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Degree: PhD

You searched for subject:(Collaborative filtering). Showing records 1 – 21 of 21 total matches.

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Virginia Tech

1. Amento, Brian. User Interfaces for Topic Management of Web Sites.

Degree: PhD, Computer Science, 2001, Virginia Tech

 Topic management is the task of gathering, evaluating, organizing, and sharing a set of web sites for a specific topic. Current web tools do not… (more)

Subjects/Keywords: social filtering; Cocitation analysis; information visualization; collaborative filtering; social network analysis; computer-supported cooperative work

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APA (6th Edition):

Amento, B. (2001). User Interfaces for Topic Management of Web Sites. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/29871

Chicago Manual of Style (16th Edition):

Amento, Brian. “User Interfaces for Topic Management of Web Sites.” 2001. Doctoral Dissertation, Virginia Tech. Accessed July 17, 2019. http://hdl.handle.net/10919/29871.

MLA Handbook (7th Edition):

Amento, Brian. “User Interfaces for Topic Management of Web Sites.” 2001. Web. 17 Jul 2019.

Vancouver:

Amento B. User Interfaces for Topic Management of Web Sites. [Internet] [Doctoral dissertation]. Virginia Tech; 2001. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/10919/29871.

Council of Science Editors:

Amento B. User Interfaces for Topic Management of Web Sites. [Doctoral Dissertation]. Virginia Tech; 2001. Available from: http://hdl.handle.net/10919/29871


University of Colorado

2. Gartrell, Charles Michael. Enhancing Recommender Systems Using Social Indicators.

Degree: PhD, Computer Science, 2014, University of Colorado

  Recommender systems are increasingly driving user experiences on the Internet. In recent years, online social networks have quickly become the fastest growing part of… (more)

Subjects/Keywords: collaborative filtering; group recommendation; machine learning; mobile computing; recommender systems; social networks; Computer Sciences

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APA (6th Edition):

Gartrell, C. M. (2014). Enhancing Recommender Systems Using Social Indicators. (Doctoral Dissertation). University of Colorado. Retrieved from http://scholar.colorado.edu/csci_gradetds/3

Chicago Manual of Style (16th Edition):

Gartrell, Charles Michael. “Enhancing Recommender Systems Using Social Indicators.” 2014. Doctoral Dissertation, University of Colorado. Accessed July 17, 2019. http://scholar.colorado.edu/csci_gradetds/3.

MLA Handbook (7th Edition):

Gartrell, Charles Michael. “Enhancing Recommender Systems Using Social Indicators.” 2014. Web. 17 Jul 2019.

Vancouver:

Gartrell CM. Enhancing Recommender Systems Using Social Indicators. [Internet] [Doctoral dissertation]. University of Colorado; 2014. [cited 2019 Jul 17]. Available from: http://scholar.colorado.edu/csci_gradetds/3.

Council of Science Editors:

Gartrell CM. Enhancing Recommender Systems Using Social Indicators. [Doctoral Dissertation]. University of Colorado; 2014. Available from: http://scholar.colorado.edu/csci_gradetds/3

3. Hassanzadeh, Farzad. Distances on rankings: from social choice to flash memories.

Degree: PhD, 1200, 2013, University of Illinois – Urbana-Champaign

 From social choice to statistics to coding theory, rankings are found to be a useful vehicle for storing and presenting information in modern data systems.… (more)

Subjects/Keywords: Distance; Rankings; Permutations; Social choice; Flash memories; Kendall tau distance; Weighted Kendall distance; Weighted Transposition distance; Rank aggregation; Information Retrieval; Collaborative filtering; Rank modulation; Ulam distance; error-correcting codes

collaborative filtering, if user preferences are presented as rankings, distance measures between… …addition to rank aggregation, weighted distances are useful for collaborative filtering [18… …agents [17] as well as in recommender systems in the context of collaborative… …filtering [18]. In coding theory, transmitting rankings instead of absolute values was… 

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APA (6th Edition):

Hassanzadeh, F. (2013). Distances on rankings: from social choice to flash memories. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/44268

Chicago Manual of Style (16th Edition):

Hassanzadeh, Farzad. “Distances on rankings: from social choice to flash memories.” 2013. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed July 17, 2019. http://hdl.handle.net/2142/44268.

