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Measures of Similarity in Memory-Based Collaborative Filtering Recommender System

Shalini Christabel Stephen, Hong Xie, Shri Rai
2017 Proceedings of the 4th Multidisciplinary International Social Networks Conference on ZZZ - MISNC '17  
The effectiveness of existing memory based algorithms depend on the similarity measure that is used to identify nearest neighbours.  ...  Collaborative filtering (CF) technique in recommender systems (RS) is a well-known and popular technique that exploits relationships between users or items to make product recommendations to an active  ...  Moreover, content-based filtering methods cannot filter items based on assessment of quality, style or point of view [10] . 2) Collaborative filtering: this is a complementary filtering technique that  ... 
doi:10.1145/3092090.3092105 fatcat:v5hxtgfs25hdxivme5woubpltq

Evaluation of Similarity Functions by using User based Collaborative Filtering approach in Recommendation Systems
English

Shaivya Kaushik, Pradeep Tomar
2015 International Journal of Engineering Trends and Technoloy  
introduce user based collaborative filtering approach and the similarity function.The algorithm will identify relationships between different users and then compute recommendation for the users.This paperpresent  ...  Recommendation Systems has been comprehensively analysed and are changing from novelties used by a few E-commerce sites in the past decades.Many of the popular and largest commerce websites are widely  ...  Fig1:Collaborative Filtering Structure Collaborative filtering algorithms are usually categorized into two subgroups: memory-based and model-based.  ... 
doi:10.14445/22315381/ijett-v21p234 fatcat:2hu5cjwzwvhobduv3hbirilz4y

Data Sensitive Recommendation Based On Community Detection

Chang Su, Yue Yu, Xianzhong Xie, Yukun Wang
2015 Foundations of Computing and Decision Sciences  
Collaborative filtering is one of the most successful and widely used recommendation systems.  ...  A hybrid collaborative filtering method called data sensitive recommendation based on community detection (DSRCD) is proposed as a solution to cold start and data sparsity problems in CF.  ...  Based on Social Network, proposed by Zhu et al [23] , Leveraging Overlapping Communities Detection Improve Personalized Recommendation in Folksonomy Networks, proposed by Su et al [19] .  ... 
doi:10.1515/fcds-2015-0010 fatcat:5gx7on327bauhk4xwc7uterx6q

A Recommender System based on the Immune Network [article]

Steve Cazyer, Uwe Aickelin
2008 arXiv   pre-print
This paper presents an artificial immune system (AIS) that exploits some of these characteristics and is applied to the task of film recommendation by collaborative filtering (CF).  ...  Rather we intend to identify a sub-set of good matches on which recommendations can be based.  ...  That is, your recommendations are based purely on the votes of your neighbours, and not on the content of the item.  ... 
arXiv:0801.3547v2 fatcat:nmeov6bz7ffnxmthrqjpgqdzyi

A Recommender System Based on the Immune Network

Steve Cayzer, Uwe Aickelin
2012 Social Science Research Network  
This paper presents an artificial immune system (AIS) that exploits some of these characteristics and is applied to the task of film recommendation by collaborative filtering (CF).  ...  Rather we intend to identify a sub-set of good matches on which recommendations can be based.  ...  That is, your recommendations are based purely on the votes of your neighbours, and not on the content of the item.  ... 
doi:10.2139/ssrn.2832049 fatcat:voapox37v5dijncb3wvfquq7sa

Recommender System Based on the Immune Network

Steve Cayzer, Uwe Aickelin
2002 Social Science Research Network  
This paper presents an artificial immune system (AIS) that exploits some of these characteristics and is applied to the task of film recommendation by collaborative filtering (CF).  ...  Rather we intend to identify a sub-set of good matches on which recommendations can be based.  ...  That is, your recommendations are based purely on the votes of your neighbours, and not on the content of the item.  ... 
doi:10.2139/ssrn.2832078 fatcat:4f26jjlxhbhrlp56u6umrvt5yi

Collaborative Filtering Using a Regression-Based Approach

Slobodan Vucetic, Zoran Obradovic
2005 Knowledge and Information Systems  
The task of collaborative filtering is to predict the preferences of an active user for Please check address. unseen items given preferences of other users.  ...  Based on ratings provided by an active user for some of the items, the experts are combined by using statistical methods to predict the user's preferences for the remaining items.  ...  Suggestions from anonymous reviewers that resulted in significantly improved presentation are greatly appreciated.  ... 
doi:10.1007/s10115-003-0123-8 fatcat:226h76exbndffcayvgs4kwaaxm

Which Recommender System Can Best Fit Social Learning Platforms? [chapter]

Soude Fazeli, Babak Loni, Hendrik Drachsler, Peter Sloep
2014 Lecture Notes in Computer Science  
We therefore take into account social interactions of users to make recommendations on learning resources.  ...  The results show that the graph-based approach can help to improve performance of the baseline recommenders; particularly for rather sparse educational datasets used in this study.  ...  Martin Wolpers and Katja Niemann, for providing us with both the MACE and OpenScout datasets on a short notice. Without their support, this study would not have been possible.  ... 
doi:10.1007/978-3-319-11200-8_7 fatcat:47mtvsq6i5dcdaivdkerptkgou

