falling weight deflectometer data collected by IDOT. They also reviewed CIR/FDR literature and updated/modified (as appropriate) previously provided inputs concerning mix design, testing procedures, thickness design, construction, and performance as well as cold central plant recycling (CCPR) literature related to design and construction. The team monitored the performance of test sections at the National Center for Asphalt Technology and Virginia Department of Transportation. The researchers assisted IDOT in the development of a CCPR special provision as well as responded to IDOT inquiries and questions concerning issues related to CIR, FDR, and CCPR. They attended meetings of IDOT’s FDR with the Cement Working Group and provided input in the development of a special provision for FDR with cement. The project’s activities confirmed that CIR, FDR, and CCPR techniques are successfully utilized in Illinois. Recommendations for improving the above-discussed techniques are provided.
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