MLA Handbook (7th Edition):

Hassanzadeh, Farzad. “Distances on rankings: from social choice to flash memories.” 2013. Web. 17 Jul 2019.

Vancouver:

Hassanzadeh F. Distances on rankings: from social choice to flash memories. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2013. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/2142/44268.

Council of Science Editors:

Hassanzadeh F. Distances on rankings: from social choice to flash memories. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2013. Available from: http://hdl.handle.net/2142/44268

4. Hou, Hailong. Computing with Granular Words.

Degree: PhD, Computer Science, 2011, Georgia State University

  Computational linguistics is a sub-field of artificial intelligence; it is an interdisciplinary field dealing with statistical and/or rule-based modeling of natural language from a… (more)

Subjects/Keywords: Computing with word; Granular word; Granular information hyper tree; Spam filtering; Recommendation system; Collaborative intelligence.

…computing with granular word and CWGW based collaborative filtering algorithm are proposed in this… …35 CHAPTER 5 GIHT BASED BAYESIAN ALGORITHM FOR SPAM FILTERING… …72 ix LIST OF FIGURES Figure 1.1 Compare spam filtering algorithms… …52 Figure 6.2 Collaborative Intelligence System… …recent years, it has been applied to multiple areas such as E-mail filtering, semantic web… 

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APA (6th Edition):

Hou, H. (2011). Computing with Granular Words. (Doctoral Dissertation). Georgia State University. Retrieved from https://scholarworks.gsu.edu/cs_theses/73

Chicago Manual of Style (16th Edition):

Hou, Hailong. “Computing with Granular Words.” 2011. Doctoral Dissertation, Georgia State University. Accessed July 17, 2019. https://scholarworks.gsu.edu/cs_theses/73.

MLA Handbook (7th Edition):

Hou, Hailong. “Computing with Granular Words.” 2011. Web. 17 Jul 2019.

Vancouver:

Hou H. Computing with Granular Words. [Internet] [Doctoral dissertation]. Georgia State University; 2011. [cited 2019 Jul 17]. Available from: https://scholarworks.gsu.edu/cs_theses/73.

Council of Science Editors:

Hou H. Computing with Granular Words. [Doctoral Dissertation]. Georgia State University; 2011. Available from: https://scholarworks.gsu.edu/cs_theses/73


University of Miami

5. Huang, Zifang. Knowledge-Assisted Sequential Pattern Analysis: An Application in Labor Contraction Prediction.

Degree: PhD, Electrical and Computer Engineering (Engineering), 2012, University of Miami

 Although neuraxial techniques, such as spinal and epidural, are still considered as the gold standard for labor analgesia, there are some parturients who cannot receive… (more)

Subjects/Keywords: labor contraction prediction; time series prediction; sequential association rule mining; least squares support vector machine; collaborative filtering; heuristic parameter tuning

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APA (6th Edition):

Huang, Z. (2012). Knowledge-Assisted Sequential Pattern Analysis: An Application in Labor Contraction Prediction. (Doctoral Dissertation). University of Miami. Retrieved from https://scholarlyrepository.miami.edu/oa_dissertations/757

Chicago Manual of Style (16th Edition):

Huang, Zifang. “Knowledge-Assisted Sequential Pattern Analysis: An Application in Labor Contraction Prediction.” 2012. Doctoral Dissertation, University of Miami. Accessed July 17, 2019. https://scholarlyrepository.miami.edu/oa_dissertations/757.

MLA Handbook (7th Edition):

Huang, Zifang. “Knowledge-Assisted Sequential Pattern Analysis: An Application in Labor Contraction Prediction.” 2012. Web. 17 Jul 2019.

Vancouver:

Huang Z. Knowledge-Assisted Sequential Pattern Analysis: An Application in Labor Contraction Prediction. [Internet] [Doctoral dissertation]. University of Miami; 2012. [cited 2019 Jul 17]. Available from: https://scholarlyrepository.miami.edu/oa_dissertations/757.

