Relevant bibliographies by topics / Hybrid recommendation system

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

Academic literature on the topic 'Hybrid recommendation system'

Author: Grafiati
Published: 4 June 2021
Last updated: 19 June 2025

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Journal articles on the topic "Hybrid recommendation system"

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Kudori, Dio Saputra. "Event Recommendation System using Hybrid Method Based on Mobile Device." Journal of Information Technology and Computer Science 6, no. 1 (2021): 107–16. http://dx.doi.org/10.25126/jitecs.202161221.

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In everyday life there are many events that are held. Theseeventuse various ways in term of announcing eventfor attracting people to come.Because there are many event that are held in everyday life,an event recommendation system can be implemented to provide event recommendations that are appropriate for the user. In developing event recommendation systems, there are many methods that can be used, the onethat frequently used is collaborative filtering. The event recommendation system has a unique character compared to other recommendation systems. This is because the event recommendation system doesn’t use the classic scenario of a recommendation system. In this study we tried to use a hybrid method that combines collaborative filteringwith sentiment analysis. The experiment show that the results of the event recommendations have an accuracy value of 82%. Itshows that the hybrid method can be utilized for developing event recommendation systems.
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Gandhi, Pritesh. "Distributed Hybrid Book Recommendation System." International Journal for Research in Applied Science and Engineering Technology V, no. III (2017): 297–98. http://dx.doi.org/10.22214/ijraset.2017.3056.

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Yang, Fan. "A hybrid recommendation algorithm–based intelligent business recommendation system." Journal of Discrete Mathematical Sciences and Cryptography 21, no. 6 (2018): 1317–22. http://dx.doi.org/10.1080/09720529.2018.1526408.

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Gaurav, D. Ganesh. "HOTEL RECOMMENDATION SYSTEM USING HYBRID TECHNIQUE." International Journal of Advanced Research in Computer Science 11, no. 3 (2020): 47–49. http://dx.doi.org/10.26483/ijarcs.v11i3.6529.

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Barathan, Monishkanna, and Ershad Sharifahmadian. "Hybrid POI Recommendation System for Tourism." International Journal of System Modeling and Simulation 3, no. 1 (2018): 1. http://dx.doi.org/10.24178/ijsms.2018.3.1.01.

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Due to the increase in amount of available information, finding places and planning of the activities to be done during a tour can be strenuous. Tourists are looking for information about a place in which they have not been before, which worsen the selection of places that fit better with user’s preferences. Recommendation systems have been fundamentally applicable in tourism, suggest suitable places, and effectively prune large information from different locations, so tourists are directed toward those places where are matched with their needs and preferences. Several techniques have been studied for point-of-interest (POI) recommendation, including content-based which builds based on user preferences, collaborative filtering which exploits the behavior of other users, and different places, knowledge-based method, and several other techniques. These methods are vulnerable to some limitations and shortcomings related to recommendation environment such as scalability, sparsity, first-rater or gray sheep problems. This paper tries to identify the drawbacks that prevent wide spread use of these methodologies in recommendation. To improve performance of recommendation systems, these methods are combined to form hybrid recommenders. This paper proposes a novel hybrid recommender system which suggests tourism destinations to a user with minimal user interaction. Furthermore, we use sentiment analysis of user’s comments to enhance the efficiency of the proposed system.
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Vishwajith, V., S. Kaviraj, and R. Vasanth. "Hybrid Recommender System for Therapy Recommendation." IJARCCE 8, no. 1 (2019): 78–84. http://dx.doi.org/10.17148/ijarcce.2019.8118.

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Suka Parwita, Wayan Gede, and Edi Winarko. "Hybrid Recommendation System Memanfaatkan Penggalian Frequent Itemset dan Perbandingan Keyword." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 9, no. 2 (2015): 167. http://dx.doi.org/10.22146/ijccs.7545.

