Relevant bibliographies by topics / Collaborative and content based filtering

Contents

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

Academic literature on the topic 'Collaborative and content based filtering'

Author: Grafiati
Published: 3 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Collaborative and content based filtering"

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Jalali, Meysam, Hojjat Gholizadeh, and Seyyed Alireza Hashemi Golpayegani. "An improved hybrid recommender system based on collaborative filtering, content based, and demographic filtering." International Journal of Academic Research 6, no. 6 (2014): 22–28. http://dx.doi.org/10.7813/2075-4124.2014/6-6/a.3.

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Wu, Xinyi. "Comparison Between Collaborative Filtering and Content-Based Filtering." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 480–89. http://dx.doi.org/10.54097/hset.v16i.2627.

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With the rapid development of Internet technology nowadays, how to quickly obtain the effective information needed by users has become the key point of the scientific and technological academia. Therefore, various kinds of recommendation algorithms have been invented. Based on the previous research, this paper introduces the most famous and widely used recommendation algorithms among many recommendation systems, which are collaborative filtering and content-based filtering. In this paper, the core ideas and operation principles of the two algorithms are introduced in detail. In addition, by de
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Spoorthi, Chinivar. "Personalized Recommendations of Products to Users." International Journal of Recent Technology and Engineering (IJRTE) 11, no. 3 (2022): 105–9. https://doi.org/10.35940/ijrte.C7274.0911322.

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<strong>Abstract:</strong> Many organizations utilize recommendation systems to increase their profitability and win over their customers, including Facebook, which suggests friends, LinkedIn, which promotes employment, Spotify, which recommends music, Netflix, which recommends movies, and Amazon, which recommends purchases. When it comes to movie recommendation system, suggestions are made based on user similarities (collaborative filtering) or by considering a specific user&#39;s behavior (content-based filtering) that he or she wishes to interact with. Using TF-IDF, cosine similarity method
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Manmohan, Singh*1 &. Madhavi Shivhare2. "COMPARATIVE ANALYSIS ASSOCIATION RULE BASED COLLABORATIVE FILTERING." GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES 6, no. 1 (2019): 211–19. https://doi.org/10.5281/zenodo.2553258.

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The World Wide Web have brought us an overabundant knowledge in varied fields and as a result of the data or information overloading, it is very arduous to find out related data. So, Recommendation System comes into existence. The main goal of this system is to recommend the best suitable items to the user or customer. The suggestions pertinent to decision making processes, like what things to obtain, which new music to listen to, which on-line latest news to search, or which image is best one from all. The advantages of recommendation system depend on efficiency of the system. The efficiency
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Necheporuk, Oleksandr, Svitlana Vashchenko, Nataliia Fedotova, Iryna Baranova, and Yaroslava Dehtiarenko. "ANALYSIS OF CONTENT RECOMMENDATION METHODS IN INFORMATION SERVICES." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 14, no. 3 (2024): 105–8. http://dx.doi.org/10.35784/iapgos.6203.

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The object of the research is the process of selecting a content recommendation method in information services. The study's relevance stems from the rapid development of informational and entertainment resources and the increasing volume of data they operate on, thus prompting the utilisation of recommendation systems to maintain user engagement. Considering the different types of content, it is necessary to address the problem of data filtration based on their characteristics and user preferences. To solve this task, we analysed content-based and collaborative filtering methods using various
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Venkata, Bhanu Prasad Tolety, and Venkateswara Prasad Evani. "Hybrid content and collaborative filtering based recommendation system for e-learning platforms." Bulletin of Electrical Engineering and Informatics 11, no. 3 (2022): 1543~1549. https://doi.org/10.11591/eei.v11i3.3861.

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Recommendation systems, although a well-studied topic, experience several shortcomings when applied on e-learning platforms. While collaborative filtering methods have enjoyed great success in making recommendations on large scale e-commerce and social networking and observation, users of elearning platforms have continually evolving preferences, which render collaborative filtering methods weak. On the other end of the spectrum are content-based filtering approaches. Although such methods are more suited for e-learning platforms, the primary concern is that these methods find it hard to gener
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B.Thorat, Poonam, R. M. Goudar, and Sunita Barve. "Survey on Collaborative Filtering, Content-based Filtering and Hybrid Recommendation System." International Journal of Computer Applications 110, no. 4 (2015): 31–36. http://dx.doi.org/10.5120/19308-0760.

