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Discovering The Impact Of Knowledge In Recommender Systems: A Comparative Study

Bahram Amini, Roliana Ibrahim, Mohd Shahizan Othman
2011 International Journal of Computer Science & Engineering Survey  
However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache.  ...  In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources.  ...  cold-start or data sparsity problem.  ... 
doi:10.5121/ijcses.2011.2301 fatcat:l3wymqfgqbhovhiy72osnb6hja

Securing Tag-based recommender systems against profile injection attacks: A comparative study. (Extended Report) [article]

Georgios K. Pitsilis, Heri Ramampiaro, Helge Langseth
2019 arXiv   pre-print
In particular, we study various countermeasures against two types of such attacks for social tagging systems, the Overload attack and the Piggyback attack.  ...  The countermeasure schemes studied here include baseline classifiers such as, Naive Bayes filter and Support Vector Machine, as well as a Deep Learning approach.  ...  Acknowledgements This work has been financially supported by Telenor Research, Norway, through the collaboration project between NTNU and Telenor, and it has been carried out at the Norwegian Open AI-Lab  ... 
arXiv:1901.08422v1 fatcat:i5tltq6e65hmtfmwdcfx6gaptu

Sandplay: A comparative study

Susan Cockle
1993 International Journal of Play Therapy  
They tend to lack resourcefulness in dealing with adversity and have a lack of hope for the future.  ...  They show resourcefulness in dealing with adversity and have hope for the future. The DCG tend to perceive their world as barren, a struggle, and consisting of threat and danger.  ...  Likewise, 14% of sand worlds in the CG were active as compared with 92% in the DCG.  ... 
doi:10.1037/h0089349 fatcat:rgbqxfe4d5bdpoinvy6fdqc2fe

Deep Hybrid Recommender System

Didem Turker, Alper Ozcan, Sule Gunduz Oguducu
2020 2020 28th Signal Processing and Communications Applications Conference (SIU)  
In this thesis we focus on improving the accuracy of a recommendation system developed for fashion industry where the data is sparse and the system has a prevalent cold start problem.  ...  This problem called the "Cold Start Problem" in literature.  ... 
doi:10.1109/siu49456.2020.9302193 fatcat:snrqnvqf25d6tn2yyibaduxdv4

UV bright red-sequence galaxies: a comparative study between UV upturn and UV weak systems [article]

M. L. L. Dantas
2020 arXiv   pre-print
stratification of the first study in terms of emission lines; (iii) the comparison between UV weak and upturn for passive RSGs in terms of their stellar populations.  ...  The final front is focused on the study of stellar population properties of UV weak and upturn systems, by making use of catalogues from GAMA-DR3.  ...  It gathered observations for a little over a decade, starting in the year 2003 and completely decommissioned in 2013, after facing years of technical issues.  ... 
arXiv:2011.06476v1 fatcat:lllfsicosvhqpmf6fneakxlysy


Muhammad Mansoor Mirza, Institute of Business & Technology, Karachi.
2013 IBT journal of business studies  
Purpose - The study has been conducted to examine the process of reconciliation and conflict resolution between neighboring states with deep-seated antagonism and to learn why in some cases enmity transformed  ...  Design/Methodology/Sample - In this study the authors have conducted a comparative analyze the reconciliation and conflict resolution between hereditary enemy states like France-Germany and India-Pakistan  ...  Comparative Study  ... 
doi:10.46745/ilma.jbs.2013.09.02.05 fatcat:wskwtxl7sfegvbdyvzwc4ahzu4

An Application-oriented Review of Deep Learning in Recommender Systems

Jyoti Shokeen, Chhavi Rana
2019 International Journal of Intelligent Systems and Applications  
This paper gives a brief overview of various deep learning techniques and their implementation in recommender systems for various applications.  ...  The increasing research in recommender systems using deep learning proves the success of deep learning techniques over traditional methods of recommender systems.  ...  It shows promising results in dealing with the cold-start problem. In this paper, we have taken a dataset of 45 papers specifically concerned with deep learning in RSs.  ... 
doi:10.5815/ijisa.2019.05.06 fatcat:67fgexfbfjh2no5b3phvohbole

Collaborative Filtering and Artificial Neural Network Based Recommendation System for Advanced Applications

Bharadwaja Krishnadev Mylavarapu
2018 Journal of Computer and Communications  
They are complete cold start (CCS) problem and incomplete cold start (ICS) problem.  ...  To make recommendation on items from the user for historical user rating several intelligent systems are using. The most common method is Recommendation systems.  ...  Conflicts of Interest The authors declare no conflicts of interest regarding the publication of this paper. References [1] Wei, J, He., J.H., Chen, K., Zhou, Y. and Tang  ... 
doi:10.4236/jcc.2018.612001 fatcat:bvcdfob5vjg2zgoavhrospblhe

