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Degree: MS

You searched for subject:(Collaborative filtering). Showing records 1 – 20 of 20 total matches.

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University of Georgia

1. Fan, Xiaohu. A study of attacks on collaborative filter.

Degree: MS, Computer Science, 2011, University of Georgia

Collaborative filtering is a widely used technique to make classifications by using distributed feedback from all users. Recently, collaborative filtering has been proposed and used… (more)

Subjects/Keywords: Collaborative spam filtering

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APA (6th Edition):

Fan, X. (2011). A study of attacks on collaborative filter. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/fan_xiaohu_201105_ms

Chicago Manual of Style (16th Edition):

Fan, Xiaohu. “A study of attacks on collaborative filter.” 2011. Masters Thesis, University of Georgia. Accessed June 16, 2019. http://purl.galileo.usg.edu/uga_etd/fan_xiaohu_201105_ms.

MLA Handbook (7th Edition):

Fan, Xiaohu. “A study of attacks on collaborative filter.” 2011. Web. 16 Jun 2019.

Vancouver:

Fan X. A study of attacks on collaborative filter. [Internet] [Masters thesis]. University of Georgia; 2011. [cited 2019 Jun 16]. Available from: http://purl.galileo.usg.edu/uga_etd/fan_xiaohu_201105_ms.

Council of Science Editors:

Fan X. A study of attacks on collaborative filter. [Masters Thesis]. University of Georgia; 2011. Available from: http://purl.galileo.usg.edu/uga_etd/fan_xiaohu_201105_ms


Penn State University

2. Yao, Luqi. Study On Bipartite Network In Collaborative Filtering Recommender System.

Degree: MS, Industrial Engineering, 2015, Penn State University

 Recommender system is increasingly popular in recent years, scientists came up with plenty of recommendation algorithms and never stop trying to make the recommendation more… (more)

Subjects/Keywords: recommender system; bipartite network; collaborative filtering

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APA (6th Edition):

Yao, L. (2015). Study On Bipartite Network In Collaborative Filtering Recommender System. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/26335

Chicago Manual of Style (16th Edition):

Yao, Luqi. “Study On Bipartite Network In Collaborative Filtering Recommender System.” 2015. Masters Thesis, Penn State University. Accessed June 16, 2019. https://etda.libraries.psu.edu/catalog/26335.

MLA Handbook (7th Edition):

Yao, Luqi. “Study On Bipartite Network In Collaborative Filtering Recommender System.” 2015. Web. 16 Jun 2019.

Vancouver:

Yao L. Study On Bipartite Network In Collaborative Filtering Recommender System. [Internet] [Masters thesis]. Penn State University; 2015. [cited 2019 Jun 16]. Available from: https://etda.libraries.psu.edu/catalog/26335.

Council of Science Editors:

Yao L. Study On Bipartite Network In Collaborative Filtering Recommender System. [Masters Thesis]. Penn State University; 2015. Available from: https://etda.libraries.psu.edu/catalog/26335


Kansas State University

3. Karanam, Manikanta Babu. Tackling the problems of diversity in recommender systems.

Degree: MS, Department of Computing and Information Sciences, 2010, Kansas State University

 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… (more)

Subjects/Keywords: Recommender Systems; Diversity; Collaborative Filtering; Content Based Filtering; Hybrid Systems; Computer Science (0984)

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APA (6th Edition):

Karanam, M. B. (2010). Tackling the problems of diversity in recommender systems. (Masters Thesis). Kansas State University. Retrieved from http://hdl.handle.net/2097/6981

Chicago Manual of Style (16th Edition):

Karanam, Manikanta Babu. “Tackling the problems of diversity in recommender systems.” 2010. Masters Thesis, Kansas State University. Accessed June 16, 2019. http://hdl.handle.net/2097/6981.

MLA Handbook (7th Edition):

Karanam, Manikanta Babu. “Tackling the problems of diversity in recommender systems.” 2010. Web. 16 Jun 2019.