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Fu, Lei, and XiaoMing Ma. "An Improved Recommendation Method Based on Content Filtering and Collaborative Filtering." Complexity 2021 (May 28, 2021): 1–11. http://dx.doi.org/10.1155/2021/5589285.

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With the popularization of the Internet and the prevalence of online marketing, e-commerce systems provide enterprises with unlimited display space and provide customers with more product choices, while its structure is becoming increasingly complex. The emergence and application of the network marketing recommendation system have greatly improved this series of problems. It can effectively retain customers, prevent customer loss, and increase the cross-selling volume of the e-commerce system. However, the current network marketing recommendation system is still immature in practical applicati
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Yanisa Putri, Komang Sri, I Made Agus Dwi Suarjaya, and Wayan Oger Vihikan. "Sistem Rekomendasi Skincare Menggunakan Metode Content Based Filtering dan Collaborative Filtering." Decode: Jurnal Pendidikan Teknologi Informasi 4, no. 3 (2024): 764–74. http://dx.doi.org/10.51454/decode.v4i3.601.

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Seiring berjalannya waktu, setiap orang akan mengikuti perkembangan zaman yang semakin modern dalam segala hal terutama dalam hal perawatan kesehatan kulit wajah yang mengakibatkan kebutuhan akan produk perawatan kulit sangat diperlukan. Meningkatnya penggunaan teknologi digital dan maraknya berbagai macam jenis skincare membuat sistem rekomendasi produk skincare menjadi semakin penting. Sistem rekomendasi untuk pemilihan skincare ini dibuat untuk dapat merekomendasikan skincare yang cocok dengan tipe wajah pengguna serta kandungan yang sesuai dengan kulit wajah pengguna berdasarkan preferensi
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Nurjayanto, Bagus Wicaksono, and Z. K. A. Baizal. "NEWS RECOMMENDER SYSTEM USING HYBRID CONTENT-BASED FILTERING AND COLLABORATIVE FILTERING." JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) 9, no. 1 (2024): 26–33. http://dx.doi.org/10.29100/jipi.v9i1.4256.

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The development of online news services has offered users numerous choices, resulting in information overload. This makes it challenging for users to locate desired news within a spesific timeframe. to adress this, recommender systems have developed to help users discover and select news article.
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Dissertations / Theses on the topic "Collaborative and content based filtering"

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Torres, Júnior Roberto Dias. "Combining collaborative and content-based filtering to recommend research papers." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2004. http://hdl.handle.net/10183/5887.

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The number of research papers available today is growing at a staggering rate, generating a huge amount of information that people cannot keep up with. According to a tendency indicated by the United States’ National Science Foundation, more than 10 million new papers will be published in the next 20 years. Because most of these papers will be available on the Web, this research focus on exploring issues on recommending research papers to users, in order to directly lead users to papers of their interest. Recommender systems are used to recommend items to users among a huge stream of available
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Karaman, Hilal. "A Content Based Movie Recommendation System Empowered By Collaborative Missing Data Prediction." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612037/index.pdf.

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The evolution of the Internet has brought us into a world that represents a huge amount of information items such as music, movies, books, web pages, etc. with varying quality. As a result of this huge universe of items, people get confused and the question &ldquo<br>Which one should I choose?&rdquo<br>arises in their minds. Recommendation Systems address the problem of getting confused about items to choose, and filter a specific type of information with a specific information filtering technique that attempts to present information items that are likely of interest to the user. A variety of
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Dias, Pedro Ricardo Gomes. "Recommending media content based on machine learning methods." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/6581.

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Dissertação para obtenção do Grau de Mestre em Engenharia Informática<br>Information is nowadays made available and consumed faster than ever before. This information technology generation has access to a tremendous deal of data and is left with the heavy burden of choosing what is relevant. With the increasing growth of media sources, the amount of content made available to users has become overwhelming and in need to be managed. Recommender systems emerged with the purpose of providing personalized and meaningful content recommendations based on users’ preferences and usage history. Due
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Ozbal, Gozde. "A Content Boosted Collaborative Filtering Approach For Movie Recommendation Based On Local &amp." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610984/index.pdf.

