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Relevant bibliographies by topics / Recommendation Mahout

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Academic literature on the topic 'Recommendation Mahout'

Author: Grafiati

Published: 4 June 2021

Last updated: 14 February 2022

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Journal articles on the topic "Recommendation Mahout"

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Yu, Jian Yun. "Design of Distributed Recommendation Engine Based on Hadoop and Mahout." Applied Mechanics and Materials 641-642 (September 2014): 1284–86. http://dx.doi.org/10.4028/www.scientific.net/amm.641-642.1284.

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The distributed recommendation engine consists of three layers of data storage layer, Produce recommended layer and application layer, the data storage layer is mainly stored user preferences data, these data are recommended on the basis of upper recommendation engines. Produce recommend layer producing part recommend the key lies in the recommendation engine of the algorithm, the algorithm adopts the Mahout as recommendation framework, and implement custom recommendation algorithm, including the recommendation algorithm based on user similarity, based on the recommendations from the project similarity algorithm and based on the recommendations of the Slope One algorithm after receiving recommended the client's request, the Servlet will produce a recommended by pushing engine first get data model, according to the similarity between data model computing project, and generate the recommended ID. The application layer is mainly based on B/S architecture to implement, can be very easily expanded to mobile platforms.

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Yang, Deguo, and Qing Shi. "Implementation of Personalized Recommendation Algorithm based on Mahout." Journal of Physics: Conference Series 1827, no. 1 (2021): 012147. http://dx.doi.org/10.1088/1742-6596/1827/1/012147.

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Veena, Ch, and B. Vijaya Babu. "A User- Based Recommendation with a Scalable Machine Learning Tool." International Journal of Electrical and Computer Engineering (IJECE) 5, no. 5 (2015): 1153. http://dx.doi.org/10.11591/ijece.v5i5.pp1153-1157.

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Recommender Systems have proven to be valuable way for online users to recommend information items like books, videos, songs etc.colloborative filtering methods are used to make all predictions from historical data. In this paper we introduce Apache mahout which is an open source and provides a rich set of components to construct a customized recommender system from a selection of machine learning algorithms.[12] This paper also focuses on addressing the challenges in collaborative filtering like scalability and data sparsity. To deal with scalability problems, we go with a distributed frame work like hadoop. We then present a customized user based recommender system.

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Song, Bo, Yue Gao, and Xiao-Mei Li. "Research on Collaborative Filtering Recommendation Algorithm Based on Mahout and User Model." Journal of Physics: Conference Series 1437 (January 2020): 012095. http://dx.doi.org/10.1088/1742-6596/1437/1/012095.

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., Swati Pandey. "COSTOMIZATION OF RECOMMENDATION SYSTEM USING COLLABORATIVE FILTERING ALGORITHM ON CLOUD USING MAHOUT." International Journal of Research in Engineering and Technology 03, no. 19 (2014): 39–43. http://dx.doi.org/10.15623/ijret.2014.0319008.

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Symeonidis, Panagiotis, Ludovik Coba, and Markus Zanker. "Improving Time-Aware Recommendations in Open Source Packages." International Journal on Artificial Intelligence Tools 28, no. 06 (2019): 1960007. http://dx.doi.org/10.1142/s0218213019600078.

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Collaborative filtering techniques have been studied extensively during the last decade. Many open source packages (Apache Mahout, LensKit, MyMediaLite, rrecsys etc.) have implemented them, but typically the top-N recommendation lists are only based on a highest predicted ratings approach. However, exploiting frequencies in the user/item neighborhood for the formation of the top-N recommendation lists has been shown to provide superior accuracy results in offline simulations. In addition, most open source packages use a time-independent evaluation protocol to test the quality of recommendations, which may result to misleading conclusions since it cannot simulate well the real-life systems, which are strongly related to the time dimension. In this paper, we have therefore implemented the time-aware evaluation protocol to the open source recommendation package for the R language — denoted rrecsys — and compare its performance across open source packages for reasons of replicability. Our experimental results clearly demonstrate that using the most frequent items in neighborhood approach significantly outperforms the highest predicted rating approach on three public datasets. Moreover, the time-aware evaluation protocol has been shown to be more adequate for capturing the life-time effectiveness of recommender systems.