Vancouver:

Karanam MB. Tackling the problems of diversity in recommender systems. [Internet] [Masters thesis]. Kansas State University; 2010. [cited 2019 Jun 16]. Available from: http://hdl.handle.net/2097/6981.

Council of Science Editors:

Karanam MB. Tackling the problems of diversity in recommender systems. [Masters Thesis]. Kansas State University; 2010. Available from: http://hdl.handle.net/2097/6981

4. Phillips, Taylor. Using Transitivity with Nearest Neighbor to Reduce Error in Sample-Based Pearson Correlation Coefficients.

Degree: MS, Computer Science, 2010, U of Denver

  Pearson product-moment correlation coefficients are a well- practiced quantification of linear dependence seen across many fields. When calculating a sample-based correlation coefficient, the accuracy… (more)

Subjects/Keywords: Collaborative Filtering; Correlation Coefficient; Nearest Neighbor; Pearson; Reduce Error

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APA (6th Edition):

Phillips, T. (2010). Using Transitivity with Nearest Neighbor to Reduce Error in Sample-Based Pearson Correlation Coefficients. (Thesis). U of Denver. Retrieved from https://digitalcommons.du.edu/etd/515

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Phillips, Taylor. “Using Transitivity with Nearest Neighbor to Reduce Error in Sample-Based Pearson Correlation Coefficients.” 2010. Thesis, U of Denver. Accessed June 16, 2019. https://digitalcommons.du.edu/etd/515.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Phillips, Taylor. “Using Transitivity with Nearest Neighbor to Reduce Error in Sample-Based Pearson Correlation Coefficients.” 2010. Web. 16 Jun 2019.

Vancouver:

Phillips T. Using Transitivity with Nearest Neighbor to Reduce Error in Sample-Based Pearson Correlation Coefficients. [Internet] [Thesis]. U of Denver; 2010. [cited 2019 Jun 16]. Available from: https://digitalcommons.du.edu/etd/515.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Phillips T. Using Transitivity with Nearest Neighbor to Reduce Error in Sample-Based Pearson Correlation Coefficients. [Thesis]. U of Denver; 2010. Available from: https://digitalcommons.du.edu/etd/515

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Cincinnati

5. NARAYANASWAMY, SHRIRAM. A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS.

Degree: MS, Engineering : Computer Science, 2007, University of Cincinnati

 In today’s consumer driven world, people are faced with the problem of plenty. Choices abound everywhere, be it in movies, books or music. Recommender systems… (more)

Subjects/Keywords: Computer Science; collaborative filtering, recommender systems; lattice, concept, algorithm, Jester, Movielens

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APA (6th Edition):

NARAYANASWAMY, S. (2007). A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1186165016

Chicago Manual of Style (16th Edition):

NARAYANASWAMY, SHRIRAM. “A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS.” 2007. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1186165016.

MLA Handbook (7th Edition):

NARAYANASWAMY, SHRIRAM. “A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS.” 2007. Web. 16 Jun 2019.

Vancouver:

NARAYANASWAMY S. A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS. [Internet] [Masters thesis]. University of Cincinnati; 2007. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1186165016.

Council of Science Editors:

NARAYANASWAMY S. A CONCEPT-BASED FRAMEWORK AND ALGORITHMS FOR RECOMMENDER SYSTEMS. [Masters Thesis]. University of Cincinnati; 2007. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1186165016


University of Georgia

6. Jahangeer, Khalid. An open science approach to exploring time-accuracy trade-offs in recommender systems.

Degree: MS, Computer Science, 2017, University of Georgia

 Recommender Systems have become an integral part of our consumer dominated world. With the evolution of Big Data and the exponential expansion of consumerism it… (more)

Subjects/Keywords: Recommender Systems; Collaborative Filtering; Matrix Factorization; Singular Value Decomposition

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APA (6th Edition):

Jahangeer, K. (2017). An open science approach to exploring time-accuracy trade-offs in recommender systems. (Masters Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/37801

Chicago Manual of Style (16th Edition):

Jahangeer, Khalid. “An open science approach to exploring time-accuracy trade-offs in recommender systems.” 2017. Masters Thesis, University of Georgia. Accessed June 16, 2019. http://hdl.handle.net/10724/37801.

