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Neural Collaborative with Sentence BERT for News Recommender System

Budi Juarto, Abba Suganda Girsang
2021 JOIV: International Journal on Informatics Visualization  
Neural collaborative filtering is usually being used for recommendation systems by combining collaborative filtering with neural networks.  ...  The method that can be used in providing recommendations from the same user is collaborative filtering.  ...  content-based filtering and collaborative filtering.  ... 
doi:10.30630/joiv.5.4.678 fatcat:gaboovym3rax3h5js2cod25nxa

User Demographic Information and Deep Neural Network in Film Recommendation System based on Collaborative Filtering

Adrianus Lunardi Pradana, Computer Science Department, BINUS Graduate Program – Master of Computer Science Bina Nusantara University, Jakarta, Indonesia 11480, Antoni Wibowo
2022 International Journal of Emerging Technology and Advanced Engineering  
One of the major problems in deep neural network based collaborative filtering recommendation system was coldstart problem.  ...  Research about implementation of deep neural network in recommender system based on collaborative filtering received many attentions recently.  ...  system based on collaborative filtering.  ... 
doi:10.46338/ijetae0522_16 fatcat:gfc5nm6cavgbhnb6xnujacgghm

Link prediction approach to collaborative filtering

Zan Huang, Xin Li, Hsinchun Chen
2005 Proceedings of the 5th ACM/IEEE-CS joint conference on Digital libraries - JCDL '05  
One of the most commonlyused and successful recommendation algorithms is collaborative filtering, which explores the correlations within user-item interactions to infer user interests and preferences.  ...  Our preliminary experimental results based on a book recommendation dataset show that some of these measures achieved significantly better performance than standard collaborative filtering algorithms.  ...  ACKNOWLEDGMENTS This work was supported in part by: NSF Information Technology Research, "Developing a collaborative information and knowledge management infrastructure", IIS-0114011, 2001-2004.  ... 
doi:10.1145/1065385.1065415 dblp:conf/jcdl/HuangLC05 fatcat:psjde3n7g5chbjusvi3qa3ssyy

A Comparison between Item-Based and Tag-Based Recommendation on a Knowledge Management System: A Preliminary Investigation

Worasit Choochaiwattana
2015 International Journal of Information and Education Technology  
Index Terms-Collaborative filtering, content based filtering, item-based recommendation, tag-based recommendation, knowledge management system.  ...  This paper compared the effectiveness of two recommendation techniques namely, an item-based recommendation (a collaborative filtering technique) and a tag-based recommendation (a content-based filtering  ...  Thus, the knowledge management system could benefit from applying A Comparison between Item-Based and Tag collaborative filtering or content-based filtering to provide automated knowledge dissemination  ... 
doi:10.7763/ijiet.2015.v5.605 fatcat:w3nccdhrwbesxfme655amz64hq

Collaborative Filtering for People to People Recommendation in Social Networks [chapter]

Xiongcai Cai, Michael Bain, Alfred Krzywicki, Wayne Wobcke, Yang Sok Kim, Paul Compton, Ashesh Mahidadia
2010 Lecture Notes in Computer Science  
In social networks this goes beyond traditional, merely taste-based, collaborative filtering for item selection.  ...  In this paper we propose a model that fully captures the bilateral role of user interactions within a social network and formulate collaborative filtering methods to enable people to people recommendation  ...  Bilateral Collaborative Filtering A Prototypical Collaborative Filtering Algorithm Traditional collaborative filtering can operate in two directions: user-based or item-based.  ... 
doi:10.1007/978-3-642-17432-2_48 fatcat:fc6xn5ac5nalfjxvagi7fyx4my

Recommendation Systems for E-Commerce: A Review

Priya S, Mansoor Hussain D
2017 IJARCCE  
Nowadays, there is a big variety of different approaches and algorithms for data filtering and recommendation giving.Recommendation techniques can be classified into three major divisions: Collaborative  ...  Filtering, Content Based and Hybrid Recommendations.This paper compares and elaborates these approaches and discusses their limitations by describing the problems suffered by recommendation techniques  ...  filtering TiVo Hybrid Item-based collaborative filtering , Bayesian content based filtering Jester 2.0 Collaborative Cluster Libra Hybrid Bayesian Learning algorithm Group Lens Hybrid Correlation  ... 
doi:10.17148/ijarcce.2017.6496 fatcat:657sncidxrcezfoczizqpdj5ye

Hidden Details of Negotiation: The Mechanics of Reality-Based Collaboration in Information Seeking [chapter]

Mathias Heilig, Stephan Huber, Jens Gerken, Mischa Demarmels, Katrin Allmendinger, Harald Reiterer
2011 Lecture Notes in Computer Science  
One is based on an interactive multi-touch tabletop in combination with on-screen tangibles, therefore qualifying as a reality-based UI, while the other interface uses three synchronized PCs each controlled  ...  By blending characteristics of real-world interaction and social qualities with the advantages of virtual computer systems, they inherently change the possibilities for collaboration, but until now this  ...  interaction strategies in comparison to PC-based UIs?  ... 
doi:10.1007/978-3-642-23771-3_46 fatcat:qcacojdadvcb5n7af6xnz2six4

A Simple Graph Convolutional Network with Abundant Interaction for Collaborative Filtering

