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A Study of Recommender System Techniques

Reena Pagare, Anita Shinde
2012 International Journal of Computer Applications  
Collaborative filtering techniques play vital component in recommender systems as they generate high-quality recommendations by influencing the likings of society of similar users.  ...  Recommender systems provide an important response to the information overload problem as it presents users more practical and personalized information services.  ...  RECOMMENDATION STRATEGIES The methods used for recommendations can be content based, collaborative filtering and trust based.  ... 
doi:10.5120/7269-0078 fatcat:mjigr6katzestnjfiqra2e7pxu

A Review of Content-Based and Context-Based Recommendation Systems

Umair Javed, Kamran Shaukat, Ibrahim A. Hameed, Farhat Iqbal, Talha Mahboob Alam, Suhuai Luo
2021 International Journal of Emerging Technologies in Learning (iJET)  
To achieve this goal, we have used content-based, collaborative filtering, a hybrid recommender system, and implemented a Web ontology language (OWL).  ...  In a content-based recommender system, the system provides additional options or results that rely on the user's ratings, appraisals, and interests.  ...  The currently widely used techniques are content-based, collaborative filtering, and hybrid techniques.  ... 
doi:10.3991/ijet.v16i03.18851 fatcat:e5br2bdqmvgn7lb7huqmhww3we

Novel Approach of Neural Collaborative Filter by Pairwise Objective Function with Matrix Factorization

Ram Sethuraman, Akshay Havalgi
2018 International Journal of Engineering & Technology  
The concept of deep learning is used in the various fields like text, speech and vision. The proposed work deep neural network is used for recommender system.  ...  The proposed framework is named as NCF which is basically neural network based collaborative filtering. The NCF gives the latent features by reducing the non-linearity and generalizing the matrix.  ...  Related work A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques are proposed and worked on the issue of sparsity and scalability.  ... 
doi:10.14419/ijet.v7i3.12.17840 fatcat:xruysoytwjdildmgxicpuhjyim

Ontology Based Recommender System using Fuzzy Clustering Technique

2019 International Journal of Engineering and Advanced Technology  
In this work, the collaborative filtering recommender is improved by incorporating ontology and fuzzy clustering algorithms to provide ranked recommendations.  ...  Recommender systems (RS) are the agents of information filtering processes. With a myriad of data in the world wide web, reaching the exact entity of interest is the need of the hour.  ...  EVALUATION RESULTS The ontology based recommender system using fuzzy clustering technique is tested with a data corpus.  ... 
doi:10.35940/ijeat.a2206.109119 fatcat:oncsg7r4rnforftqx7uguhkiiq

Using a Semantic Multidimensional Approach to create a Contextual Recommender System

Abdulbaki Uzun, Christian Räck
2010 Lernen, Wissen, Daten, Analysen  
Item recommendations calculated by recommender systems mostly in use today, only rely on item content description, user feedback and profile information.  ...  In modern mobile services, however, contextual information and semantic knowledge can play a significant role concerning the quality of these recommendations.  ...  A contextaware collaborative filtering system is presented by [Chen, 2005] , which generates item recommendations for a user based on different context situations.  ... 
dblp:conf/lwa/UzunR10 fatcat:54j3woqe7jb4tjjm74dmiznc6e

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.  ...  Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information.  ...  The item-based collaborative filtering, a variant of model-based collaborative filtering, starts from the same user rating profile databases and builds an item-item similarity matrix in offline situation  ... 
doi:10.5121/ijcses.2011.2301 fatcat:l3wymqfgqbhovhiy72osnb6hja

Semantically Enhanced Collaborative Filtering on the Web [chapter]

Bamshad Mobasher, Xin Jin, Yanzan Zhou
2004 Lecture Notes in Computer Science  
reference ontologies, is used in conjunction with user-item mappings to create a combined similarity measure and generate predictions.  ...  Item-based Collaborative Filtering (CF) algorithms have been designed to deal with the scalability problems associated with traditional user-based CF approaches without sacrificing recommendation or prediction  ...  [9] on using hierarchical structures in computing similarities, and that of Hotho et al. [13] on ontology-based text clustering.  ... 
doi:10.1007/978-3-540-30123-3_4 fatcat:xwtz4aogw5f4dezpihrv4yhpx4

A Literature Survey on Recommendation System Based on Sentimental Analysis

Achin Jain, Vanita Jain
2016 Advanced Computational Intelligence An International Journal (ACII)  
The articles are categorized into three techniques of recommender system, i.e.; collaborative filtering (CF), content based and context based.  ...  Recommender systems have grown to be a critical research subject after the emergence of the first paper on collaborative filtering in the Nineties.  ...  Other based CF is that it loses useful information for dimensionality reduction Hybrid Collaborative Filtering Techniques In hybrid recommender system, different techniques of collaborative approaches  ... 
doi:10.5121/acii.2016.3103 fatcat:iro2hlh2mnhwdefigjbik4rxfi