MLA Handbook (7th Edition):

Jahangeer, Khalid. “An open science approach to exploring time-accuracy trade-offs in recommender systems.” 2017. Web. 16 Jun 2019.

Vancouver:

Jahangeer K. An open science approach to exploring time-accuracy trade-offs in recommender systems. [Internet] [Masters thesis]. University of Georgia; 2017. [cited 2019 Jun 16]. Available from: http://hdl.handle.net/10724/37801.

Council of Science Editors:

Jahangeer K. An open science approach to exploring time-accuracy trade-offs in recommender systems. [Masters Thesis]. University of Georgia; 2017. Available from: http://hdl.handle.net/10724/37801


Rochester Institute of Technology

7. Matus Nicodemos, Marcelo. Information-Based Neighborhood Modeling.

Degree: MS, Information Sciences and Technologies (GCCIS), 2017, Rochester Institute of Technology

  Since the inception of the World Wide Web, the amount of data present on websites and internet infrastructure has grown exponentially that researchers continuously… (more)

Subjects/Keywords: Collaborative filtering; Data; DIKW hierarchy; Information; k-Nearest neighbor; Recommender systems

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APA (6th Edition):

Matus Nicodemos, M. (2017). Information-Based Neighborhood Modeling. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9461

Chicago Manual of Style (16th Edition):

Matus Nicodemos, Marcelo. “Information-Based Neighborhood Modeling.” 2017. Masters Thesis, Rochester Institute of Technology. Accessed June 16, 2019. https://scholarworks.rit.edu/theses/9461.

MLA Handbook (7th Edition):

Matus Nicodemos, Marcelo. “Information-Based Neighborhood Modeling.” 2017. Web. 16 Jun 2019.

Vancouver:

Matus Nicodemos M. Information-Based Neighborhood Modeling. [Internet] [Masters thesis]. Rochester Institute of Technology; 2017. [cited 2019 Jun 16]. Available from: https://scholarworks.rit.edu/theses/9461.

Council of Science Editors:

Matus Nicodemos M. Information-Based Neighborhood Modeling. [Masters Thesis]. Rochester Institute of Technology; 2017. Available from: https://scholarworks.rit.edu/theses/9461


Penn State University

8. Gupta, Gaurav. PERSONALIZED AND EFFICIENT TOP-K SPATIAL OBJECT RECOMMENDATION IN LOCATION BASED SOCIAL NETWORKS.

Degree: MS, Computer Science and Engineering, 2011, Penn State University

 Location Based Social Networks (LBSNs) have become popular among people in recent times. LBSN allow people to tag their presence at the places they visit,… (more)

Subjects/Keywords: location based social networks; recommendation systems; social network analysis; collaborative filtering; spatial databases

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APA (6th Edition):

Gupta, G. (2011). PERSONALIZED AND EFFICIENT TOP-K SPATIAL OBJECT RECOMMENDATION IN LOCATION BASED SOCIAL NETWORKS. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/11575

Chicago Manual of Style (16th Edition):

Gupta, Gaurav. “PERSONALIZED AND EFFICIENT TOP-K SPATIAL OBJECT RECOMMENDATION IN LOCATION BASED SOCIAL NETWORKS.” 2011. Masters Thesis, Penn State University. Accessed June 16, 2019. https://etda.libraries.psu.edu/catalog/11575.

MLA Handbook (7th Edition):

Gupta, Gaurav. “PERSONALIZED AND EFFICIENT TOP-K SPATIAL OBJECT RECOMMENDATION IN LOCATION BASED SOCIAL NETWORKS.” 2011. Web. 16 Jun 2019.