Council of Science Editors:

Huang Z. Knowledge-Assisted Sequential Pattern Analysis: An Application in Labor Contraction Prediction. [Doctoral Dissertation]. University of Miami; 2012. Available from: https://scholarlyrepository.miami.edu/oa_dissertations/757


Oregon State University

6. Jung, Seikyung. Designing and understanding information retrieval systems using collaborative filtering in an academic library environment.

Degree: PhD, Computer Science, 2007, Oregon State University

 Accessing information on the Web has become ingrained into our daily lives, and we seek information from many different sources, including conference and journal publications,… (more)

Subjects/Keywords: Collaborative Filtering; Electronic information resource searching

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APA (6th Edition):

Jung, S. (2007). Designing and understanding information retrieval systems using collaborative filtering in an academic library environment. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/5694

Chicago Manual of Style (16th Edition):

Jung, Seikyung. “Designing and understanding information retrieval systems using collaborative filtering in an academic library environment.” 2007. Doctoral Dissertation, Oregon State University. Accessed July 17, 2019. http://hdl.handle.net/1957/5694.

MLA Handbook (7th Edition):

Jung, Seikyung. “Designing and understanding information retrieval systems using collaborative filtering in an academic library environment.” 2007. Web. 17 Jul 2019.

Vancouver:

Jung S. Designing and understanding information retrieval systems using collaborative filtering in an academic library environment. [Internet] [Doctoral dissertation]. Oregon State University; 2007. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/1957/5694.

Council of Science Editors:

Jung S. Designing and understanding information retrieval systems using collaborative filtering in an academic library environment. [Doctoral Dissertation]. Oregon State University; 2007. Available from: http://hdl.handle.net/1957/5694


University of Minnesota

7. Kluver, Daniel. Improvements in Holistic Recommender System Research.

Degree: PhD, Computer Science, 2018, University of Minnesota

 Since the mid 1990s, recommender systems have grown to be a major area of deployment in industry, and research in academia. A through-line in this… (more)

Subjects/Keywords: Collaborative Filtering; Libraries; New User; Recommender Systems

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APA (6th Edition):

Kluver, D. (2018). Improvements in Holistic Recommender System Research. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/201165

Chicago Manual of Style (16th Edition):

Kluver, Daniel. “Improvements in Holistic Recommender System Research.” 2018. Doctoral Dissertation, University of Minnesota. Accessed July 17, 2019. http://hdl.handle.net/11299/201165.

MLA Handbook (7th Edition):

Kluver, Daniel. “Improvements in Holistic Recommender System Research.” 2018. Web. 17 Jul 2019.

Vancouver:

Kluver D. Improvements in Holistic Recommender System Research. [Internet] [Doctoral dissertation]. University of Minnesota; 2018. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/11299/201165.

Council of Science Editors:

Kluver D. Improvements in Holistic Recommender System Research. [Doctoral Dissertation]. University of Minnesota; 2018. Available from: http://hdl.handle.net/11299/201165


Georgia Tech

8. Lee, Joonseok. Local approaches for collaborative filtering.

Degree: PhD, Computer Science, 2015, Georgia Tech

 Recommendation systems are emerging as an important business application as the demand for personalized services in E-commerce increases. Collaborative filtering techniques are widely used for… (more)

Subjects/Keywords: Recommendation systems; Collaborative filtering; Machine learning; Local low-rank assumption; Matrix factorization; Matrix approximation; Ensemble collaborative ranking

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APA (6th Edition):

Lee, J. (2015). Local approaches for collaborative filtering. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53846

Chicago Manual of Style (16th Edition):

Lee, Joonseok. “Local approaches for collaborative filtering.” 2015. Doctoral Dissertation, Georgia Tech. Accessed July 17, 2019. http://hdl.handle.net/1853/53846.

MLA Handbook (7th Edition):

Lee, Joonseok. “Local approaches for collaborative filtering.” 2015. Web. 17 Jul 2019.

Vancouver:

Lee J. Local approaches for collaborative filtering. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/1853/53846.