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AbstrakRecommendation system sering dibangun dengan memanfaatkan data peringkat item dan data identitas pengguna. Data peringkat item merupakan data yang langka pada sistem yang baru dibangun. Sedangkan, pemberian data identitas pada recommendation system dapat menimbulkan kekhawatiran penyalahgunaan data identitas.Hybrid recommendation system memanfaatkan algoritma penggalian frequent itemset dan perbandingan keyword dapat memberikan daftar rekomendasi tanpa menggunakan data identitas pengguna dan data peringkat item. Penggalian frequent itemset dilakukan menggunakan algoritma FP-Growth. Sedangkan perbandingan keyword dilakukan dengan menghitung similaritas antara dokumen dengan pendekatan cosine similarity.Hybrid recommendation system memanfaatkan kombinasi penggalian frequent itemset dan perbandingan keyword dapat menghasilkan rekomendasi tanpa menggunakan identitas pengguna dan data peringkat dengan penggunaan ambang batas berupa minimum similarity, minimum support, dan jumlah rekomendasi. Nilai pengujian yaitu precision, recall, F-measure, dan MAP dipengaruhi oleh besarnya nilai ambang batas yang ditetapkan. Kata kunci— Hybrid recommendation system, frequent itemset, cosine similarity. AbstractRecommendation system was commonly built by manipulating item is ranking data and user is identity data. Item ranking data were rarely available on newly constructed system. Whereas, giving identity data to the recommendation system causes concerns about identity data misuse.Hybrid recommendation system used frequent itemset mining algorithm and keyword comparison, it can provide recommendations without identity data and item ranking data. Frequent itemset mining was done using FP-Gwowth algorithm and keyword comparison with calculating document similarity value using cosine similarity approach.Hybrid recommendation system with a combination of frequent itemset mining and keywords comparison can give recommendations without using user identity and rating data. Hybrid recommendation system using 3 thresholds ie minimum similarity, minimum support, and number of recommendations. With the testing data used, precision, recall, F-measure, and MAP testing value are influenced by the threshold value. Keywords— Hybrid recommendation system, frequent itemset, cosine similarity.
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Tian, Yonghong, Bing Zheng, Yanfang Wang, Yue Zhang, and Qi Wu. "College Library Personalized Recommendation System Based on Hybrid Recommendation Algorithm." Procedia CIRP 83 (2019): 490–94. http://dx.doi.org/10.1016/j.procir.2019.04.126.

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Monika, Miss Jadhav, and Mrs Kakade Shital P. "Hybrid Recommendation System with Review Helpfulness Features." IARJSET 4, no. 4 (2017): 148–51. http://dx.doi.org/10.17148/iarjset/nciarcse.2017.43.

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Guia, Márcio, Rodrigo Rocha Silva, and Jorge Bernardino. "A Hybrid Ontology-Based Recommendation System in e-Commerce." Algorithms 12, no. 11 (2019): 239. http://dx.doi.org/10.3390/a12110239.

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The growth of the Internet has increased the amount of data and information available to any person at any time. Recommendation Systems help users find the items that meet their preferences, among the large number of items available. Techniques such as collaborative filtering and content-based recommenders have played an important role in the implementation of recommendation systems. In the last few years, other techniques, such as, ontology-based recommenders, have gained significance when reffering better active user recommendations; however, building an ontology-based recommender is an expensive process, which requires considerable skills in Knowledge Engineering. This paper presents a new hybrid approach that combines the simplicity of collaborative filtering with the efficiency of the ontology-based recommenders. The experimental evaluation demonstrates that the proposed approach presents higher quality recommendations when compared to collaborative filtering. The main improvement is verified on the results regarding the products, which, in spite of belonging to unknown categories to the users, still match their preferences and become recommended.
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Dissertations / Theses on the topic "Hybrid recommendation system"

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Alsalama, Ahmed. "A Hybrid Recommendation System Based on Association Rules." TopSCHOLAR®, 2013. http://digitalcommons.wku.edu/theses/1250.

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Recommendation systems are widely used in e-commerce applications. Theengine of a current recommendation system recommends items to a particular user based on user preferences and previous high ratings. Various recommendation schemes such as collaborative filtering and content-based approaches are used to build a recommendation system. Most of current recommendation systems were developed to fit a certain domain such as books, articles, and movies. We propose a hybrid framework recommendation system to be applied on two dimensional spaces (User × Item) with a large number of users and a small number of items. Moreover, our proposed framework makes use of both favorite and non-favorite items of a particular user. The proposed framework is built upon the integration of association rules mining and the content-based approach. The results of experiments show that our proposed framework can provide accurate recommendations to users.
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Ozturk, Gizem. "A Hybrid Veideo Recommendation System Based On A Graph Based Algorithm." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612624/index.pdf.