Vancouver:

Gupta G. PERSONALIZED AND EFFICIENT TOP-K SPATIAL OBJECT RECOMMENDATION IN LOCATION BASED SOCIAL NETWORKS. [Internet] [Masters thesis]. Penn State University; 2011. [cited 2019 Jun 16]. Available from: https://etda.libraries.psu.edu/catalog/11575.

Council of Science Editors:

Gupta G. PERSONALIZED AND EFFICIENT TOP-K SPATIAL OBJECT RECOMMENDATION IN LOCATION BASED SOCIAL NETWORKS. [Masters Thesis]. Penn State University; 2011. Available from: https://etda.libraries.psu.edu/catalog/11575


Penn State University

9. Ference, Gregory David. Location Recommendation for Mobile Users in Location-Based Social Networks.

Degree: MS, Computer Science and Engineering, 2013, Penn State University

 Location-based services have become popular in the twenty-first century due to technological advances, such as mobile and online social networking. One of its key features… (more)

Subjects/Keywords: Location Recommendation; Location-Based Social Networks; Collaborative Filtering; K-Nearest Diverse Neighbor; Spatial Diversity

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APA (6th Edition):

Ference, G. D. (2013). Location Recommendation for Mobile Users in Location-Based Social Networks. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/17382

Chicago Manual of Style (16th Edition):

Ference, Gregory David. “Location Recommendation for Mobile Users in Location-Based Social Networks.” 2013. Masters Thesis, Penn State University. Accessed June 16, 2019. https://etda.libraries.psu.edu/catalog/17382.

MLA Handbook (7th Edition):

Ference, Gregory David. “Location Recommendation for Mobile Users in Location-Based Social Networks.” 2013. Web. 16 Jun 2019.

Vancouver:

Ference GD. Location Recommendation for Mobile Users in Location-Based Social Networks. [Internet] [Masters thesis]. Penn State University; 2013. [cited 2019 Jun 16]. Available from: https://etda.libraries.psu.edu/catalog/17382.

Council of Science Editors:

Ference GD. Location Recommendation for Mobile Users in Location-Based Social Networks. [Masters Thesis]. Penn State University; 2013. Available from: https://etda.libraries.psu.edu/catalog/17382


Virginia Tech

10. Mirza, Batul J. Jumping Connections: A Graph-Theoretic Model for Recommender Systems.

Degree: MS, Computer Science, 2001, Virginia Tech

 Recommender systems have become paramount to customize information access and reduce information overload. They serve multiple uses, ranging from suggesting products and artifacts (to consumers),… (more)

Subjects/Keywords: Random Graphs; Collaborative Filtering; Recommender Systems

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APA (6th Edition):

Mirza, B. J. (2001). Jumping Connections: A Graph-Theoretic Model for Recommender Systems. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/31370

Chicago Manual of Style (16th Edition):

Mirza, Batul J. “Jumping Connections: A Graph-Theoretic Model for Recommender Systems.” 2001. Masters Thesis, Virginia Tech. Accessed June 16, 2019. http://hdl.handle.net/10919/31370.

MLA Handbook (7th Edition):

Mirza, Batul J. “Jumping Connections: A Graph-Theoretic Model for Recommender Systems.” 2001. Web. 16 Jun 2019.

Vancouver:

Mirza BJ. Jumping Connections: A Graph-Theoretic Model for Recommender Systems. [Internet] [Masters thesis]. Virginia Tech; 2001. [cited 2019 Jun 16]. Available from: http://hdl.handle.net/10919/31370.

Council of Science Editors:

Mirza BJ. Jumping Connections: A Graph-Theoretic Model for Recommender Systems. [Masters Thesis]. Virginia Tech; 2001. Available from: http://hdl.handle.net/10919/31370


University of Windsor

11. Xiao, Ying. Recommending Best Products from E-commerce Purchase History and User Click Behavior Data.

Degree: MS, Computer Science, 2018, University of Windsor

 E-commerce collaborative filtering recommendation systems, the main input data of user-item rating matrix is a binary purchase data showing only what items a user has… (more)

Subjects/Keywords: CF; clickstream history; collaborative filtering; data mining; E-commerce recommendation system; weighted frequent item

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APA (6th Edition):

Xiao, Y. (2018). Recommending Best Products from E-commerce Purchase History and User Click Behavior Data. (Masters Thesis). University of Windsor. Retrieved from https://scholar.uwindsor.ca/etd/7455

Chicago Manual of Style (16th Edition):

Xiao, Ying. “Recommending Best Products from E-commerce Purchase History and User Click Behavior Data.” 2018. Masters Thesis, University of Windsor. Accessed June 16, 2019. https://scholar.uwindsor.ca/etd/7455.