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Recently, it has become more and more difficult for the existing web based systems to locate or retrieve any kind of relevant information, due to the rapid growth of the World Wide Web (WWW) in terms of the information space and the amount of the users in that space. However, in today&#039<br>s world, many systems and approaches make it possible for the users to be guided by the recommendations that they provide about new items such as articles, news, books, music, and movies. However, a lot of traditional recommender systems result in failure when the data to be used throughout the recommenda
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Sahinkaya, Ferhat. "A Content Boosted Collaborative Filtering Approach For Recommender Systems Based On Multi Level And Bidirectional Trust Data." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612013/index.pdf.

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As the Internet became widespread all over the world, people started to share great amount of data on the web and almost every people joined different data networks in order to have a quick access to data shared among people and survive against the information overload on the web. Recommender systems are created to provide users more personalized information services and to make data available for people without an extra effort. Most of these systems aim to get or learn user preferences, explicitly or implicitly depending to the system, and guess &ldquo<br>preferable data&rdquo<br>that has not
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Bergqvist, Martin, and Jim Glansk. "Fördelar med att applicera Collaborative Filtering på Steam : En utforskande studie." Thesis, Högskolan i Borås, Akademin för bibliotek, information, pedagogik och IT, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-14129.

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Rekommendationssystem används överallt. På populära plattformar såsom Netflix och Amazon får du alltid rekommendationer på vad som är nästa lämpliga film eller inköp, baserat på din personliga profil. Detta sker genom korsreferering mellan användare och produkter för att finna sannolika mönster. Syftet med studien har varit att jämföra de två prevalenta tillvägagångssätten att skapa rekommendationer, på en annorlunda datamängd, där ”best practice” inte nödvändigtvis är tillämpbart. Som följd därav, har jämförelse gjorts på effektiviteten av Content-based Filtering kontra Collaborative Filterin
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Andersson, Morgan. "Personal news video recommendations based on implicit feedback : An evaluation of different recommender systems with sparse data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-234137.

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The amount of video content online will nearly triple in quantity by 2021 compared to 2016. The implementation of sophisticated filters is of paramount importance to manage this information flow. The research question of this thesis asks to what extent it is possible to generate personal recommendations, based on the data that news videos implies. The objective is to evaluate how different recommender systems compare to complete random, each other and how they are received by users in a test environment. This study was performed during the spring of 2018, and explore four different algorithms.
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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
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Rudakova, Olga. "User- and system initiated approaches to content discovery." Thesis, Linnéuniversitetet, Institutionen för datavetenskap (DV), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-40674.

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Social networking has encouraged users to find new ways to create, post, search, collaborate and share information of various forms. Unfortunately there is a lot of data in social networks that is not well-managed, which makes the experience within these networks less than optimal. Therefore people generally need more and more time as well as advanced tools that are used for seeking relevant information. A new search paradigm is emerging, where the user perspective is completely reversed: from finding to being found. The aim of present thesis research is to evaluate two approaches of identifyi
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Karanam, Manikanta Babu. "Tackling the problems of diversity in recommender systems." Thesis, Kansas State University, 2010. http://hdl.handle.net/2097/6981.

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Master of Science<br>Department of Computing and Information Sciences<br>William H. Hsu<br>A recommender system is a computational mechanism for information filtering, where users provide recommendations (in the form of ratings or selecting items) as inputs, which the system then aggregates and directs to appropriate recipients. With the advent of web based media and publicity methods, the age where standardized methods of publicity, sales, production and marketing strategies do not. As such, in many markets the users are given a wide range of products and information to choose which product t
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Books on the topic "Collaborative and content based filtering"

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Bouza, Amancio. Hypothesis-Based Collaborative Filtering. Lulu Press, Inc., 2012.

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Investigating Disciplinary Literacy: A Framework for Collaborative Professional Learning. Harvard Education Press, 2017.

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Naeyong kiban yuhae chŏngbo pangji kisul kaebal e kwanhan yŏnʼgu =: A study on the development of content-based harmful information prevention technology. Chŏngbo Tʻongsinbu, 2006.

