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Machine Learning Algorithms for Recommender System - a comparative analysis

Satya Prakash Sahu, Anand Nautiyal, Mahendra Prasad
2017 International Journal of Computer Applications Technology and Research  
Among all these techniques we are dealing with Content Based Filtering, Collaborative Based Filtering, Hybrid Content-Collaborative Based Filtering, k-mean clustering and Naive Bayes classifier.  ...  We have exploited these algorithms to their extreme in order to achieve the best possible precision and have presented a comprehensive comparative analysis.  ...  In the Collaborative Based Filtering, recommendation for a user is governed by other users' profiles.  ... 
doi:10.7753/ijcatr0602.1005 fatcat:u7cnuuwdebd7faegbcxbhcrxwe

Use of Deep Learning in Modern Recommendation System: A Summary of Recent Works

Ayush Singhal, Pradeep Sinha, Rakesh Pant
2017 International Journal of Computer Applications  
We organize the review in three parts: Collaborative system, Content based system and Hybrid system.  ...  Finally, we also provide future directions of research which are possible based on the current state of use of deep learning in recommendation systems.  ...  Collaborative filtering based systems In this section, we summarize the main contributions of deep learning towards collaborative filtering approaches.  ... 
doi:10.5120/ijca2017916055 fatcat:m6icpquumbgczhrdnya7x35of4

AI based Book Recommender System with Hybrid Approach

Mercy Milcah Y, Moorthi K, Jansons Institute of Technology
2020 International Journal of Engineering Research and  
using a hybrid approach in recommendation.  ...  By using filtering methods for pre-processing the data, recommendations are provided either through collaborative filtering or through content-based filtering.  ...  The major benefit of a demographic approach is that it does not require any user ratings like that in collaborative and content based recommender systems.  ... 
doi:10.17577/ijertv9is020416 fatcat:csnwlajvurdsrko3jd27bda63y

A Content-Based Approach to Recommend TV Programs Enhanced with Delayering Tagging

Fulian Yin, Xingyi Pan, Huixin Liu, Jianping Chai
2016 International Journal of Multimedia and Ubiquitous Engineering  
In response to explore how to extract the recommended items' features, a method is put forward called a Content-based TV Program Recommendation Approach Enhanced with Delayering Tagging.  ...  The Content-based approach is optimized to recommend TV programs and improved the way to extract the recommended items' features.  ...  Experiment 2: Collaborative Filtering Recommendation Based on Neighborhood The collaborative filtering recommendation based on neighborhood includes userbased collaborative filtering (User-CF) and item-based  ... 
doi:10.14257/ijmue.2016.11.9.25 fatcat:agq6kni2ivhqxpzo3uhcjnjw5a

http://www.jestr.org/downloads/Volume10Issue4/fulltext171042017.pdf

Shahab Saquib Sohail, Jamshed Siddiqui, Rashid Ali
2017 Journal of Engineering Science and Technology Review  
This paper presents the state of art techniques in recommender systems (RS).  ...  The various techniques are diagrammatically illustrated which on one hand helps a naïve researcher in this field to accommodate the on-going researches and establish a strong base, on the other hand it  ...  [143] suggested a collaborative filtering-based recommender system using implicit feedback.  ... 
doi:10.25103/jestr.104.18 fatcat:hlipfdlfrrejtdjikbivhw3ejm

Collaborative Filtering with Preventing Fake Ratings

Dr. A. Srinivasa Rao, B. Bhagyalakshmi, Ab. Sirajunnisa | Md. Ashraf | E. Harika | Ch. Gangadhar
2018 International Journal of Trend in Scientific Research and Development  
In this paper, we increase a set of matrix factorization (MF) a nearest-neighbor (NN)-based recommended systems (RSs) that explore user social network and group association information for social voting  ...  order dominates group association sequence in NN-based approaches.  ...  One-class collaborative filtering deals with secondary rating data, reflecting a user action or not.  ... 
doi:10.31142/ijtsrd11334 fatcat:hdapuiilrngxbn5fsbgah26x5i

A Review on Techniques of Recommendation System

Palika Jajoo, Dolly Mittal
2021 SKIT research journal  
In this paper, we present the three major techniques that are used for generating recommendation: Collaborative filtering, Contentbased and Hybrid.  ...  Recommendation System is a method to find the needs of the customer either it can be data or an item from the enormous amount of online data.  ...  Work on recommendation system with Machine Learning and Deep Learning has been done and in progressive to provide better recommendations in aspect of quality.  ... 
doi:10.47904/ijskit.11.2.2021.31-36 fatcat:7j7ernbqmbfildqk3z2rx7xzti

A Survey of e-Commerce Recommender Systems

Farida Karimova
2016 European Scientific Journal  
Recommender systems provide great opportunities to businesses, therefore research on developing new recommender system techniques and methods have been receiving increasing attention.  ...  This paper reviews recent developments in recommender systems in the domain of ecommerce.  ...  Collaborative filtering based recommendation technique Collaborative filtering (CF) based recommender systems recommend an item for a particular user based on the items previously preferred by other users  ... 
doi:10.19044/esj.2016.v12n34p75 fatcat:5jzyl75lpfajzf3bdleoozt45u