MLA Handbook (7th Edition):

Xiao, Ying. “Recommending Best Products from E-commerce Purchase History and User Click Behavior Data.” 2018. Web. 16 Jun 2019.

Vancouver:

Xiao Y. Recommending Best Products from E-commerce Purchase History and User Click Behavior Data. [Internet] [Masters thesis]. University of Windsor; 2018. [cited 2019 Jun 16]. Available from: https://scholar.uwindsor.ca/etd/7455.

Council of Science Editors:

Xiao Y. Recommending Best Products from E-commerce Purchase History and User Click Behavior Data. [Masters Thesis]. University of Windsor; 2018. Available from: https://scholar.uwindsor.ca/etd/7455


North Carolina State University

12. Sreenath, Raghuram Masti. A Community-Based Rating System for Selecting Among Web Services.

Degree: MS, Computer Science, 2003, North Carolina State University

 The current infrastructure for Web services has a static approach to discover a service. It is based on a common repository that has a simple… (more)

Subjects/Keywords: collaborative filtering; web services; rating systems

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APA (6th Edition):

Sreenath, R. M. (2003). A Community-Based Rating System for Selecting Among Web Services. (Thesis). North Carolina State University. Retrieved from http://www.lib.ncsu.edu/resolver/1840.16/2562

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Sreenath, Raghuram Masti. “A Community-Based Rating System for Selecting Among Web Services.” 2003. Thesis, North Carolina State University. Accessed June 16, 2019. http://www.lib.ncsu.edu/resolver/1840.16/2562.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Sreenath, Raghuram Masti. “A Community-Based Rating System for Selecting Among Web Services.” 2003. Web. 16 Jun 2019.

Vancouver:

Sreenath RM. A Community-Based Rating System for Selecting Among Web Services. [Internet] [Thesis]. North Carolina State University; 2003. [cited 2019 Jun 16]. Available from: http://www.lib.ncsu.edu/resolver/1840.16/2562.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Sreenath RM. A Community-Based Rating System for Selecting Among Web Services. [Thesis]. North Carolina State University; 2003. Available from: http://www.lib.ncsu.edu/resolver/1840.16/2562

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


San Jose State University

13. Shahab, Shehba. NEXT LEVEL: A COURSE RECOMMENDER SYSTEM BASED ON CAREER INTERESTS.

Degree: MS, Computer Science, 2019, San Jose State University

  Skills-based hiring is a talent management approach that empowers employers to align recruitment around business results, rather than around credentials and title. It starts… (more)

Subjects/Keywords: Recommender Systems; Collaborative Filtering; Hybrid Approach; Job search; Artificial Intelligence and Robotics; Other Computer Sciences

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APA (6th Edition):

Shahab, S. (2019). NEXT LEVEL: A COURSE RECOMMENDER SYSTEM BASED ON CAREER INTERESTS. (Masters Thesis). San Jose State University. Retrieved from https://scholarworks.sjsu.edu/etd_projects/684

Chicago Manual of Style (16th Edition):

Shahab, Shehba. “NEXT LEVEL: A COURSE RECOMMENDER SYSTEM BASED ON CAREER INTERESTS.” 2019. Masters Thesis, San Jose State University. Accessed June 16, 2019. https://scholarworks.sjsu.edu/etd_projects/684.

MLA Handbook (7th Edition):

Shahab, Shehba. “NEXT LEVEL: A COURSE RECOMMENDER SYSTEM BASED ON CAREER INTERESTS.” 2019. Web. 16 Jun 2019.

