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Interactive collaborative filtering

Xiaoxue Zhao, Weinan Zhang, Jun Wang
2013 Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13  
In this paper, we study collaborative filtering (CF) in an interactive setting, in which a recommender system continuously recommends items to individual users and receives interactive feedback.  ...  ' personal preferences and contexts evolve over time during the interactions with the system.  ...  In this paper, in order to naturally integrate with existing collaborative filtering approaches, we address the Interactive Collaborative Filtering (ICF) problem under the popular matrix factorization  ... 
doi:10.1145/2505515.2505690 dblp:conf/cikm/ZhaoZW13 fatcat:tzufqxtwtzffxjcimiqnqnbnfy

Geometric Interaction Augmented Graph Collaborative Filtering [article]

Yiding Zhang, Chaozhuo Li, Senzhang Wang, Jianxun Lian, Xing Xie
2022 arXiv   pre-print
Graph-based collaborative filtering is capable of capturing the essential and abundant collaborative signals from the high-order interactions, and thus received increasingly research interests.  ...  Conventionally, the embeddings of users and items are defined in the Euclidean spaces, along with the propagation on the interaction graphs.  ...  In this paper, we propose a novel geometric graph collaborative filtering (GGCF) algorithm, which leverages both Euclidean spaces and hyperbolic spaces to encode the user-item interaction graphs on the  ... 
arXiv:2208.01250v1 fatcat:kkiyhbs6cfgonjrp65me7pn5by

Efficient Neural Interaction Function Search for Collaborative Filtering [article]

Quanming Yao, Xiangning Chen, James Kwok, Yong Li, Cho-Jui Hsieh
2020 arXiv   pre-print
In collaborative filtering (CF), interaction function (IFC) plays the important role of capturing interactions among items and users.  ...  Motivated by the recent success of automated machine learning (AutoML), we propose in this paper the search for simple neural interaction functions (SIF) in CF.  ...  (NCF) [17] , and convolutional neural collaborative filtering (ConvNCF) [16] .  ... 
arXiv:1906.12091v3 fatcat:xnwqonn4eva5rnvd54qg7rpl2i

COMET: Convolutional Dimension Interaction for Collaborative Filtering [article]

Zhuoyi Lin, Lei Feng, Xingzhi Guo, Yu Zhang, Rui Yin, Chee Keong Kwoh, Chi Xu
2021 arXiv   pre-print
In this paper, we propose a novel latent factor model called COMET (COnvolutional diMEnsion inTeraction), which simultaneously model the high-order interaction patterns among historical interactions and  ...  among historical interactions and embedding dimensions.  ...  His research interests include collaborative filtering, deep learning and their applications.  ... 
arXiv:2007.14129v5 fatcat:qrejcxuv7zhmdmqnq7kutsxbyi

PoPI: Glyph Designs for Collaborative Filtering on Interactive Tabletops [article]

Sven Charleer, Joris Klerkx, Erik Duval
2015 Eurographics Conference on Visualization  
Filtering data on a visualization can be a challenge when multiple people work on a shared visualization, for instance on an interactive tabletop.  ...  To support per-user filters simultaneously across a shared visualization, we explore different glyph approaches that complement data points with per-user filter status information.  ...  However, visualization interactions such as sorting, filtering and navigating the data can disturb the workflow of others.  ... 
doi:10.2312/eurovisshort.20151132 dblp:conf/vissym/CharleerKD15 fatcat:rb5q7bc6l5g5tg4xtit6ep5x3a

Learning to Denoise Unreliable Interactions for Graph Collaborative Filtering

Changxin Tian, Yuexiang Xie, Yaliang Li, Nan Yang, Wayne Xin Zhao
2022 Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval  
To tackle the above issues, this paper presents a novel GNN-based CF model, named Robust Graph Collaborative Filtering (RGCF), to denoise unreliable interactions for recommendation.  ...  Recently, graph neural networks (GNN) have been successfully applied to recommender systems as an effective collaborative filtering (CF) approach.  ...  To address the aforementioned challenges, we propose a novel GNN-based collaborative filtering approach by denoising unreliable interactions, named RGCF (Robust Graph Collaborative Filtering).  ... 
doi:10.1145/3477495.3531889 fatcat:le2y3wxuine4hovgubxhb66kbq

Collaborative Differential Evolution Filtering for Tracking Hand-Object Interactions

Dongnian Li, Yang Guo, Chengjun Chen, Zhengxu Zhao
2020 IEEE Access  
To overcome the difficulties of optimum searching in the handobject high-dimensional space, we propose a new algorithm -collaborative differential evolution filtering (CoDEF) -for tracking hand-object  ...  interactions.  ...  COLLABORATIVE DIFFERENTIAL EVOLUTION FILTERING We integrate the DE algorithm into the PF framework for tracking hand-object interactions.  ... 
doi:10.1109/access.2020.3015834 fatcat:tzwqyutlobbbxgc5tjvhdi2hkq

Online Interactive Collaborative Filtering Using Multi-Armed Bandit with Dependent Arms [article]