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This thesis proposes the design, development and evaluation of a hybrid video recommendation system. The proposed hybrid video recommendation system is based on a graph algorithm called Adsorption. Adsorption is a collaborative filtering algorithm in which relations between users are used to make recommendations. Adsorption is used to generate the base recommendation list. In order to overcome the problems that occur in pure collaborative system, content based filtering is injected. Content based filtering uses the idea of suggesting similar items that matches user preferences. In order to use content based filtering, first, the base recommendation list is updated by removing weak recommendations. Following this, item similarities of the remaining list are calculated and new items are inserted to form the final recommendations. Thus, collaborative recommendations are empowered considering item similarities. Therefore, the developed hybrid system combines both collaborative and content based approaches to produce more effective suggestions.
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Wiklund, Ida. "A Recommendation system for News Push Notifications- Personalizing with a User-based and Content-based Recommendation system." Thesis, Umeå universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-172275.

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The news landscape has changed during recent years because of the digitization. News can nowadays be found in both newspapers and on different sites online. The availability of the digital newspapers leads to competition among the news companies. To make the users stay on one specific platform for news, relevance is required in the content and oneway of creating relevance is through personalization, to tailor the content to each user. The focus of this thesis is therefore personalizing newspush notifications for a digital  newspaper and making them more relevant for users. The project was made in cooperation with VK Media, and their digital newspaper. The task in this thesis is to implement personalization of push notifications by building a recommendation system and to test the implemented system with data from VK. In order to perform the task, a dataset representing reading habits of VK’s users was extracted from their data warehouse. Then a user-based and content-based recommendation system was implemented in Python.The idea with the system is to recommend new articles that are sufficiently similar to one or more of the already read articles. Articles that may be liked by one of the most similar users should also be recommended. Finally, the system’s performance was evaluated with the data representing reading habits for VK’s users. The results show that the implemented system has better performance than the current solution without any personalization, when recommending a few articles to each user. The results from the evaluation also show that the more articles the users have read, the better predictions are possible to make. Thus, this thesis offers a first step towards meeting the expectations of more relevant content among VK’s users.
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Oktay, Fulya. "A Hybrid Recommendation System Capturing The Effect Of Time And Demographic Data." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/2/12612019/index.pdf.

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The information that World Wide Web (WWW) provides have grown up very rapidly in recent years, which resulted in new approaches for people to reach the information they need. Although web pages and search engines are indeed strong enough for us to reach what we want, it is not an efficient solution to present data and wait people to reach it. Some more creative and beneficial methods had to be developed for decreasing the time to reach the information and increase the quality of the information. Recommendation systems are one of the ways for achieving this purpose. The idea is to design a system that understands the information user wants to obtain from user actions, and to find the information similar to that. Several studies have been done in this field in order to develop a recommendation system which is capable of recommending movies, books, web sites and similar items like that. All of them are based on two main principles, which are collaborative filtering and content based recommendations. Within this thesis work, a recommendation system approach which combines both content based (CB) and collaborative filtering (CF) approaches by capturing the effect of time like purchase time or release time. In addition to this temporal behavior, the influence of demographic information of user on purchasing habits is also examined this system which is called &ldquo<br>TDRS&rdquo<br>.
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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<br>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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Ceylan, Ugur. "An Ontology-based Hybrid Recommendation System Using Semantic Similarity Measure And Feature Weighting." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613754/index.pdf.

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The task of the recommendation systems is to recommend items that are relevant to the preferences of users. Two main approaches in recommendation systems are collaborative filtering and content-based filtering. Collaborative filtering systems have some major problems such as sparsity, scalability, new item and new user problems. In this thesis, a hybrid recommendation system that is based on content-boosted collaborative filtering approach is proposed in order to overcome sparsity and new item problems of collaborative filtering. The content-based part of the proposed approach exploits semantic similarities between items based on a priori defined ontology-based metadata in movie domain and derived feature-weights from content-based user models. Using the semantic similarities between items and collaborative-based user models, recommendations are generated. The results of the evaluation phase show that the proposed approach improves the quality of recommendations.
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Lokesh, Ashwini. "A Comparative Study of Recommendation Systems." TopSCHOLAR®, 2019. https://digitalcommons.wku.edu/theses/3166.