Council of Science Editors:

Lee J. Local approaches for collaborative filtering. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53846


Georgia Tech

9. Parameswaran, Rupa. A Robust Data Obfuscation Technique for Privacy Preserving Collaborative Filtering.

Degree: PhD, Electrical and Computer Engineering, 2006, Georgia Tech

 Privacy is defined as the freedom from unauthorized intrusion. The availability of personal information through online databases, such as government records, medical records, and voters… (more)

Subjects/Keywords: Data privacy; Data obfuscation; Data mining; Collaborative filtering; Data mining; Information storage and retrieval systems; Cluster analysis Computer programs; Database security

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APA (6th Edition):

Parameswaran, R. (2006). A Robust Data Obfuscation Technique for Privacy Preserving Collaborative Filtering. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/11459

Chicago Manual of Style (16th Edition):

Parameswaran, Rupa. “A Robust Data Obfuscation Technique for Privacy Preserving Collaborative Filtering.” 2006. Doctoral Dissertation, Georgia Tech. Accessed July 17, 2019. http://hdl.handle.net/1853/11459.

MLA Handbook (7th Edition):

Parameswaran, Rupa. “A Robust Data Obfuscation Technique for Privacy Preserving Collaborative Filtering.” 2006. Web. 17 Jul 2019.

Vancouver:

Parameswaran R. A Robust Data Obfuscation Technique for Privacy Preserving Collaborative Filtering. [Internet] [Doctoral dissertation]. Georgia Tech; 2006. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/1853/11459.

Council of Science Editors:

Parameswaran R. A Robust Data Obfuscation Technique for Privacy Preserving Collaborative Filtering. [Doctoral Dissertation]. Georgia Tech; 2006. Available from: http://hdl.handle.net/1853/11459

10. Parimi, Rohit. Collaborative filtering approaches for single-domain and cross-domain recommender systems.

Degree: PhD, Computing and Information Sciences, 2015, Kansas State University

 Increasing amounts of content on the Web means that users can select from a wide variety of items (i.e., items that concur with their tastes… (more)

Subjects/Keywords: Recommender systems; Collaborative filtering; Implicit feedback; Cross-domain; Adsorption; Matrix factorization; Computer Science (0984)

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APA (6th Edition):

Parimi, R. (2015). Collaborative filtering approaches for single-domain and cross-domain recommender systems. (Doctoral Dissertation). Kansas State University. Retrieved from http://hdl.handle.net/2097/20108

Chicago Manual of Style (16th Edition):

Parimi, Rohit. “Collaborative filtering approaches for single-domain and cross-domain recommender systems.” 2015. Doctoral Dissertation, Kansas State University. Accessed July 17, 2019. http://hdl.handle.net/2097/20108.

MLA Handbook (7th Edition):

Parimi, Rohit. “Collaborative filtering approaches for single-domain and cross-domain recommender systems.” 2015. Web. 17 Jul 2019.

Vancouver:

Parimi R. Collaborative filtering approaches for single-domain and cross-domain recommender systems. [Internet] [Doctoral dissertation]. Kansas State University; 2015. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/2097/20108.

Council of Science Editors:

Parimi R. Collaborative filtering approaches for single-domain and cross-domain recommender systems. [Doctoral Dissertation]. Kansas State University; 2015. Available from: http://hdl.handle.net/2097/20108


Brunel University

11. Ryding, Michael Philip. The collaborative index.

Degree: PhD, 2006, Brunel University

 Information-seekers use a variety of information stores including electronic systems and the physical world experience of their community. Within electronic systems, information-seekers often report feelings… (more)

Subjects/Keywords: 020; Information stores; Information overload; Collaborative filtering; Recommender systems; Social navigation

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APA (6th Edition):

Ryding, M. P. (2006). The collaborative index. (Doctoral Dissertation). Brunel University. Retrieved from http://bura.brunel.ac.uk/handle/2438/5481 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487018

Chicago Manual of Style (16th Edition):

Ryding, Michael Philip. “The collaborative index.” 2006. Doctoral Dissertation, Brunel University. Accessed July 17, 2019. http://bura.brunel.ac.uk/handle/2438/5481 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487018.

MLA Handbook (7th Edition):

Ryding, Michael Philip. “The collaborative index.” 2006. Web. 17 Jul 2019.