Vancouver:

Shahab S. NEXT LEVEL: A COURSE RECOMMENDER SYSTEM BASED ON CAREER INTERESTS. [Internet] [Masters thesis]. San Jose State University; 2019. [cited 2019 Jun 16]. Available from: https://scholarworks.sjsu.edu/etd_projects/684.

Council of Science Editors:

Shahab S. NEXT LEVEL: A COURSE RECOMMENDER SYSTEM BASED ON CAREER INTERESTS. [Masters Thesis]. San Jose State University; 2019. Available from: https://scholarworks.sjsu.edu/etd_projects/684


University of Florida

14. Alex, Daley. Enhanced Glade and Its Impact on Computational Data Analytics.

Degree: MS, Computer Engineering - Computer and Information Science and Engineering, 2012, University of Florida

 The management and analysis of large amounts of constantly increasing data is required to facilitate better knowledge and understanding. Such analysis extracts less apparent information… (more)

Subjects/Keywords: Aggregation; Analytics; Collaborative filtering; Databases; Datasets; Decision trees; Glades; Mining; Statistical mechanics; Statistics; datapath  – glade  – mahout  – mining

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APA (6th Edition):

Alex, D. (2012). Enhanced Glade and Its Impact on Computational Data Analytics. (Masters Thesis). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0044817

Chicago Manual of Style (16th Edition):

Alex, Daley. “Enhanced Glade and Its Impact on Computational Data Analytics.” 2012. Masters Thesis, University of Florida. Accessed June 16, 2019. http://ufdc.ufl.edu/UFE0044817.

MLA Handbook (7th Edition):

Alex, Daley. “Enhanced Glade and Its Impact on Computational Data Analytics.” 2012. Web. 16 Jun 2019.

Vancouver:

Alex D. Enhanced Glade and Its Impact on Computational Data Analytics. [Internet] [Masters thesis]. University of Florida; 2012. [cited 2019 Jun 16]. Available from: http://ufdc.ufl.edu/UFE0044817.

Council of Science Editors:

Alex D. Enhanced Glade and Its Impact on Computational Data Analytics. [Masters Thesis]. University of Florida; 2012. Available from: http://ufdc.ufl.edu/UFE0044817


Virginia Tech

15. Conry, Donald C. Recommender Systems for the Conference Paper Assignment Problem.

Degree: MS, Computer Science, 2009, Virginia Tech

 Conference paper assignment – the task of assigning paper submissions to reviewers – is a key step in the management and smooth functioning of conferences. We study… (more)

Subjects/Keywords: collaborative filtering; conference paper assignment; conference management

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APA (6th Edition):

Conry, D. C. (2009). Recommender Systems for the Conference Paper Assignment Problem. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/33236

Chicago Manual of Style (16th Edition):

Conry, Donald C. “Recommender Systems for the Conference Paper Assignment Problem.” 2009. Masters Thesis, Virginia Tech. Accessed June 16, 2019. http://hdl.handle.net/10919/33236.

MLA Handbook (7th Edition):

Conry, Donald C. “Recommender Systems for the Conference Paper Assignment Problem.” 2009. Web. 16 Jun 2019.

Vancouver:

Conry DC. Recommender Systems for the Conference Paper Assignment Problem. [Internet] [Masters thesis]. Virginia Tech; 2009. [cited 2019 Jun 16]. Available from: http://hdl.handle.net/10919/33236.

Council of Science Editors:

Conry DC. Recommender Systems for the Conference Paper Assignment Problem. [Masters Thesis]. Virginia Tech; 2009. Available from: http://hdl.handle.net/10919/33236


Virginia Tech

16. Chan, William Hannibal. SNAP Biclustering.

Degree: MS, Electrical and Computer Engineering, 2009, Virginia Tech

 This thesis presents a new ant-optimized biclustering technique known as SNAP biclustering, which runs faster and produces results of superior quality to previous techniques. Biclustering… (more)

Subjects/Keywords: Single Nucleotide Polymorphisms; Collaborative Filtering; Microarray Analysis; Ant Colony Optimization; Biclustering

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APA (6th Edition):

Chan, W. H. (2009). SNAP Biclustering. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/36442

Chicago Manual of Style (16th Edition):

Chan, William Hannibal. “SNAP Biclustering.” 2009. Masters Thesis, Virginia Tech. Accessed June 16, 2019. http://hdl.handle.net/10919/36442.