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Shi, Yongqing, and Xiaojiang Yang. "A Personalized Matching System for Management Teaching Resources Based on Collaborative Filtering Algorithm." International Journal of Emerging Technologies in Learning (iJET) 15, no. 13 (2020): 207. http://dx.doi.org/10.3991/ijet.v15i13.15353.

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To realize education informatization, it is highly necessary to recommend teaching resources to students that can enhance their learning interest and improve teaching quality. This paper develops a personalized matching system for management teaching resources based on collaborative filtering (CF) algorithm. Firstly, the authors set up a user interest model, designed the flow and algorithm for personalized matching, and improved the similarity calculation method. Next, a personalized recommendation algorithm was developed based on the CF, and a personalized matching engine was constructed with the aid of Apache Mahout. The experimental results show that the proposed CF algorithm can effectively improve the recommendation quality, and push personalized teaching resources to each user; the learners are highly satisfied with the personalized matching system. The research results shed new light on personalized recommendation of teaching resources, opening up a new way to education informatization.

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Thillainayagam, Venkatesan, Saravanan Kunjithapatham, and Ramkumar Thirunavukarasu. "Performing item-based recommendation for mining multi-source big data by considering various weighting parameters." International Journal of Engineering & Technology 7, no. 4 (2018): 2360. http://dx.doi.org/10.14419/ijet.v7i4.16001.

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In the context of big data, a recommendation system has been put forth as an efficient strategy for predicting the consumer’s pref-erences while rating items. Organizations that are functioning with multiple branches are in the imperative need for analyzing their multi-source big data to arrive novel decisions with respect to branch level and central level. In such circumstances, a multi-state business organi-zation would like to analyze their consumer preferences and enhance their decision-making activities based on the taste/preferences obtained from diversified data sources located in different places. One of the problems in current Item-based collaborative filtering approach is that users and their ratings have been considered uniformly while recording their preferences about target items. To improve the quality of rec-ommendations, the paper proposes various weighting strategies for arriving effective recommendation of items especially when the sources of data are multi-source in nature. For a multi-source data environment, the proposed strategies would be effective for validating the active user rating for a target item. To validate the novelty of the proposal, a Hadoop based big data eco-system with aid of Mahout has been con-structed and experimental investigations are carried out in a benchmark dataset.

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Thillainayagam, Venkatesan, Saravanan Kunjithapatham, and Ramkumar Thirunavukarasu. "Performing item-based recommendation for mining multi-source big data by considering various weighting parameters." International Journal of Engineering & Technology 7, no. 4 (2018): 2360. http://dx.doi.org/10.14419/ijet.v7i4.16002.

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In the context of big data, a recommendation system has been put forth as an efficient strategy for predicting the consumer’s pref-erences while rating items. Organizations that are functioning with multiple branches are in the imperative need for analyzing their multi-source big data to arrive novel decisions with respect to branch level and central level. In such circumstances, a multi-state business organi-zation would like to analyze their consumer preferences and enhance their decision-making activities based on the taste/preferences obtained from diversified data sources located in different places. One of the problems in current Item-based collaborative filtering approach is that users and their ratings have been considered uniformly while recording their preferences about target items. To improve the quality of rec-ommendations, the paper proposes various weighting strategies for arriving effective recommendation of items especially when the sources of data are multi-source in nature. For a multi-source data environment, the proposed strategies would be effective for validating the active user rating for a target item. To validate the novelty of the proposal, a Hadoop based big data eco-system with aid of Mahout has been con-structed and experimental investigations are carried out in a benchmark dataset.