Qing Wang, Chunqiu Zeng, Wubai Zhou, Tao Li, Larisa Shwartz, Genady Ya. Grabarnik
2017 arXiv   pre-print
In this paper, we study online interactive collaborative filtering problems by considering the dependencies among items.  ...  To address these issues, collaborative filtering (CF), one of the recommendation techniques relying on the interaction data only, as well as the online multi-armed bandit mechanisms, capable of achieving  ...  Interactive Collaborative Filtering (ICF) [32] tackles these problems in a partially online se ing leveraging PMF framework and bandit algorithms.  ... 
arXiv:1708.03058v2 fatcat:xyflokb6qngn5dp2x3otzmt5xy

Interaction-Based Collaborative Filtering Methods for Recommendation in Online Dating [chapter]

Alfred Krzywicki, Wayne Wobcke, Xiongcai Cai, Ashesh Mahidadia, Michael Bain, Paul Compton, Yang Sok Kim
2010 Lecture Notes in Computer Science  
In this paper, we show that collaborative filtering based on interactions between users is a viable approach in this domain.  ...  We propose a number of new methods and metrics to measure and predict potential improvement in user interaction success, which may lead to increased user satisfaction with the dating site.  ...  By pure collaboration filtering we mean user-to-user or itemto-item methods that are based only on user interactions.  ... 
doi:10.1007/978-3-642-17616-6_31 fatcat:v7z6avst3vdh5jfm5md245oudu

Feature-Driven Interactive Recommendations and Explanations with Collaborative Filtering Approach

Sidra Naveed, Jürgen Ziegler
2019 ACM Conference on Recommender Systems  
Recommender systems (RS) based on collaborative filtering (CF) or content-based filtering (CB) have been shown to be effective means to identify items that are potentially of interest to a user, by mostly  ...  and interactively manipulate their recommendations through existing user profile and explanations.  ...  INTRODUCTION Recommender systems (RS) based on collaborative filtering (CF) and content-based filtering (CB) are widely used techniques [16] .  ... 
dblp:conf/recsys/Naveed019 fatcat:b2ct57iqwnegppjty2t4e5ingi

Collaborative Filtering Recommender Systems

Michael D. Ekstrand
2011 Foundations and Trends® in Human–Computer Interaction  
Nearly two decades of research on collaborative filtering have led to a varied set of algorithms and a rich collection of tools for evaluating their performance.  ...  They represent a powerful method for enabling users to filter through large information and product spaces.  ...  During this time, recommender systems and collaborative filtering became an topic of increasing interest among human-computer interaction, machine learning, and information retrieval researchers.  ... 
doi:10.1561/1100000009 fatcat:34jiby3txreetnp2n4ew5q7swu

Group Bayesian personalized ranking with rich interactions for one-class collaborative filtering

Weike Pan, Li Chen
2016 Neurocomputing  
One-class collaborative filtering or collaborative ranking with implicit feedback has been steadily receiving more attention, mostly due to the "oneclass" characteristics of data in various services, e.g  ...  As a response, we propose a new and improved assumption, group Bayesian personalized ranking (GBPR), via introducing richer interactions among users.  ...  Table 1 : 1 Summary of GBPR and other methods for one-class collaborative filtering w.r.t. different preference assumptions.  ... 
doi:10.1016/j.neucom.2016.05.019 fatcat:wakwh7oo5ncgdidqkwgqi4gqeq

Extraction of design variables using collaborative filtering for interactive genetic algorithms

Tomoyuki Hiroyasu, Hisatake Yokouchi, Misato Tanaka, Mitsunori Miki
2009 2009 IEEE International Conference on Fuzzy Systems  
We constructed the design variables using the relevance of products obtained by Collaborative Filtering and discussed them.  ...  Interactive Genetic Algorithm (iGA) is one of evolutionary computations in which the design candidates are evaluated by human.  ...  EXTRACTION OF DESIGN VARIABLES USING COLLABORATIVE FILTERING FOR INTERACTIVE GENETIC ALGORITHMS A.  ... 
doi:10.1109/fuzzy.2009.5277265 dblp:conf/fuzzIEEE/HiroyasuYTM09 fatcat:oorrirpunfaohgy4tlfyjwvr3y

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  
INDEX TERMS Collaborative filtering, graph convolutional network, recommender systems.  ...  Recently, recommender systems based on Graph Convolution Network (GCN) have become a research hotspot, especially in collaborative filtering.  ...  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

Interaction-Rich Transfer Learning for Collaborative Filtering with Heterogeneous User Feedback

Weike Pan, Zhong Ming
2014 IEEE Intelligent Systems  
There are also some efficient distributed or online collaborative filtering algorithms such as distributed stochastic gradient descent 8 and online multitask collaborative filtering, 9 but they're designed  ...  Here, we focus on this new research problem of collaborative filtering with heterogeneous user feedback, which is associated with few prior works.  ...  collaborative filtering, 4 for example, rating sparsity.  ... 
doi:10.1109/mis.2014.2 fatcat:5orkkd4oqra25dobrkcrg2clwq
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