Ronghui Guo, Xunkai Li, Youpeng Hu, Yixuan Wu, Xin Xiong, Meixia Qu
2021 IEEE Access  
Recently, recommender systems based on Graph Convolution Network (GCN) have become a research hotspot, especially in collaborative filtering.  ...  Second, LII-GCCF removes the unnecessary nonlinear transformation based on the characteristics of collaborative filtering to simplify the graph convolution process.  ...  A collaborative filtering model based on deep learning consists of two components: First, the embeddings of users and items are learned from the historical interactions.  ... 
doi:10.1109/access.2021.3083600 fatcat:rk5xzfziavf53bmc67r2iuefke

A Comparative Study between Collaborative Filtering Techniques and Generate Personalized Story Recommendations for the Vixio Application

Albert Darmawan, Ida Bagus Kerthyayana Manuaba
2019 International Journal on Advanced Science, Engineering and Information Technology  
Interactive fiction (or text-based game) is a game that consists of texts which are used to bring interactivity to a story.  ...  This paper also discusses determining which techniques are better to be implemented inside the recommender system by conducting a comparative study between five collaborative filtering techniques, which  ...  To implement this idea, we used collaborative filtering, which provides recommendations based on user preferences, in comparison with other users' preferences.  ... 
doi:10.18517/ijaseit.9.4.7402 fatcat:m3yh3u5lsfcoviescchqhopkiy

Incentivizing Participation in Clinical Trials [article]

Yingkai Li, Aleksandrs Slivkins
2022 arXiv   pre-print
], graph-based collaborative filtering organize interaction https://github.com/RUCAIBox/RecBole  ...  In general, GNN-based collaborative filtering methods [? ? ?  ...  For comparison, we also report the collection of randomly sampled items.  ... 
arXiv:2202.06191v2 fatcat:impmzif57bdbdm5unqdtfmld3a

Graph Trend Filtering Networks for Recommendations [article]

Wenqi Fan, Xiaorui Liu, Wei Jin, Xiangyu Zhao, Jiliang Tang, Qing Li
2022 arXiv   pre-print
To address these drawbacks, we introduce a principled graph trend collaborative filtering method and propose the Graph Trend Filtering Networks for recommendations (GTN) that can capture the adaptive reliability  ...  Despite their success, most existing GNNs-based recommender systems overlook the existence of interactions caused by unreliable behaviors (e.g., random/bait clicks) and uniformly treat all the interactions  ...  RELATED WORK 5.1 Collaborative Filtering Collaborative Filtering (CF) is one of the most popular techniques in the modern recommender systems [41, 57] .  ... 
arXiv:2108.05552v2 fatcat:whx7iv5eonbcdelkzz2ob3ofu4

Advertising Popularity Feature Collaborative Recommendation Algorithm Based on Attention-LSTM Model

Yang Su, Xiangwei Kong, Guobao Liu, Jian Su
2021 Security and Communication Networks  
Experimental results on the KDDCUP2012 dataset show that this model collaborative filtering and recommendation algorithm has better scalability and better recommendation quality.  ...  To accurately predict the click-through rate (CTR) and use it for ad recommendation, we propose a deep attention AD popularity prediction model (DAFCT) based on label recommendation technology and collaborative  ...  collaborative ad recommendation algorithm, the label-based ad recommendation algorithm, the label-item relationship- based ad recommendation algorithm, and the collaborative filtering ad recommendation  ... 
doi:10.1155/2021/9940232 fatcat:4aaqsvb5rvbcfahpombwild3qy

Recommendation Systems in the Big Data Era

2020 VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE  
This paper presents an overview about recommendation systems and a review of generation of recommendation methods based on categories like contentbased, collaborative, and hybrid approaches.  ...  Collaborative filtering based systems perform best on a large user space. c.  ...  Collaborative filtering engines can overcome "filter bubble" problem, as user finds and connects subspace in the item space.  Limitations to Collaborative Filtering based Recommender System: a.  ... 
doi:10.35940/ijitee.l1006.10812s319 fatcat:u66i3i7jpbdexj5fnpqbhtbnue

Knowledge Graph Embedding Based Collaborative Filtering

Yuhang Zhang, Jun Wang, Jie Luo
2020 IEEE Access  
KNOWLEDGE GRAPH EMBEDDING BASED COLLABORATIVE FILTERING In order to model the interactions between users and items in the scenario of collaborative filtering with implicit feedback, where users' preferences  ...  Deep Collaborative Filtering (DeepCF) model is propose by [12] to combine the advantages of representation learning based and matching function learning based collaborative filtering to overcome flaws  ...  The current integration of knowledge graph embedding to collaborative filtering is still preliminary, finding better ways for integrating is also an interesting topic.  ... 
doi:10.1109/access.2020.3011105 fatcat:v2bja3sz2beyngugsvwb6t2j7q

Data Analytics and Data monitoring Based on Database Recommendation - A Comparison

Pooja Mudgil, Paras Jain, Vikas Singh
2019 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
A comparison of various analysis algorithms based for recommendation systems used in the market and businesses has their usage without considering the fact that if used with correct algorithm can increase  ...  Comparison is obtained in this paper after studying and researching various obtained algorithms present currently.  ...  Ringo and grouplens, type of similar content based filterers are available in the market all based on collaborative filtering methods assisting users to localise the articles as in [4][5].  ... 
doi:10.32628/cseit1952312 fatcat:eaoo7jptbjcdvfbz7m4v5bibia
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