Vancouver:

Ryding MP. The collaborative index. [Internet] [Doctoral dissertation]. Brunel University; 2006. [cited 2019 Jul 17]. Available from: http://bura.brunel.ac.uk/handle/2438/5481 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487018.

Council of Science Editors:

Ryding MP. The collaborative index. [Doctoral Dissertation]. Brunel University; 2006. Available from: http://bura.brunel.ac.uk/handle/2438/5481 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.487018


University of Minnesota

12. Sharma, Mohit. Preference modeling and Accuracy in Recommender Systems.

Degree: PhD, Computer Science, 2017, University of Minnesota

 Recommender systems are widely used to recommend the most appealing items to users. In this thesis, we focus on analyzing the accuracy of the state-of-the-art… (more)

Subjects/Keywords: Cold-Start item recommendations; Collaborative filtering; Group of items; Matrix completion; Matrix factorization; Recommender systems

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APA (6th Edition):

Sharma, M. (2017). Preference modeling and Accuracy in Recommender Systems. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/192686

Chicago Manual of Style (16th Edition):

Sharma, Mohit. “Preference modeling and Accuracy in Recommender Systems.” 2017. Doctoral Dissertation, University of Minnesota. Accessed July 17, 2019. http://hdl.handle.net/11299/192686.

MLA Handbook (7th Edition):

Sharma, Mohit. “Preference modeling and Accuracy in Recommender Systems.” 2017. Web. 17 Jul 2019.

Vancouver:

Sharma M. Preference modeling and Accuracy in Recommender Systems. [Internet] [Doctoral dissertation]. University of Minnesota; 2017. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/11299/192686.

Council of Science Editors:

Sharma M. Preference modeling and Accuracy in Recommender Systems. [Doctoral Dissertation]. University of Minnesota; 2017. Available from: http://hdl.handle.net/11299/192686


Northeastern University

13. Shokat Fadaee, Saber. Classification and prediction of matrix structured data with applications to recommendation systems, identifying anti-socials and bot-nets.

Degree: PhD, Computer Science Program, 2017, Northeastern University

 Matrix representations are a natural way to represent many forms of networked and tabulated data. These include connections among people, user preferences over items, or… (more)

Subjects/Keywords: artificial Intelligence; collaborative filtering; deep learning; recommendation systems; social networks; statistical network modeling

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APA (6th Edition):

Shokat Fadaee, S. (2017). Classification and prediction of matrix structured data with applications to recommendation systems, identifying anti-socials and bot-nets. (Doctoral Dissertation). Northeastern University. Retrieved from http://hdl.handle.net/2047/D20284626

Chicago Manual of Style (16th Edition):

Shokat Fadaee, Saber. “Classification and prediction of matrix structured data with applications to recommendation systems, identifying anti-socials and bot-nets.” 2017. Doctoral Dissertation, Northeastern University. Accessed July 17, 2019. http://hdl.handle.net/2047/D20284626.

MLA Handbook (7th Edition):

Shokat Fadaee, Saber. “Classification and prediction of matrix structured data with applications to recommendation systems, identifying anti-socials and bot-nets.” 2017. Web. 17 Jul 2019.

Vancouver:

Shokat Fadaee S. Classification and prediction of matrix structured data with applications to recommendation systems, identifying anti-socials and bot-nets. [Internet] [Doctoral dissertation]. Northeastern University; 2017. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/2047/D20284626.

Council of Science Editors:

Shokat Fadaee S. Classification and prediction of matrix structured data with applications to recommendation systems, identifying anti-socials and bot-nets. [Doctoral Dissertation]. Northeastern University; 2017. Available from: http://hdl.handle.net/2047/D20284626


University of Cincinnati

14. Strunjas, Svetlana. Algorithms and Models for Collaborative Filtering from Large Information Corpora.

Degree: PhD, Engineering : Computer Science, 2008, University of Cincinnati

  In this thesis we propose novel collaborative filtering approaches for large data sets. We also demonstrate how these collaborative approaches can be used for… (more)

Subjects/Keywords: Computer Science; collaborative filtering; collaborative partitioning; clustering; information retrieval

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APA (6th Edition):

Strunjas, S. (2008). Algorithms and Models for Collaborative Filtering from Large Information Corpora. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1220001182

Chicago Manual of Style (16th Edition):

Strunjas, Svetlana. “Algorithms and Models for Collaborative Filtering from Large Information Corpora.” 2008. Doctoral Dissertation, University of Cincinnati. Accessed July 17, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1220001182.

