Review on Machine Learning Based Suggestion System
DOI:
https://doi.org/10.26438/ijcse/v6i4.242244Keywords:
Suggestion, Collaborative filtering, Model based, Memory based, Content based, HybridAbstract
Suggestion system plays vital role in WWW world and used in many applications. It has created the collection of many application, created global village and growth for numerous information. This paper represents the overview of Approaches and techniques generated in Suggestion system. Suggestion system is divided into three main types Collaborative Filtering, Content based and hybrid-based Method. The work in our categories collaborative filtering as Memory based type and Model based type. The paper discusses in detail the methods, their pros and cons. The paper proves to be a milestone in the research field of suggestion system.
References
Alexandrin Popescu and Lyle H. Ungar, David M. Pennock and Steve Lawrence,” Probabilistic Models for Unified
Collaborative and Content-Based Suggestion in Sparse-Data Environments”, POPESCUL ET Al,2001
Greg Linden, Brent Smith, and Jeremy York,”Amazon.com Suggestions Item-to-Item Collaborative Filtering”, IEEE Computer Society 2003.
Jun Wang, Arjen P. de Vries, Marcel J.T. Reinders,” Unifying Userbased and Itembased Collaborative Filtering Approaches by Similarity Fusion”, 2006 ACM.
Panagiotis Symeonidis *, Alexandros Nanopoulos, Apostolos N. Papadopoulos,Yannis Manolopoulos,” Collaborative recommender systems: Combining effectiveness and efficiency”, 2007 Elsevier Ltd.
Kazuyoshi Yoshii, Masataka Goto, Kazunori Komatani, Tetsuya Ogata, Hiroshi G. Okuno,” An Efficient Hybrid Music Recommender System Using an Incrementally TrainableProbabilistic Generative Model”,IEEE 2008
Akhmed Umyarov, Alexander Tuzhilin,”Improving Collaborative Filtering Suggestions Using External Data”, 2008 IEEE.
Zunping Cheng, Neil Hurley,” Effective Diverse and Obfuscated Attacks on Model-based Recommender Systems” 2009 ACM.
Hyeong-Joon Kwon, Tae-Hoon Lee, and Kwang-Seok Hong,” Improved Memory-based Collaborative Filtering Using Entropy-based Similarity Measures”, 2009 ACADEMY PUBLISHER.
Asela Gunawardana, Christopher Meek,” A Unified Approach to Building Hybrid Recommender Systems” 200X ACM
Sergio Cleger-Tamayo, Juan M. Fern´andez-Luna, and Juan F. Huete, ”A New Criteria for Selecting Neighborhood in Memory-Based Recommender Systems*”,Springer-Verlag Berlin Heidelberg 2011.
Pasquale Lops, Marco de Gemmis and Giovanni Semeraro,” Content-based Recommender Systems: State of the Art and Trends”, Springer 2011
[12] Yoav Bergner,Stefan Dr¨oschlery, Gerd Kortemeyer, Saif Rayyan, Daniel Seaton, and David E. Pritchard,” ModelBased
Collaborative Filtering Analysis of Student Response Data: MachineLearning Item Response Theory”,2012.
Svetlin Bostandjiev, John O‟Donovan, Tobias Höllerer,” TasteWeights:A Visual Interactive Hybrid Recommender System” 2012 ACM
Jiankai Sun, Shuaiqiang Wang, Byron J. Gao, Jun Ma,” Learning to Rank for Hybrid Suggestion”, 2012 ACM
Meenakshi Sharma and Sandeep Mann, “A Survey of Recommender Systems: Approaches and Limitations” ,ICAECE- 2013.
Royi Ronen, Noam Koenigstein, Elad Ziklik and Nir Nice,” Selecting Content-Based Features for Collaborative Filtering Recommenders”, ACM 2013
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