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Recommendation Systems or Recommender Systems have become widely popular due to surge of information at present time and consumer centric environment. Researchers have looked into a wide range of recommendation systems leveraging a wide range of algorithms. This study investigates three popular recommendation systems in existence, Collaborative Filtering, Content-Based Filtering, and Hybrid recommendation system. The famous MovieLens dataset was utilized for the purpose of this study. The evaluation looked into both quantitative and qualitative aspects of the recommendation systems. We found that from both the perspectives, the hybrid recommendation system performs comparatively better than standalone Collaborative Filtering or Content-Based Filtering recommendation system
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Kaufman, Jaime C. "A Hybrid Approach to Music Recommendation: Exploiting Collaborative Music Tags and Acoustic Features." UNF Digital Commons, 2014. http://digitalcommons.unf.edu/etd/540.

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Recommendation systems make it easier for an individual to navigate through large datasets by recommending information relevant to the user. Companies such as Facebook, LinkedIn, Twitter, Netflix, Amazon, Pandora, and others utilize these types of systems in order to increase revenue by providing personalized recommendations. Recommendation systems generally use one of the two techniques: collaborative filtering (i.e., collective intelligence) and content-based filtering. Systems using collaborative filtering recommend items based on a community of users, their preferences, and their browsing or shopping behavior. Examples include Netflix, Amazon shopping, and Last.fm. This approach has been proven effective due to increased popularity, and its accuracy improves as its pool of users expands. However, the weakness with this approach is the Cold Start problem. It is difficult to recommend items that are either brand new or have no user activity. Systems that use content-based filtering recommend items based on extracted information from the actual content. A popular example of this approach is Pandora Internet Radio. This approach overcomes the Cold Start problem. However, the main issue with this approach is its heavy demand on computational power. Also, the semantic meaning of an item may not be taken into account when producing recommendations. In this thesis, a hybrid approach is proposed by utilizing the strengths of both collaborative and content-based filtering techniques. As proof-of-concept, a hybrid music recommendation system was developed and evaluated by users. The results show that this system effectively tackles the Cold Start problem and provides more variation on what is recommended.
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Bunnell, Lawrence. "FinPathlight: Framework for an Ontology-Based, Multiagent, Hybrid Recommender System Designed to Increase Consumer Financial Capability." VCU Scholars Compass, 2019. https://scholarscompass.vcu.edu/etd/5801.

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This study is a design science research (DSR) project in which a description of the development and evaluation process for several novel technological artifacts will be communicated. Specifically, this study will establish: 1) an ontology of recommender systems issues, 2) an ontology of financial capability goals, and 3) a framework for a Personal Financial Recommender System (PFRS) application designed to improve user financial capability, called FinPathlight. The impetus for the RecSys Issues Ontology is to address a gap in the literature by providing researchers with a comprehensive knowledge classification of the issues and limitations inherent to recommender systems research. The development of a Financial Capability Goals Ontology will contribute domain knowledge classification for technological systems within the domain of finance and serves as a recommendation item knowledgebase for our PFRS. The FinPathlight framework provides the architecture and principles of implementation for a novel, financial-technology (FinTech) PFRS. FinPathlight is designed to improve the financial capability of its users through the recommendation, tracking and assistance with achieving financial capability enhancing goals. This research is notable in that it expands the influence and furthers the relevance of information systems research by providing an explicitly applicable research solution to an area of significant socio-economic importance, financial capability, a heretofore unsolved “wicked problem” (Churchman 1967) domain. In light of current financial conditions, recommender systems research that addresses a problem such as consumer financial capability is a step towards ensuring that information systems research continues to matter and retain its influence and relevance in everyday practice.
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Pequeno, Paulo Andrà Lima. "A Recommendation system with hybrid content filtering for virtual learning environments as a tool for supporting students and monitoring numerous classes." Universidade Federal do CearÃ, 2014. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=12894.