MLA Handbook (7th Edition):

Strunjas, Svetlana. “Algorithms and Models for Collaborative Filtering from Large Information Corpora.” 2008. Web. 17 Jul 2019.

Vancouver:

Strunjas S. Algorithms and Models for Collaborative Filtering from Large Information Corpora. [Internet] [Doctoral dissertation]. University of Cincinnati; 2008. [cited 2019 Jul 17]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1220001182.

Council of Science Editors:

Strunjas S. Algorithms and Models for Collaborative Filtering from Large Information Corpora. [Doctoral Dissertation]. University of Cincinnati; 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1220001182


Wayne State University

15. Timalsina, Arun K. A framework for personalized dynamic cross-selling in e-commerce retailing.

Degree: PhD, Industrial and Manufacturing Engineering, 2012, Wayne State University

  Cross-selling and product bundling are prevalent strategies in the retail sector. Instead of static bundling offers, i.e. giving the same offer to everyone, personalized… (more)

Subjects/Keywords: cross-selling, data mining, dynamic pricing, factorization, one class collaborative filtering, simulation; Computer Sciences; Industrial Engineering; Library and Information Science

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APA (6th Edition):

Timalsina, A. K. (2012). A framework for personalized dynamic cross-selling in e-commerce retailing. (Doctoral Dissertation). Wayne State University. Retrieved from https://digitalcommons.wayne.edu/oa_dissertations/479

Chicago Manual of Style (16th Edition):

Timalsina, Arun K. “A framework for personalized dynamic cross-selling in e-commerce retailing.” 2012. Doctoral Dissertation, Wayne State University. Accessed July 17, 2019. https://digitalcommons.wayne.edu/oa_dissertations/479.

MLA Handbook (7th Edition):

Timalsina, Arun K. “A framework for personalized dynamic cross-selling in e-commerce retailing.” 2012. Web. 17 Jul 2019.

Vancouver:

Timalsina AK. A framework for personalized dynamic cross-selling in e-commerce retailing. [Internet] [Doctoral dissertation]. Wayne State University; 2012. [cited 2019 Jul 17]. Available from: https://digitalcommons.wayne.edu/oa_dissertations/479.

Council of Science Editors:

Timalsina AK. A framework for personalized dynamic cross-selling in e-commerce retailing. [Doctoral Dissertation]. Wayne State University; 2012. Available from: https://digitalcommons.wayne.edu/oa_dissertations/479

16. Yang, Shuang-Hong. Predictive models for online human activities.

Degree: PhD, Computing, 2012, Georgia Tech

 The availability and scale of user generated data in online systems raises tremendous challenges and opportunities to analytic study of human activities. Effective modeling of… (more)

Subjects/Keywords: Social contagion; Collaborative competitive filtering; Social ties; Behavior prediction; User-generated data; Redictive models; Online human activities; Language gap; User cognitive aspects; Content mining; Behavior-relation interplay; User-generated content; User interfaces (Computer systems); Data mining

…thesis is on behavior prediction. We present collaborative competitive filtering (CCF)… …introduction of collaborative filtering, the most popular techniques for personalization and… …These two will be used as foundations to the collaborative competitive filtering framework we… …mining user cognitive aspects, as will be presented in Chapter 7. 2.1 Collaborative filtering… …Collaborative filtering (CF) is widely used for establishing personalized systems such as… 

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APA (6th Edition):

Yang, S. (2012). Predictive models for online human activities. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/43689

Chicago Manual of Style (16th Edition):

Yang, Shuang-Hong. “Predictive models for online human activities.” 2012. Doctoral Dissertation, Georgia Tech. Accessed July 17, 2019. http://hdl.handle.net/1853/43689.

MLA Handbook (7th Edition):

Yang, Shuang-Hong. “Predictive models for online human activities.” 2012. Web. 17 Jul 2019.