Collaborative Deep Forest Learning for Recommender Systems

Soheila Molaei, Amirhossein Havvaei, Hadi Zare, Mahdi Jalili
2021 IEEE Access  
Then, a novel deep learning approach based on cascade tree forest is used in the recommendation process.  ...  Here, we propose an end-to-end deep learning framework by learning latent social features to embed in a CF approach.  ...  LITERATURE REVIEW Data sparsity is a critical problem in recommender systems for collaborative filtering (CF) techniques, particularly for new users and items.  ... 
doi:10.1109/access.2021.3054818 fatcat:gvyjzdcgbna37l2mb5pzyy65yu

A Comparative Study of Staff Perspectives on Design Based Learning in Engineering Education

Sivachandran Chandrasekaran, Guy Littlefair, Matthew Joordens, Alex Stojcevski
2014 Journal of Modern Education Review  
This paper is part of a research study which aims to develop a framework for a newly proposed learning and teaching approach, project oriented design based learning (PODBL).  ...  Nonetheless, there is still a gap exits between students learning outcomes and the teaching practice. It is a vital task for pedagogy to compete and fulfil current design expectations in engineering.  ...  Staff also described DBL as using a methodology of design as a foundation for learning activity that guides students through a design problem.  ... 
doi:10.15341/jmer(2155-7993)/03.04.2014/001 fatcat:hgddiekm7ng6xppqqhvepfzwrm

From College Readiness to Ready for Revolution! Third World Student Activism at a Northern California Community College, 1965–1969

Jason Ferreira
2014 Kalfou A Journal of Comparative and Relational Ethnic Studies  
We started learning the truth about the system, and about our people.  ...  And in relation to the student demand for a Third World Studies Division, he "referred" the matter to the Committee on Instruction "for further study and recommendation."  ... 
doi:10.15367/kf.v1i1.12 fatcat:kid6evit5rdjfe2wtmtvpcqqdi

Deep-Sequence–Aware Candidate Generation for e-Learning System

Aziz Ilyosov, Alpamis Kutlimuratov, Taeg-Keun Whangbo
2021 Processes  
Therefore, in this study, we developed a novel recommendation system that takes advantage of sequence and heterogeneous information in the candidate-generation process.  ...  The proposed deep-learning model may improve the prediction accuracy using user data, item data, and sequential information of the user's profile.  ...  over the period in which this manuscript was written.  ... 
doi:10.3390/pr9081454 fatcat:jm6mlgizc5abfdzrh3utjigszi

Comparative study on traditional recommender systems and deep learning based recommender systems

N.L. Anantha, Bhanu Bathula
2018 Advances in Modelling and Analysis B  
In this paper performance of Traditional Recommender Systems and Deep Learning-based Recommender Systems are compared.  ...  Traditional Recommender Systems provided the solutions in recommending the products. This in turn help companies to generate good revenue. Now a day Deep Learning is using in every domain.  ...  The big challenge for the Collaborative Filtering is sparse data and Cold start problem. It is mainly used in recommending the products based on the user's interest.  ... 
doi:10.18280/ama_b.610202 fatcat:4iur3pjuujdkha6dyt3v6ntequ

Artificial intelligence in recommender systems

Qian Zhang, Jie Lu, Yaochu Jin
2020 Complex & Intelligent Systems  
accuracy and solve data sparsity and cold start problems.  ...  learning, genetic algorithms, evolutionary algorithms, neural networks and deep learning, and active learning.  ...  First, it is not able to deal with the cold-start problem. When a new user/item enters the system, there are no ratings for the system to use to make predictions.  ... 
doi:10.1007/s40747-020-00212-w fatcat:ev3cyoy2mjeuhmq3rymkx2shsy


Ssvr Kumar Addagarla
2019 International Journal of Electronic Commerce Studies  
system like location-aware, context-aware, and Deep Learning techniques.  ...  Various improvements and limitations in Recommendation systems have been listed out with evolution metrics for analyzing the accuracy of the algorithms.  ...  Some of the hybrid RS addressed the scalability, sparsity, cold-start problems but not completely overcome and cold-start problem still an open challenge for the researchers.  ... 
doi:10.7903/ijecs.1705 fatcat:puqvc6uhd5dhppuatartqqq6ki
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