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The provision of educational sources on the Web through specific portals or by public libraries has given democratic spaces to both students and teachers to support their educational routine. However, it is always a challenge to make all that diversity of resources useful to each individual having into consideration their needs. This dissertation seeks to contribute providing students and teachers with a computational environment to help in the learning process. This solution connected to a Learning Virtual Environment and an Exercise Virtual Environment allows students to have an automatic tutorial support, which has references and content targeted to their learning level. This solution allows to the teachers not only support their didactic work with the students, but also it permits to view the status of each student against curricular elements that should be addressed in the teacherâs discipline. Such approach can help the teacher in making adjustments and improvements to the course. ESignifica, a recommendation system was developed according to the filtering hybrid techniques, that add a content and a collaborative filter as well. The developed solution was tested with a student group from the Calculus subject that belonged to the Electrical Engineering course from the Federal University of Cearà â UFC, academic years 2012, 2013 and 2014. The Recommendation System developed and the experimental results achieved are presented in this dissertation.<br>A oferta de recursos educacionais na web por meio de portais especÃficos ou de bibliotecas pÃblicas de conteÃdo tem proporcionado espaÃos democrÃticos a alunos e professores no apoio a suas prÃticas acadÃmicas. No entanto, tornar Ãtil a diversidade de recursos disponÃveis levando em consideraÃÃo as necessidades especÃficas de cada indivÃduo à ainda um desafio a enfrentar. Inserindo-se neste contexto, este trabalho, propÃe um ambiente computacional a alunos e professores que seja capaz de sugerir, de maneira seletiva, conteÃdos de apoio ao processo de aprendizagem. Integrando um Ambiente Virtual de Aprendizagem a um Ambiente Virtual de ExercÃcios, o Sistema de RecomendaÃÃo sugere ao aluno referÃncias a conteÃdos adequados ao nÃvel de dificuldade apresentado durante a realizaÃÃo de exercÃcios interativos propostos. AlÃm disso, a partir do rastreamento das interaÃÃes dos alunos com os exercÃcios interativos e compilaÃÃo de resultados, o sistema permite identificar, atravÃs de relatÃrios, os conteÃdos com os quais os alunos vÃm apresentando maiores dificuldades, tanto do ponto de vista individual como coletivo, instrumentalizando professores à realizaÃÃo de medidas proativas. O sistema de recomendaÃÃo desenvolvido, denominado eSignifica, foi especificado segundo as tÃcnicas de filtragem hÃbrida, combinando filtragem de conteÃdo e filtragem colaborativa. A soluÃÃo desenvolvida foi testada com turmas de alunos de uma disciplina de CÃlculo Fundamental do curso de Engenharia ElÃtrica da Universidade federal do Cearà â UFC nos anos letivos de 2012, 2013 e 2014. O Sistema de RecomendaÃÃo desenvolvido e os resultados experimentais alcanÃados demonstraram a possibilidade que um trabalho preventivo pode resultar em um melhor rendimento da turma, assim como apresentar as dificuldades mais relevantes da turma pode servir de auxÃlio ao professor para um planejamento de aulas mais eficaz.
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Books on the topic "Hybrid recommendation system"

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El-Tawil, Sherif. Recommendations for seismic design of hybrid coupled wall systems. SEI/America Society of Civil Engineers, 2010.

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Sherif, El-Tawil, and ASCE Technical Committee on Composite Construction., eds. Recommendations for seismic design of hybrid coupled wall systems. America Society of Civil Engineers, 2009.

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Book chapters on the topic "Hybrid recommendation system"

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Passi, Rohan, Surbhi Jain, and Pramod Kumar Singh. "Hybrid Approach for Recommendation System." In Proceedings of the 2nd International Conference on Data Engineering and Communication Technology. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-1610-4_12.

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Jain, Kartik Narendra, Vikrant Kumar, Praveen Kumar, and Tanupriya Choudhury. "Movie Recommendation System: Hybrid Information Filtering System." In Intelligent Computing and Information and Communication. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7245-1_66.

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Yadav, Naina, Rajesh Kumar Mundotiya, Anil Kumar Singh, and Sukomal Pal. "Diversity in Recommendation System: A Cluster Based Approach." In Hybrid Intelligent Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49336-3_12.