Vancouver:

Yang S. Predictive models for online human activities. [Internet] [Doctoral dissertation]. Georgia Tech; 2012. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/1853/43689.

Council of Science Editors:

Yang S. Predictive models for online human activities. [Doctoral Dissertation]. Georgia Tech; 2012. Available from: http://hdl.handle.net/1853/43689


Georgia Tech

17. Yu, Hong. A data-driven approach for personalized drama management.

Degree: PhD, Interactive Computing, 2015, Georgia Tech

 An interactive narrative is a form of digital entertainment in which players can create or influence a dramatic storyline through actions, typically by assuming the… (more)

Subjects/Keywords: Personalized drama manager; Interactive narrative; Player modeling; Prefix based collaborative filtering

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APA (6th Edition):

Yu, H. (2015). A data-driven approach for personalized drama management. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/53851

Chicago Manual of Style (16th Edition):

Yu, Hong. “A data-driven approach for personalized drama management.” 2015. Doctoral Dissertation, Georgia Tech. Accessed July 17, 2019. http://hdl.handle.net/1853/53851.

MLA Handbook (7th Edition):

Yu, Hong. “A data-driven approach for personalized drama management.” 2015. Web. 17 Jul 2019.

Vancouver:

Yu H. A data-driven approach for personalized drama management. [Internet] [Doctoral dissertation]. Georgia Tech; 2015. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/1853/53851.

Council of Science Editors:

Yu H. A data-driven approach for personalized drama management. [Doctoral Dissertation]. Georgia Tech; 2015. Available from: http://hdl.handle.net/1853/53851


Florida International University

18. Zeng, Kaiman. Next Generation of Product Search and Discovery.

Degree: PhD, Electrical Engineering, 2015, Florida International University

  Online shopping has become an important part of people’s daily life with the rapid development of e-commerce. In some domains such as books, electronics,… (more)

Subjects/Keywords: visual search; content based image retrieval; ranking; hypergraph learning; recommendation; collaborative filtering; clustering; Other Electrical and Computer Engineering; Signal Processing

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APA (6th Edition):

Zeng, K. (2015). Next Generation of Product Search and Discovery. (Doctoral Dissertation). Florida International University. Retrieved from http://digitalcommons.fiu.edu/etd/2312 ; 10.25148/etd.FIDC000207 ; FIDC000207

Chicago Manual of Style (16th Edition):

Zeng, Kaiman. “Next Generation of Product Search and Discovery.” 2015. Doctoral Dissertation, Florida International University. Accessed July 17, 2019. http://digitalcommons.fiu.edu/etd/2312 ; 10.25148/etd.FIDC000207 ; FIDC000207.

MLA Handbook (7th Edition):

Zeng, Kaiman. “Next Generation of Product Search and Discovery.” 2015. Web. 17 Jul 2019.

Vancouver:

Zeng K. Next Generation of Product Search and Discovery. [Internet] [Doctoral dissertation]. Florida International University; 2015. [cited 2019 Jul 17]. Available from: http://digitalcommons.fiu.edu/etd/2312 ; 10.25148/etd.FIDC000207 ; FIDC000207.

Council of Science Editors:

Zeng K. Next Generation of Product Search and Discovery. [Doctoral Dissertation]. Florida International University; 2015. Available from: http://digitalcommons.fiu.edu/etd/2312 ; 10.25148/etd.FIDC000207 ; FIDC000207

19. Zhang, Zhuo. Sparsity, robustness, and diversification of Recommender Systems .

Degree: PhD, 2014, Princeton University

 Recommender systems have played an important role in helping individuals select useful items or places of interest when they face too many choices. Collaborative filtering(more)

Subjects/Keywords: Collaborative Filtering; Diversification; Recommender System; Robustness; Sparisity

…By analyzing the available ratings, collaborative filtering attempts to make the best… …rating matrix 1 is very sparse. The traditional collaborative filtering algorithms will… …available information is one of the trends in the future. 1.1 Iterative Collaborative Filtering… …in Sparse Recommender Systems Collaborative filtering (CF) is one of the most… …methods. 1.2 Shilling Attack Detection using Graph-based Algorithms Collaborative filtering… 

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

APA (6th Edition):

Zhang, Z. (2014). Sparsity, robustness, and diversification of Recommender Systems . (Doctoral Dissertation). Princeton University. Retrieved from http://arks.princeton.edu/ark:/88435/dsp01g732dc22c

Chicago Manual of Style (16th Edition):

Zhang, Zhuo. “Sparsity, robustness, and diversification of Recommender Systems .” 2014. Doctoral Dissertation, Princeton University. Accessed July 17, 2019. http://arks.princeton.edu/ark:/88435/dsp01g732dc22c.