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Machorro-Cano, Isaac, Giner Alor-Hernández, Mario Andrés Paredes-Valverde, Uriel Ramos-Deonati, José Luis Sánchez-Cervantes, and Lisbeth Rodríguez-Mazahua. "PISIoT: A Machine Learning and IoT-Based Smart Health Platform for Overweight and Obesity Control." Applied Sciences 9, no. 15 (2019): 3037. http://dx.doi.org/10.3390/app9153037.

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Overweight and obesity are affecting productivity and quality of life worldwide. The Internet of Things (IoT) makes it possible to interconnect, detect, identify, and process data between objects or services to fulfill a common objective. The main advantages of IoT in healthcare are the monitoring, analysis, diagnosis, and control of conditions such as overweight and obesity and the generation of recommendations to prevent them. However, the objects used in the IoT have limited resources, so it has become necessary to consider other alternatives to analyze the data generated from monitoring, analysis, diagnosis, control, and the generation of recommendations, such as machine learning. This work presents PISIoT: a machine learning and IoT-based smart health platform for the prevention, detection, treatment, and control of overweight and obesity, and other associated conditions or health problems. Weka API and the J48 machine learning algorithm were used to identify critical variables and classify patients, while Apache Mahout and RuleML were used to generate medical recommendations. Finally, to validate the PISIoT platform, we present a case study on the prevention of myocardial infarction in elderly patients with obesity by monitoring biomedical variables.

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Dissertations / Theses on the topic "Recommendation Mahout"

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Zanarella, Leonardo. "Progettazione ed Implementazione di Recommendation Content-based Filtering basato su Apache Mahout." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018.

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Borgiani, Silvia. "Progettazione ed implementazione di un recommendation system di articoli scientifici basato su Apache Mahout." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/10529/.

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Il focus di questo elaborato è sui sistemi di recommendations e le relative caratteristiche. L'utilizzo di questi meccanism è sempre più forte e presente nel mondo del web, con un parallelo sviluppo di soluzioni sempre più accurate ed efficienti. Tra tutti gli approcci esistenti, si è deciso di prendere in esame quello affrontato in Apache Mahout. Questa libreria open source implementa il collaborative-filtering, basando il processo di recommendation sulle preferenze espresse dagli utenti riguardo ifferenti oggetti. Grazie ad Apache Mahout e ai principi base delle varie tipologie di recommendationè stato possibile realizzare un applicativo web che permette di produrre delle recommendations nell'ambito delle pubblicazioni scientifiche, selezionando quegli articoli che hanno un maggiore similarità con quelli pubblicati dall'utente corrente. La realizzazione di questo progetto ha portato alla definizione di un sistema ibrido. Infatti l'approccio alla recommendation di Apache Mahout non è completamente adattabile a questa situazione, per questo motivo le sue componenti sono state estese e modellate per il caso di studio. Siè cercato quindi di combinare il collaborative filtering e il content-based in un unico approccio. Di Apache Mahout si è mantenuto l'algoritmo attraverso il quale esaminare i dati del data set, tralasciando completamente l'aspetto legato alle preferenze degli utenti, poichè essi non esprimono delle valutazioni sugli articoli. Del content-based si è utilizzata l'idea del confronto tra i titoli delle pubblicazioni. La valutazione di questo applicativo ha portato alla luce diversi limiti, ma anche possibili sviluppi futuri che potrebbero migliorare la qualità delle recommendations, ma soprattuto le prestazioni. Grazie per esempio ad Apache Hadoop sarebbe possibile una computazione distribuita che permetterebbe di elaborare migliaia di dati con dei risultati più che discreti.

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KORTUS, Lukáš. "Analýza a návrh modulu doporučovacího systému." Master's thesis, 2015. http://www.nusl.cz/ntk/nusl-201543.

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Recommendation systems serve to users of e-commerce applications for individual recommendations to certain products or services based on their preferences. The aim of this thesis is to create a module of recommender system. The work includes analysis of recommendation systems and the methods used in these systems, including a description of the calculations. This work also solves the cold start problem, which is a problem when generation of some good recommendations for the new user is needed, but the recommendation system has no or little information about this user. Based on analysis is in this thesis designed module for recommender system, which is applicable e.g. internet for e-commerce or other internet-based application. Part of this module is the realization of a platform Apache Mahout, which some parts are built on a distributed computing platform Apache Hadoop project. Furthermore, in this work, on the aforementioned platform Mahout, selected methods of calculating the similarity using selected criteria (e.g. the average time for a recommendation, and the number of users for who have not been able to generate recommendations) are tested.