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Li, Xuanxi. Design and Development of a Wiki-Based Collaborative Process Writing Pedagogy: Putting Technological, Pedagogical, and Content Knowledge in Action. World Scientific Publishing Co Pte Ltd, 2021.

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Li, Xuanxi. Design and Development of a Wiki-Based Collaborative Process Writing Pedagogy: Putting Technological, Pedagogical, and Content Knowledge in Action. World Scientific Publishing Co Pte Ltd, 2021.

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Michaelsen, Larry K., Arletta Bauman Knight, and L. Dee Fink, eds. Team-Based Learning. Praeger, 2002. http://dx.doi.org/10.5040/9798216023364.

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Team learning is an especially powerful way of using small groups. Different authors have used different terms when writing about small groups: learning groups, collaborative learning, cooperative learning, and team learning. Despite the varying terminology, all refer to the same idea: putting individual students in a class into small groups for the purpose of promoting more active and more effective learning. By creating a course structure that involves small groups in the initial acquisition of course content, in learning how to apply that content, and in the assessment of student learning,
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Nadler, Anthony M. Popularizing News 2.0. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252040146.003.0005.

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This chapter examines attempts to popularize and democratize news online through collaborative filtering. Collaborative filtering offers a means to replace the role of professional editors in setting the news agenda and deciding which stories deserve the most prominence. Instead of professional editors, collaborative filtering relies on algorithms to sort, rank, and prioritize the news based on the activity of large groups of web users. Various news sites have added some aspect of collaborative filtering, but the chapter focuses on social news sites (Reddit, Newsvine, and Slashdot) because the
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Puis, Danielle N. Du, and Lori M. Mazursky. Destination Collaboration 2. ABC-CLIO, LLC, 2010. http://dx.doi.org/10.5040/9798400639449.

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This comprehensive guide empowers library media specialists to achieve full instructional collaboration, providing curriculum-coordinated lesson plans for grades 3–5, teaching content while fully integrating information literacy and technology skills. Destination Collaboration 2: A Complete Reference Focused Curriculum Guidebook to Educate 21st Century Learners in Grades 3–5 is a reference-focused book containing four chapters: Encyclopedias, Atlases, Almanacs, and Biographies. Each includes two lesson plans for each grade level (3rd, 4th, and 5th). Content-focused, learner-driven, and based o
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Kulesza, Joanna, and Roy Balleste, eds. Cybersecurity and Human Rights in the Age of Cyberveillance. ROWMAN & LITTLEFIELD, 2015. https://doi.org/10.5040/9798881815189.

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Cybersecurity and Human Rights in the Age of Cyberveillance isa collection of articles by distinguished authors from the US and Europe and presents a contemporary perspectives on the limits online of human rights. By considering the latest political events and case law, including the NSA PRISM surveillance program controversy, the planned EU data protection amendments, and the latest European Court of Human Rights jurisprudence, it provides an analysis of the ongoing legal discourse on global cyberveillance. Using examples from contemporary state practice, including content filtering and Inter
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Mazursky, Lori M., and Danielle N. Du Puis. Destination Collaboration 1. ABC-CLIO, LLC, 2010. http://dx.doi.org/10.5040/9798400639432.

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This comprehensive guide empowers library media specialists to achieve full instructional collaboration, providing curriculum-coordinated lesson plans for grades 3–5, teaching content while fully integrating information literacy and technology skills. Destination Collaboration 1: A Complete Research Focused Curriculum Guidebook to Educate 21st Century Learners in Grades 3–5 is a research-focused book containing four chapters: Note Taking, Public Access Catalog, Informational Text, and Online Resources. Each includes two or three lesson plans for each grade level (3rd, 4th, and 5th). Content-fo
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Book chapters on the topic "Collaborative and content based filtering"

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Kar, Pushpendu, Monideepa Roy, and Sujoy Datta. "Collaborative Filtering and Content-Based Systems." In Recommender Systems: Algorithms and their Applications. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0538-2_3.

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Meel, Priyanka, Farhin Bano, Agniva Goswami, and Saloni Gupta. "Movie Recommendation Using Content-Based and Collaborative Filtering." In Advances in Intelligent Systems and Computing. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5113-0_22.