MLA Handbook (7th Edition):

Zhang, Zhuo. “Sparsity, robustness, and diversification of Recommender Systems .” 2014. Web. 17 Jul 2019.

Vancouver:

Zhang Z. Sparsity, robustness, and diversification of Recommender Systems . [Internet] [Doctoral dissertation]. Princeton University; 2014. [cited 2019 Jul 17]. Available from: http://arks.princeton.edu/ark:/88435/dsp01g732dc22c.

Council of Science Editors:

Zhang Z. Sparsity, robustness, and diversification of Recommender Systems . [Doctoral Dissertation]. Princeton University; 2014. Available from: http://arks.princeton.edu/ark:/88435/dsp01g732dc22c


Georgia Tech

20. Zhou, Ke. Extending low-rank matrix factorizations for emerging applications.

Degree: PhD, Computational Science and Engineering, 2013, Georgia Tech

 Low-rank matrix factorizations have become increasingly popular to project high dimensional data into latent spaces with small dimensions in order to obtain better understandings of… (more)

Subjects/Keywords: Matrix factorization; Collaborative filtering; Social network; Dimensional analysis Computer programs; Cluster analysis Data processing; Social networks

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

APA (6th Edition):

Zhou, K. (2013). Extending low-rank matrix factorizations for emerging applications. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/50230

Chicago Manual of Style (16th Edition):

Zhou, Ke. “Extending low-rank matrix factorizations for emerging applications.” 2013. Doctoral Dissertation, Georgia Tech. Accessed July 17, 2019. http://hdl.handle.net/1853/50230.

MLA Handbook (7th Edition):

Zhou, Ke. “Extending low-rank matrix factorizations for emerging applications.” 2013. Web. 17 Jul 2019.

Vancouver:

Zhou K. Extending low-rank matrix factorizations for emerging applications. [Internet] [Doctoral dissertation]. Georgia Tech; 2013. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/1853/50230.

Council of Science Editors:

Zhou K. Extending low-rank matrix factorizations for emerging applications. [Doctoral Dissertation]. Georgia Tech; 2013. Available from: http://hdl.handle.net/1853/50230


Georgia Tech

21. Zou, Jun. Social computing for personalization and credible information mining using probabilistic graphical models.

Degree: PhD, Electrical and Computer Engineering, 2016, Georgia Tech

 In this dissertation, we address challenging social computing problems in personalized recommender systems and social media information mining. We tap into probabilistic graphical models, including… (more)

Subjects/Keywords: Social computing; Recommender systems; Collaborative filtering; Belief propagation; Probabilistic graphical models

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

APA (6th Edition):

Zou, J. (2016). Social computing for personalization and credible information mining using probabilistic graphical models. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/55646

Chicago Manual of Style (16th Edition):

Zou, Jun. “Social computing for personalization and credible information mining using probabilistic graphical models.” 2016. Doctoral Dissertation, Georgia Tech. Accessed July 17, 2019. http://hdl.handle.net/1853/55646.

MLA Handbook (7th Edition):

Zou, Jun. “Social computing for personalization and credible information mining using probabilistic graphical models.” 2016. Web. 17 Jul 2019.

Vancouver:

Zou J. Social computing for personalization and credible information mining using probabilistic graphical models. [Internet] [Doctoral dissertation]. Georgia Tech; 2016. [cited 2019 Jul 17]. Available from: http://hdl.handle.net/1853/55646.

Council of Science Editors:

Zou J. Social computing for personalization and credible information mining using probabilistic graphical models. [Doctoral Dissertation]. Georgia Tech; 2016. Available from: http://hdl.handle.net/1853/55646

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