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Dong, Jie, and Gui Li. "Hybrid Filtering Recommendation System for Libraries." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5959-4_119.

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Doke, Niket, and Deepali Joshi. "Song Recommendation System Using Hybrid Approach." In Proceeding of International Conference on Computational Science and Applications. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0790-8_31.

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Amara, Indraneel, K. Sai Pranav, and H. R. Mamatha. "Hybrid Recommendation System for Scientific Literature." In Intelligent Data Communication Technologies and Internet of Things. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-9509-7_59.

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Parikh, Dhairya, Dilpreet Kaur, Kajal Parikh, Prakhar Yadav, and Hemant Rathore. "Movie Recommendation System Addressing Changes in User Preferences with Time." In Hybrid Intelligent Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-73050-5_48.

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Gupta, Shefali, and Meenu Dave. "A Hybrid Recommendation System for E-commerce." In Algorithms for Intelligent Systems. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-3246-4_18.

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Jeong, HwaYoung, and BongHwa Hong. "Best Recommendation Using Topic Map for On-Line Shopping System." In Convergence and Hybrid Information Technology. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24106-2_70.

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Chang, Na, Mhd Irvan, and Takao Terano. "Designing a Hybrid Recommendation System for TV Content." In Intelligent Decision Technology Support in Practice. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21209-8_13.

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Conference papers on the topic "Hybrid recommendation system"

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Chen, Jen-Hsiang, Kuo-Ming Chao, and Nazaraf Shah. "Hybrid Recommendation System for Tourism." In 2013 IEEE 10th International Conference on e-Business Engineering (ICEBE). IEEE, 2013. http://dx.doi.org/10.1109/icebe.2013.24.

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Jayathilaka, Dineth Keshawa, Gayumi Nimesha Kottage, Kapuliyanage Chasika Chankuma, Gamage Upeksha Ganegoda, and Thanuja Sandanayake. "Hybrid Weight Factorization Recommendation System." In 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer). IEEE, 2018. http://dx.doi.org/10.1109/icter.2018.8615467.

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Coelho, Bruno, Fernando Costa, and Gil M. Gonçalves. "Hyred - HYbrid Job REcommenDation System." In International Conference on e-Business. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005569200290038.

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Tseng, C. C. "Portfolio management using hybrid recommendation system." In IEEE International Conference on e-Technology, e-Commerce and e-Service, 2004. EEE '04. 2004. IEEE, 2004. http://dx.doi.org/10.1109/eee.2004.1287310.

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Song, Yannan, Wei Ji, and Shi Liu. "Research on personalized hybrid recommendation system." In 2017 International Conference on Computer, Information and Telecommunication Systems (CITS). IEEE, 2017. http://dx.doi.org/10.1109/cits.2017.8035321.

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Patel, Angira Amit, and Jyotindra N. Dharwa. "Fuzzy Based Hybrid Mobile Recommendation System." In the Second International Conference. ACM Press, 2016. http://dx.doi.org/10.1145/2905055.2905205.

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Xiang, Bin, Zhongnan Zhang, Huaili Dong, Qingfeng Wu, and Lei Hu. "Research of mobile recommendation system based on hybrid recommendation technology." In 2013 3rd International Conference on Consumer Electronics, Communications and Networks (CECNet). IEEE, 2013. http://dx.doi.org/10.1109/cecnet.2013.6703381.

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Patil, Pritish, Jiayun Wang, Yuya Aratani, and Kyoji Kawagoe. "Prototyping a recommendation system for Ukiyo-e using hybrid recommendation algorithm." In 2017 Twelfth International Conference on Digital Information Management (ICDIM). IEEE, 2017. http://dx.doi.org/10.1109/icdim.2017.8244658.

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Tong, Junyu, Hongyuan Ma, Wei Liu, and Bo Wang. "Time and location-based hybrid recommendation system." In 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA). IEEE, 2017. http://dx.doi.org/10.1109/icbda.2017.8078721.

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Kanetkar, Salil, Akshay Nayak, Sridhar Swamy, and Gresha Bhatia. "Web-based personalized hybrid book recommendation system." In 2014 International Conference on Advances in Engineering and Technology Research (ICAETR). IEEE, 2014. http://dx.doi.org/10.1109/icaetr.2014.7012952.

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