Temporal Community-Based Collaborative Filtering to Relieve from Cold-Start and Sparsity Problems

Anupama Angadi, 10.5815/ijisa.2018.10.06, Satya Keerthi Gorripati, P. Suresh Varma
2018 International Journal of Intelligent Systems and Applications  
This paper proposes a novel architecture, called Temporal Community-based Collaborative filtering, which is an association of recommendation and the dynamic community algorithm in order to exploit the  ...  Traditional recommender systems rely on similarity of users or items in static networks where the user/item neighbourhood is almost same and they generate the same recommendations since the network is  ...  User-based collaborative filtering approach correlates users by excavating (similar) ratings and recommends unseen or new items that were preferred by similar users (see Fig. 3 ).  ... 
doi:10.5815/ijisa.2018.10.06 fatcat:ciabnek3zffxbagvyefjhp2rn4

Connecting users and items with weighted tags for personalized item recommendations

Huizhi Liang, Yue Xu, Yuefeng Li, Richi Nayak, Xiaohui Tao
2010 Proceedings of the 21st ACM conference on Hypertext and hypermedia - HT '10  
In addition, the user and item-based collaborative filtering combined with the content filtering approach are explored.  ...  However, since tags are arbitrary words given by users, they contain a lot of noise such as tag synonyms, semantic ambiguities and personal tags.  ...  The collaborative filtering approach can be classified into memory based and model based approaches.  ... 
doi:10.1145/1810617.1810628 dblp:conf/ht/LiangXLNT10 fatcat:d4wjkcnjureppeawiq34qn2nci

Hybrid Recommender System Based on Personal Behavior Mining [article]

Zhiyuan Fang, Lingqi Zhang, Kun Chen
2016 arXiv   pre-print
Classical personalized recommender algorithm includes item-based collaborative filtering method applied in Amazon, matrix factorization based collaborative filtering algorithm from Netflix, etc.  ...  We use desensitized mobile transaction record provided by T-mall, Alibaba to build a hybrid dynamic recommender system.  ...  Traditional CF method include memory based and model based Collaborative Filtering algorithm.  ... 
arXiv:1607.02754v1 fatcat:4jurfvmnkzfcvi7aitai2xq5fq

The collaborative filtering recommendation based on SOM cluster-indexing CBR

T Roh
2003 Expert systems with applications  
This paper demonstrates the utility of the CF recommendation based on SOM cluster-indexing CBR with validation against control algorithms through an open dataset of user preference. q  ...  This model combines a CF algorithm with two machine learning processes, Self-Organizing Map (SOM) and Case Based Reasoning (CBR) by changing an unsupervized clustering problem into a supervized user preference  ...  Acknowledgements This research was financially supported by Han Sung University in the year of 2003.  ... 
doi:10.1016/s0957-4174(03)00067-8 fatcat:qq5soh7eazajnpiubbbls35ndu

Understanding Similarity Metrics in Neighbour-based Recommender Systems

Alejandro Bellogín, Arjen P. de Vries
2013 Proceedings of the 2013 Conference on the Theory of Information Retrieval - ICTIR '13  
Neighbour-based collaborative filtering is a recommendation technique that provides meaningful and, usually, accurate recommendations.  ...  Specifically, we define two sets of features, a first one based on statistics computed over the distance distribution in the neighbourhood, and, a second one based on the nearest neighbour graph.  ...  The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no.246016, and has partially been supported by the Dutch  ... 
doi:10.1145/2499178.2499186 dblp:conf/ictir/BelloginV13 fatcat:kg5az72bxjgl3fyamwl4jlaoaq

Completing partial recipes using item-based collaborative filtering to recommend ingredients [article]

Paula Fermín Cueto, Meeke Roet, Agnieszka Słowik
2020 arXiv   pre-print
We implement the item-based collaborative filtering algorithm using a high-dimensional, sparse dataset of recipes, which inherently contains only implicit feedback.  ...  Applying collaborative filtering to build recommendation systems in domains where only implicit feedback is available is also a rapidly growing research area.  ...  Based on their evidence, we have selected the item-based approach as our preferred method for the task at hand.  ... 
arXiv:1907.12380v2 fatcat:jsoaxknnsnhphlqpsel5pleywi

An enhanced kernel weighted collaborative recommended system to alleviate sparsity

S. Babeetha, B. Muruganantham, S.Ganesh Kumar, A. Murugan
2020 International Journal of Electrical and Computer Engineering (IJECE)  
Collaborative Filtering (CF) system is one of the widely used democratic recommender system where it completely rely on user ratings to provide recommendations for the users.  ...  In this paper, an enhanced Collaborative Filtering system is proposed using Kernel Weighted K-means Clustering (KWKC) approach using Radial basis Functions (RBF) for eliminate the Sparsity problem where  ...  RELATED WORK As per [9] , there are two types in Collaborative Filtering system:model(User/item) and memory based (or heuristic-based).  ... 
doi:10.11591/ijece.v10i1.pp447-454 fatcat:7ys6m54szzhbforn6p24zy5hoa
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