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Nguyen, Minh Luan, Siu Cheung Hui, and Alvis C. M. Fong. "Content-Based Collaborative Filtering for Question Difficulty Calibration." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32695-0_33.

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Jung, Kyung-Yong, Dong-Hyun Park, and Jung-Hyun Lee. "Hybrid Collaborative Filtering and Content-Based Filtering for Improved Recommender System." In Computational Science - ICCS 2004. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24685-5_37.

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Do Thi, Lien, and Phuong Nguyen Duy. "PageRank-Based Context-Aware Collaborative Filtering." In Lecture Notes in Networks and Systems. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-92545-0_36.

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Paulson, Patrick, and Aimilia Tzanavari. "Combining Collaborative and Content-Based Filtering Using Conceptual Graphs." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39906-3_9.

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Rojsattarat, Ekkawut, and Nuanwan Soonthornphisaj. "Hybrid Recommendation: Combining Content-Based Prediction and Collaborative Filtering." In Intelligent Data Engineering and Automated Learning. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45080-1_44.

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Jha, Anjali, Nidhi Agarwal, Devendra K. Tayal, et al. "Movie Recommendation Using Content-Based and Collaborative Filtering Approach." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-35078-8_37.

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Pradhan, Rahul, Jyoti Varshney, Kartik Goyal, and Latesh Kumari. "Job Recommendation System Using Content and Collaborative-Based Filtering." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2594-7_47.

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Phuong, Nguyen Duy, Le Quang Thang, and Tu Minh Phuong. "A Graph-Based Method for Combining Collaborative and Content-Based Filtering." In PRICAI 2008: Trends in Artificial Intelligence. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89197-0_80.

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Conference papers on the topic "Collaborative and content based filtering"

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Mouhiha, Mohamed, Omar Ait Oualhaj, and Abdelfettah Mabrouk. "Combining Collaborative Filtering and Content Based Filtering for Recommendation Systems." In 2024 11th International Conference on Wireless Networks and Mobile Communications (WINCOM). IEEE, 2024. http://dx.doi.org/10.1109/wincom62286.2024.10658169.

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Vemberly, Esther, Eugenia Ancilla, Anderies Anderies, and Andry Chowanda. "Enhancing Movie Recommendations: A Hybrid Filtering Approach Combining Collaborative and Content-Based Filtering." In 2023 International Conference on Artificial Intelligence Robotics, Signal and Image Processing (AIRoSIP). IEEE, 2023. https://doi.org/10.1109/airosip58759.2023.10873881.

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Kesarwani, Vansh Kumar, and Ajay Pratap. "Model for Movie Recommendation System using Collaborative Filtering and Content-Based Filtering Techniques." In 2025 2nd International Conference on Computational Intelligence, Communication Technology and Networking (CICTN). IEEE, 2025. https://doi.org/10.1109/cictn64563.2025.10932600.

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Reddy, Pennabadi Devendra, K. Satya Sampath Reddy, P. Gnaneswarachary, et al. "Enhancing Content Based Collaborative Filtering Recommendations Using Weighted Word Embeddings." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724582.

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Gupta, Sanjai, Vinodkumar Kakde, Ravindra R. Dharamshi, Gopal Sakarkar, and Agus Putu Abiyasa. "Employment Prediction System Based on Recommender System Using NLP, Content Filtering & Collaborative Filtering." In 2024 10th International Conference on Smart Computing and Communication (ICSCC). IEEE, 2024. http://dx.doi.org/10.1109/icscc62041.2024.10690402.

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Aliasgari, Malihe, Haoru Li, Ying Zhang, Yousef Nejatbakhsh, and Roya Nejatbakhsh. "Recommending Healthy Food for Phenylketonuria with Collaborative and Content-Based Filtering." In 2024 IEEE International Conference on E-health Networking, Application & Services (HealthCom). IEEE, 2024. https://doi.org/10.1109/healthcom60970.2024.10880815.

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Naveen, S., G. Vinoth Raikumar, R. Santhana Krishnan, R. Umesh, A. Alice Blessie, and Kruthika Paulraj. "Social Media-Based Travel Planning: Integrating Content-Based and Collaborative Filtering for Personalized Suggestions." In 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025. https://doi.org/10.1109/icmsci62561.2025.10893927.