Mediation of user models for enhanced personalization in recommender systems

Shlomo Berkovsky, Tsvi Kuflik, Francesco Ricci
2007 User modeling and user-adapted interaction  
This work proposes a general framework and specific methodologies for enhancing the accuracy of user modeling in recommender systems by importing and integrating data collected by other recommender systems  ...  It provides a generic user modeling data representation model, demonstrates its compatibility with existing recommendation techniques, and discusses the general steps of the mediation.  ...  of the Istituto Trentino di Cultura-the Center for Scientific and Technological Research (ITCirst) in Trento, Italy.  ... 
doi:10.1007/s11257-007-9042-9 fatcat:d3lr5r3rafgrno5qnp6rtjqpt4

A multilayer ontology-based hybrid recommendation model

Iván Cantador, Alejandro Bellogín, Pablo Castells
2008 AI Communications  
We propose a novel hybrid recommendation model in which user preferences and item features are described in terms of semantic concepts defined in domain ontologies.  ...  Our approach is tested in two sets of experiments: one including profiles manually defined by real users and another with automatically generated profiles based on data from the IMDb and MovieLens datasets  ...  The expressed content is the view of the authors but not necessarily the view of the MESH or S5T projects as a whole.  ... 
doi:10.3233/aic-2008-0437 fatcat:xqz7zrx4jfhexkorvslhxwffuu

A novel hybrid recommender system approach for student academic advising named COHRS, supported by case-based reasoning and ontology

Charbel Obeid, Christine Lahoud, Khoury El, Pierre-Antoine Champin
2022 Computer Science and Information Systems  
To reach this purpose we proposed a novel hybrid RS approach named (COHRS) that incorporates the Knowledge base (KB) and Collaborative Filtering (CF) recommender techniques.  ...  This hybrid RS approach is supported by the Case based Reasoning (CBR) system and Ontology. Hundreds of queries were processed by our hybrid RS approach.  ...  Techniques such as Demographic-based [34] , Knowledge-based [6] (Constraintbased [10] , Case-based reasoning [15] , Ontology-based [41] ), Content-based filtering [37] , Collaborative Filtering  ... 
doi:10.2298/csis220215011o fatcat:tz7cx6iiezhz3mgxxvnvqntczm

Dealing with Pure New User Cold-Start Problem in Recommendation System Based on Linked Open Data and Social Network Features

Usha Yadav, Neelam Duhan, Komal Kumar Bhatia
2020 Mobile Information Systems  
Currently, researchers are trying hard to produce correct and accurate recommendations by suggesting the use of ontology, but the lack of techniques renders to take its full advantage.  ...  A modified method to calculate user's similarity based on collaborative features to deal with other issues such as accuracy and computation time is also proposed.  ...  A recommender system based on collaborative filtering using ontology and dimensionality reduction techniques [41] EM clustering for clustering and nonincremental SVD for dimensionality reduction, Ontology-based  ... 
doi:10.1155/2020/8912065 fatcat:kp7aoacbnbgefo5udrmnrlluxq

Improving Web Service Recommendation using Clustering with K-NN and SVD Algorithms

2021 KSII Transactions on Internet and Information Systems  
In this paper, our observation was based on the recommended method such as the collaborative filtering (CF) technique which faces some failure from the data sparsity and the cold-start problems.  ...  Then, user ratings were predicted based on the model-based approach, such as singular value decomposition (SVD) and the predictions used for the recommendation.  ...  The dimensionality reduction technique [16] also uses the model-based method.  ... 
doi:10.3837/tiis.2021.05.007 fatcat:srzerpzgpndrdgzps57xbxozyy

A Framework to Formulate Customer Taste from Unstructured Review Data

Bhaskarjyoti Das, V.R. Prathima
2016 Procedia Computer Science  
This paper describes the framework and explains a specific use case such as recommendation system deriving value out of it.  ...  The proposed framework addresses the blind spot in the current techniques of content based recommendation that works well for businesses selling products such as televisions or personal computers but does  ...  User based collaborative filtering uses user neighborhood, item based collaborative filtering uses item neighborhood and content based recommendation uses content neighborhood.  ... 
doi:10.1016/j.procs.2016.07.308 fatcat:dpq2lpktjng5lfk35qb2ei74nq

Intelligent Techniques for Web Personalization [chapter]

Sarabjot Singh Anand, Bamshad Mobasher
2005 Lecture Notes in Computer Science  
The chapter concludes with a discussion on the open challenges that must be addressed by the research community if this technology is to make a positive impact on user satisfaction with the Web.  ...  In this chapter we provide a comprehensive overview of the topic of Intelligent Techniques for Web Personalization.  ...  Model Based Techniques Model based collaborative filtering techniques use a two stage process for recommendation generation.  ... 
doi:10.1007/11577935_1 fatcat:5n22ois2r5dhjj4hizeu7ispi4
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