MLA Handbook (7th Edition):

Chan, William Hannibal. “SNAP Biclustering.” 2009. Web. 16 Jun 2019.

Vancouver:

Chan WH. SNAP Biclustering. [Internet] [Masters thesis]. Virginia Tech; 2009. [cited 2019 Jun 16]. Available from: http://hdl.handle.net/10919/36442.

Council of Science Editors:

Chan WH. SNAP Biclustering. [Masters Thesis]. Virginia Tech; 2009. Available from: http://hdl.handle.net/10919/36442


University of Alberta

17. Stepan, Torin KS. Incorporating Content and Context in Recommender Systems.

Degree: MS, Department of Electrical and Computer Engineering, 2015, University of Alberta

 Recommender systems are a growing area of research that find practical applications in a variety of domains. Integrated library systems and location-based social networks can… (more)

Subjects/Keywords: cold-start; hybrid; k-nearest neighbors; spatial; location based social networks; books; collaborative filtering; temporal; fuzzy; context; fuzzy taste vector; similarity; content; social; rural libraries; movies; recommendation; recommender; university digital libraries; classifier; locations

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APA (6th Edition):

Stepan, T. K. (2015). Incorporating Content and Context in Recommender Systems. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/qr46r3589

Chicago Manual of Style (16th Edition):

Stepan, Torin KS. “Incorporating Content and Context in Recommender Systems.” 2015. Masters Thesis, University of Alberta. Accessed June 16, 2019. https://era.library.ualberta.ca/files/qr46r3589.

MLA Handbook (7th Edition):

Stepan, Torin KS. “Incorporating Content and Context in Recommender Systems.” 2015. Web. 16 Jun 2019.

Vancouver:

Stepan TK. Incorporating Content and Context in Recommender Systems. [Internet] [Masters thesis]. University of Alberta; 2015. [cited 2019 Jun 16]. Available from: https://era.library.ualberta.ca/files/qr46r3589.

Council of Science Editors:

Stepan TK. Incorporating Content and Context in Recommender Systems. [Masters Thesis]. University of Alberta; 2015. Available from: https://era.library.ualberta.ca/files/qr46r3589

18. Zeng, Jingying. Latent Factor Models for Recommender Systems and Market Segmentation Through Clustering.

Degree: MS, Statistics, 2017, The Ohio State University

 Consider the problem of recommending products to a set of online users, where avery large selection of potential products are available. Recommender systems wereintroduced to… (more)

Subjects/Keywords: Statistics; Computer Science; Recommender Systems, Matrix Factorization models, Collaborative Filtering, Stochastic Gradient Descent

…and Sensitivity Analysis of the SGD Algorithm in Collaborative Filtering… …collaborative filtering, content-based filtering and hybrid recommender systems. The latter combines… …both collaborative filtering and content-based filtering together. Figure 1.1: An… …Meteren & Van Someren, 2000). The concept of collaborative filtering was introduced by… …order to recommend products. The collaborative filtering approach can be further divided into… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zeng, J. (2017). Latent Factor Models for Recommender Systems and Market Segmentation Through Clustering. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1491255524283942

Chicago Manual of Style (16th Edition):

Zeng, Jingying. “Latent Factor Models for Recommender Systems and Market Segmentation Through Clustering.” 2017. Masters Thesis, The Ohio State University. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1491255524283942.

MLA Handbook (7th Edition):

Zeng, Jingying. “Latent Factor Models for Recommender Systems and Market Segmentation Through Clustering.” 2017. Web. 16 Jun 2019.