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Yang, Yongmao, and Kampol Woradit. "Hybrid Movie Recommendation System with Content-Based and Memory-Based Collaborative Filtering Using Similarity Graph." In 2024 23rd International Symposium on Communications and Information Technologies (ISCIT). IEEE, 2024. https://doi.org/10.1109/iscit63075.2024.10793690.

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Adillion, Ilham Gurat, Muhammad Farras Pangestu, Dini Adni Navastara, Yudhi Purwananto, Rully Soelaiman, and Diana Purwitasari. "Topic Recommendation with Article Metadata using Collaborative and Content-based Filtering on Author Activeness Profiling." In 2025 International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS). IEEE, 2025. https://doi.org/10.1109/icadeis65852.2025.10933489.

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Amangeldieva, Aruzhan, and Chingis Kharmyssov. "A hybrid approach for a movie recommender system using Content-Based, Collaborative and Knowledge-Based Filtering methods." In 2024 IEEE 4th International Conference on Smart Information Systems and Technologies (SIST). IEEE, 2024. http://dx.doi.org/10.1109/sist61555.2024.10629294.

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Reports on the topic "Collaborative and content based filtering"

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Fusco, Judi, Sarah DiMaria, Beverly Yeadon, Janet Kolodner, and Alison Shell. Assessment During Collaborative Learning. Digital Promise, 2025. https://doi.org/10.51388/20.500.12265/252.

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This primer explores assessment during collaborative learning, shifting the focus from traditional summative evaluation to ongoing formative assessment for learning. It addresses key questions regarding what should be assessed, individual knowledge, group dynamics, collaborative skills, or both; and how assessments can be designed to align with learning objectives. The document outlines effective practices for incorporating self- and peer-assessment to empower students and provides guidance on delivering meaningful feedback. It discusses the importance of balancing individual and group assessm
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Mathur, Chhavi, Sara Ahmed, Aakriti Parasha, et al. Development of Water Classrooms for Middle School Students. Indian Institute for Human Settlements, 2023. http://dx.doi.org/10.24943/tesf1206.2023.

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Water, recognised by United Nations’ Sustainable Development Goal 6, is essential to sustain all life. It intersects with various aspects of our civilisation, heritage, health, and survival. In this project, we developed pedagogical tools using place-based, multidisciplinary, imaginal, and interactive content for middle school students. The expected outcome of this pedagogy is to equip students with knowledge and core competencies such as critical transdisciplinary analysis, systems thinking, and collaborative decision-making that are essential to reimagine just, resilient, and equitable water
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Oleksiuk, Vasyl P., and Olesia R. Oleksiuk. Exploring the potential of augmented reality for teaching school computer science. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4404.

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The article analyzes the phenomenon of augmented reality (AR) in education. AR is a new technology that complements the real world with the help of computer data. Such content is tied to specific locations or activities. Over the last few years, AR applications have become available on mobile devices. AR becomes available in the media (news, entertainment, sports). It is starting to enter other areas of life (such as e-commerce, travel, marketing). But education has the biggest impact on AR. Based on the analysis of scientific publications, the authors explored the possibilities of using augme
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Colomb, Claire, and Tatiana Moreira de Souza. Regulating Short-Term Rentals: Platform-based property rentals in European cities: the policy debates. Property Research Trust, 2021. http://dx.doi.org/10.52915/kkkd3578.

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Short-term rentals mediated by digital platforms have positive and negative impacts that are unevenly distributed among socio-economic groups and places. Detrimental impacts on the housing market and quality of life of long-term residents have been particular contentious in some cities. • In the 12 cities studied in the report (Amsterdam, Barcelona, Berlin, Brussels, Lisbon, London, Madrid, Milan, Paris, Prague, Rome and Vienna), city governments have responded differently to the growth of short-term rentals. • The emerging local regulations of short-term rentals take multiple forms and exhibi
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Pikilnyak, Andrey V., Nadia M. Stetsenko, Volodymyr P. Stetsenko, Tetiana V. Bondarenko, and Halyna V. Tkachuk. Comparative analysis of online dictionaries in the context of the digital transformation of education. [б. в.], 2021. http://dx.doi.org/10.31812/123456789/4431.