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Sigman, Matthew Stephen. "Using machine learning techniques to simplify mobile interfaces." 2012. http://hdl.handle.net/2152/19967.

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This paper explores how known machine learning techniques can be applied in unique ways to simplify software and therefore dramatically increase its usability. As software has increased in popularity, its complexity has increased in lockstep, to a point where it has become burdensome. By shifting the focus from the software to the user, great advances can be achieved by way of simplification. The example problem used in this report is well known: suggest local dining choices tailored to a specific person based on known habits and those of similar people. By analyzing past choices and applying likely probabilities, assumptions can be made to reduce user interaction, allowing the user to realize the benefits of the software faster and more frequently. This is accomplished with Java Servlets, Apache Mahout machine learning libraries, and various third party resources to gather dimensions on each recommendation.<br>text

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Book chapters on the topic "Recommendation Mahout"

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Paul, Sutanu, and Dipankar Das. "User-Item-Based Hybrid Recommendation System by Employing Mahout Framework." In Advances in Intelligent Systems and Computing. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7403-6_32.

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Kumar, Thangavel Senthil, and Swati Pandey. "Customization of Recommendation System Using Collaborative Filtering Algorithm on Cloud Using Mahout." In Intelligent Distributed Computing. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11227-5_1.

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Hsieh, Meng-Yen, Gui-Lin Li, Ming-Hong Liao, Wen-Kuang Chou, and Kuan-Ching Li. "Accurate Analysis of a Movie Recommendation Service with Linked Data on Hadoop and Mahout." In Lecture Notes in Electrical Engineering. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0539-8_55.

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Conference papers on the topic "Recommendation Mahout"

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Hung, Phan Duy, and Dinh Le Huynh. "E-Commerce Recommendation System Using Mahout." In 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS). IEEE, 2019. http://dx.doi.org/10.1109/ccoms.2019.8821663.

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Wang, Yongchang, and Ligu Zhu. "Research on Collaborative Filtering Recommendation Algorithm Based on Mahout." In 2016 4th Intl Conf on Applied Computing and Information Technology/3rd Intl Conf on Computational Science/Intelligence and Applied Informatics/1st Intl Conf on Big Data, Cloud Computing, Data Science & Engineering (ACIT-CSII-BCD). IEEE, 2016. http://dx.doi.org/10.1109/acit-csii-bcd.2016.084.

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Yan, ZiDong, Qingchun Hu, Chengyu Tang, Kelang Yang, and Hongyu Cai. "The Performance Evaluation of Recommendation Algorithm Using Mahout Framework." In CSSE 2020: 2020 3rd International Conference on Computer Science and Software Engineering. ACM, 2020. http://dx.doi.org/10.1145/3403746.3403898.

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Jabakji, Ammar, and Hasan Dag. "Improving item-based recommendation accuracy with user's preferences on Apache Mahout." In 2016 IEEE International Conference on Big Data (Big Data). IEEE, 2016. http://dx.doi.org/10.1109/bigdata.2016.7840789.

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Fei, Dai, and Xiaohui Cheng. "AN IMPROVED HYBRID RECOMMENDATION ALGORITHM STUDY AND DESIGN ON MAHOUT FRAMEWORK." In International Conference on World Symposium on Mechanical and Control Engineering (WSMCE). Volkson Press, 2017. http://dx.doi.org/10.26480/wsmce.01.2017.160.163.

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Walunj, Sachin Gulabrao, and Kishor Sadafale. "An online recommendation system for e-commerce based on apache mahout framework." In the 2013 annual conference. ACM Press, 2013. http://dx.doi.org/10.1145/2487294.2487328.

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