Vancouver:

Zeng J. Latent Factor Models for Recommender Systems and Market Segmentation Through Clustering. [Internet] [Masters thesis]. The Ohio State University; 2017. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1491255524283942.

Council of Science Editors:

Zeng J. Latent Factor Models for Recommender Systems and Market Segmentation Through Clustering. [Masters Thesis]. The Ohio State University; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1491255524283942

19. Goyal, Vivek. A Recommendation System Based on Multiple Databases.

Degree: MS, Engineering and Applied Science: Computer Science, 2013, University of Cincinnati

 Recommendation Systems have long been serving the industry of e-commerce with recommendations pertaining to movies, books, travel packages et cetera. A user's activity or past… (more)

Subjects/Keywords: Computer Science; Collaborative Filtering; Similarity measures; Recommendation System; Neighborhood Model; Fuzzy Clustering; Data Mining

…14 1.2.1 1.2.2 Collaborative Filtering Recommendation Systems… …17 Chapter 2: Related Work 2.1 Neighborhood Models Based on Collaborative Filtering… …21 2.1.1 2.1.2 Item-based Collaborative Filtering… …23 2.1.3 2.2 User-based Collaborative Filtering… …24 Similarity Measures Used in Collaborative Filtering Technique… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Goyal, V. (2013). A Recommendation System Based on Multiple Databases. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1368027581

Chicago Manual of Style (16th Edition):

Goyal, Vivek. “A Recommendation System Based on Multiple Databases.” 2013. Masters Thesis, University of Cincinnati. Accessed June 16, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1368027581.

MLA Handbook (7th Edition):

Goyal, Vivek. “A Recommendation System Based on Multiple Databases.” 2013. Web. 16 Jun 2019.

Vancouver:

Goyal V. A Recommendation System Based on Multiple Databases. [Internet] [Masters thesis]. University of Cincinnati; 2013. [cited 2019 Jun 16]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1368027581.

Council of Science Editors:

Goyal V. A Recommendation System Based on Multiple Databases. [Masters Thesis]. University of Cincinnati; 2013. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1368027581

20. Padmashali, Sarika. An Open Source Discussion Group Recommendation System.

Degree: MS, Computer Science, 2017, San Jose State University

  A recommendation system analyzes user behavior on a website to make suggestions about what a user should do in the future on the website.… (more)

Subjects/Keywords: collaborative filtering; recommendation systems; Artificial Intelligence and Robotics; Databases and Information Systems; Software Engineering

…40 VII LIST OF FIGURES Figure 1 Collaborative Filtering Technique… …developers of some rule-based recommendation systems expressed the phrase “collaborative filtering… …Based Filtering, Collaborative Filtering. During the initial stage of the project we tried a… …lot of collaborative filtering techniques such as baseline predictors, latent matrix… …Chapter 2 gives a background of collaborative filtering techniques. Chapter 3 discusses… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Padmashali, S. (2017). An Open Source Discussion Group Recommendation System. (Masters Thesis). San Jose State University. Retrieved from https://scholarworks.sjsu.edu/etd_projects/537

Chicago Manual of Style (16th Edition):

Padmashali, Sarika. “An Open Source Discussion Group Recommendation System.” 2017. Masters Thesis, San Jose State University. Accessed June 16, 2019. https://scholarworks.sjsu.edu/etd_projects/537.

MLA Handbook (7th Edition):

Padmashali, Sarika. “An Open Source Discussion Group Recommendation System.” 2017. Web. 16 Jun 2019.

Vancouver:

Padmashali S. An Open Source Discussion Group Recommendation System. [Internet] [Masters thesis]. San Jose State University; 2017. [cited 2019 Jun 16]. Available from: https://scholarworks.sjsu.edu/etd_projects/537.

Council of Science Editors:

Padmashali S. An Open Source Discussion Group Recommendation System. [Masters Thesis]. San Jose State University; 2017. Available from: https://scholarworks.sjsu.edu/etd_projects/537

.