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The article is devoted to a comparative analysis of popular online dictionaries and an overview of the main tools of these resources to study a language. The use of dictionaries in learning a foreign language is an important step to understanding the language. The effectiveness of this process increases with the use of online dictionaries, which have a lot of tools for improving the educational process. Based on the Alexa Internet resource it was found the most popular online dictionaries: Cambridge Dictionary, Wordreference, Merriam–Webster, Wiktionary, TheFreeDictionary, Dictionary.com, Glos
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Mitchell, Brian G., Amir Neori, Charles Yarish, D. Allen Davis, Tzachi Samocha, and Lior Guttman. The use of aquaculture effluents in spray culture for the production of high protein macroalgae for shrimp aqua-feeds. United States Department of Agriculture, 2013. http://dx.doi.org/10.32747/2013.7597934.bard.

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The FAO has projected a doubling in world demand for seafood during the 21 ed from aquaculture of marine fish and shrimps fed primarily on fishmeal-based aquafeeds. However, current practices of high intensity monoculture of shrimp in coastal ponds and fish in offshore pens have been strongly criticized as being ecologically and socially unsustainable. This view derives from un- checked eutrophication of coastal marine ecosystems from fish farm effluents, and the destruction of coastal estuarine ecosystems by shrimp farm constructions, plus aquaculture’s reliance on wild-caught small fish - wh
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Veung, Naron. Enhancing the Collaboration between TVET Institutions and the Private Sector in Cambodia: Implications for Skills Provision. Cambodia Development Resource Institute, 2024. https://doi.org/10.64202/cdri.

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Collaboration with the private sector has been an influential tool in addressing a variety of complex issues or problems in both developed and developing economies around the globe. Like many countries, Cambodia has strongly promoted collaboration with the private sector across all education sectors. In the Cambodian TVET sector, the Royal Government of Cambodia has emphasised public-private partnerships (PPP) in its national TVET Policy 2017-2025 and Skills Development Roadmap 2023-2035. Such collaboration can make TVET more industry-relevant by increasing the private sector’s role, duties, a
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Naron, Veung. Enhancing the Collaboration between TVET Institutions and the Private Sector in Cambodia: Implications for Skills Provision. Cambodia Development Resource Institute, 2024. https://doi.org/10.64202/wp-150.

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Collaboration with the private sector has been an influential tool in addressing a variety of complex issues or problems in both developed and developing economies around the globe. Like many countries, Cambodia has strongly promoted collaboration with the private sector across all education sectors. In the Cambodian TVET sector, the Royal Government of Cambodia has emphasised public-private partnerships (PPP) in its national TVET Policy 2017-2025 and Skills Development Roadmap 2023-2035. Such collaboration can make TVET more industry-relevant by increasing the private sector’s role, duties, a
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Veung, Naron. Enhancing the Collaboration between TVET Institutions and the Private Sector in Cambodia: Implications for Skills Provision. Cambodia Development Resource Institute, 2024. https://doi.org/10.64202/wp.150.202411.

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Abstract:
Collaboration with the private sector has been an influential tool in addressing a variety of complex issues or problems in both developed and developing economies around the globe. Like many countries, Cambodia has strongly promoted collaboration with the private sector across all education sectors. In the Cambodian TVET sector, the Royal Government of Cambodia has emphasised public-private partnerships (PPP) in its national TVET Policy 2017-2025 and Skills Development Roadmap 2023-2035. Such collaboration can make TVET more industry-relevant by increasing the private sector’s role, duties, a
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Cox-Ammann, Karah, Mirella Cortez, Eric Dinger, and Alexis Necarsulmer. Rocky intertidal monitoring: 2021–2022 results from Redwood National and State Parks. National Park Service, 2025. https://doi.org/10.36967/2309092.

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This report presents survey results from 2021 and 2022 for the rocky intertidal community at 3 sites at Redwood National and State Parks (RNSP) in Del Norte County, California. These sites are part of MARINe (Multi-Agency Rocky Intertidal Network), a regional intertidal monitoring network sponsored by the Bureau of Ocean Energy Management (BOEM), with additional funding and support from local and state governments, universities, and private organizations (see www.marine.ucsc.edu). Funding for annual monitoring in RNSP is provided by the National Park Service (NPS) Klamath Network (KLMN) throug
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