Incorporating Multiple Attributes in Social Networks to Enhance the Collaborative Filtering Recommendation Algorithm

Jian Yi, Xiao Yunpeng, Liu Yanbing
2016 International Journal of Advanced Computer Science and Applications  
In view of the existing user similarity calculation principle of recommendation algorithm is single, and recommender system accuracy is not well, we propose a novel social multi-attribute collaborative  ...  recommender system with the improved comprehensive similarity computing model.  ...  The objective of this paper is to propose a new comprehensive similarity model to determine neighbors set and top-N items list recommended to the target user, thereby making a new contribution towards  ... 
doi:10.14569/ijacsa.2016.070408 fatcat:36tkcnoe5jbkdjqxwlzrlhzjri

Panorama of Recommender Systems to Support Learning [chapter]

Hendrik Drachsler, Katrien Verbert, Olga C. Santos, Nikos Manouselis
2015 Recommender Systems Handbook  
In this meta-review 82 recommender systems from 35 different countries have been investigated and categorised according to a given classification framework.  ...  This chapter presents an analysis of recommender systems in Technology-Enhanced Learning along their 15 years existence (2000-2014).  ...  The weights of the filters can be assigned by the user him/herself or automatically calculated by the system [123] . [ RS72-2014] shows that a graph-based collaborative filtering algorithm can improve  ... 
doi:10.1007/978-1-4899-7637-6_12 fatcat:fdi74mokhnhuhfqijgwmucgbny

Comprehensive study and Analysis of Extreme Multi-Label Classification Approach

Purvi Prajapati
2020 International Journal of Advanced Trends in Computer Science and Engineering  
In Recommendation System, the main goal is to recommend users based on the available data.  ...  This paper discussed different approaches for large scale Recommendation System using Extreme Multi-Label Classification Approach and empirical evaluation carried out on three multi-label datasets which  ...  Basically Recommendation System is categorized in to content based recommendation and collaborative filtering based recommendation [1, 2, 23, 26] .  ... 
doi:10.30534/ijatcse/2020/83922020 fatcat:qzgi7mtk4bfnbk4dmdjblp373y

Towards Semantics-Aware Recommender System: A LOD-Based Approach

Asmaa Fridi, Sidi Mohamed Benslimane
2017 International Journal of Modern Education and Computer Science  
They provide a valuable source of information that can improve conventional recommender systems, if properly exploited.  ...  In this paper, we aim to demonstrate that adding semantic information from LOD enhance the effectiveness of traditional collaborative filtering.  ...  The proposed approach demonstrated a higher accuracy in comparison with a user-based collaborative filtering technique. Alhamid et al.  ... 
doi:10.5815/ijmecs.2017.02.07 fatcat:fhvl2h5knrhsbibmzhkp6ruf3q

Optimizing Approach of Recommendation System using Web Usage Mining and Social Media for E-commerce

Anurag Singh, Subhadra Shaw
2020 International Journal of Computer Applications  
In the proposed work, prediction using collaborative filtering, socio-demographic methodology, and sentiment analysis are integrated into a weighted system that is consistent with producing a single recommendation  ...  This optimization approach improves the effectiveness of the recommendation process.  ...  Collaborative filtering is a technique where users can filter their favorite items based on similar customer responses.  ... 
doi:10.5120/ijca2020920510 fatcat:wbla4qm54jcsjionsjopjqitl4

A Survey on Hybrid Recommendation Engine for Businesses and Users

Spurthy Mutturaj, Department of ISE, JSS Academy of Technical Education, Bangalore, Karnataka, India, Shwetha B, Sangeetha P, Shivani Beldale, Sahana V
2021 International Journal of Information Engineering and Electronic Business  
In this research, we have analyzed several papers and majority of them have used collaborative and content-based filtering techniques to implement recommender system.  ...  To build a recommender system, we require abundant amount of data which comprises of a spectrum of reviews and sentiments from all user domains.  ...  Popular recommender systems use two main approaches in their implementation: Collaborative filtering and context-based filtering. Knowledge based recommender system have also been implemented.  ... 
doi:10.5815/ijieeb.2021.03.03 fatcat:nzqk7x6w5zf4zen52t4utfsoye

A Categorical Review of Recommender Systems

RVVSV Prasad
2012 International Journal of Distributed and Parallel systems  
Recommender Systems (RS) are software tools and techniques providing suggestions for items and/or services to be of use to a user.  ...  This paper presents a categorical review of the field of recommender systems and describes the state-of-the-art of the recommendation methods that are usually classified into four categories: Content based  ...  Collaborative filtering Recommender Systems The goal of collaborative filtering systems is to suggest new items or predict the utility of a certain item for a particular user based on users past liking  ... 
doi:10.5121/ijdps.2012.3507 fatcat:e5cgc2667fhzbbaqibkjouevo4
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