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Dual-Regularized One-Class Collaborative Filtering

Yuan Yao, Hanghang Tong, Guo Yan, Feng Xu, Xiang Zhang, Boleslaw K. Szymanski, Jian Lu
2014 Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management - CIKM '14  
In this paper, we propose a dual-regularized model for one-class collaborative filtering.  ...  While most of the existing collaborative filtering methods focus on explicit, multi-class settings (e.g., 1-5 stars in movie recommendation), many real-world applications actually belong to the one-class  ...  Recommendation with implicit feedback naturally forms the one-class collaborative filtering (OCCF) problem [24] .  ... 
doi:10.1145/2661829.2662042 dblp:conf/cikm/YaoTYXZSL14 fatcat:yff4xlruqbhzjfgvjwwlhxhixu

CATA++: A Collaborative Dual Attentive Autoencoder Method for Recommending Scientific Articles [article]

Meshal Alfarhood, Jianlin Cheng
2020 arXiv   pre-print
Extensive experiments on three real-world datasets have shown that our dual-way learning strategy has significantly improved the MF performance in comparison with other state-of-the-art MF-based models  ...  Collaborative filtering approaches, and Matrix Factorization (MF) techniques in particular, are widely used in recommender systems.  ...  Problem definition The recommendation problem with implicit data is usually defined as the following: = 1, if interacts with 0, if otherwise (3) where the ones refer to positive (observed) feedback, and  ... 
arXiv:2002.12277v2 fatcat:b6neqcdlafds7djybjxp34uc54

Dual Adversarial Variational Embedding for Robust Recommendation [article]

Qiaomin Yi, Ning Yang, Philip S. Yu
2021 arXiv   pre-print
The extensive experiments conducted on real datasets verify the effectiveness of DAVE on robust recommendation.  ...  In this paper, we propose a novel model called Dual Adversarial Variational Embedding (DAVE) for robust recommendation, which can provide personalized noise reduction for different users and items, and  ...  Pinterest is a dataset consisting of implicit feedbacks, which has been used to evaluate collaborative recommendations on images [9] , [10] .  ... 
arXiv:2106.15779v1 fatcat:3776g6w36rfjnnsmhpu3dgfuha

CoNet: Collaborative Cross Networks for Cross-Domain Recommendation [article]

Guangneng Hu, Yu Zhang, Qiang Yang
2018 arXiv   pre-print
CoNet enables dual knowledge transfer across domains by introducing cross connections from one base network to another and vice versa.  ...  Transfer learning is a class of algorithms underlying these techniques.  ...  Thus, the sparse target user-item interaction matrix can be reconstructed with the knowledge guidance from the source domain.  ... 
arXiv:1804.06769v2 fatcat:g5t3u3vxjbahbj7gpx2mwteh54

Personalized recommendation via cross-domain triadic factorization

Liang Hu, Jian Cao, Guandong Xu, Longbing Cao, Zhiping Gu, Can Zhu
2013 Proceedings of the 22nd international conference on World Wide Web - WWW '13  
In particular, we devise two CDTF algorithms to leverage user explicit and implicit feedbacks respectively, along with a genetic algorithm based weight parameters tuning algorithm to trade off influence  ...  Collaborative filtering (CF) is a major technique in recommender systems to help users find their potentially desired items.  ...  Hence, we devised an implicit feedback enhanced CDTF (CDTF-IF) model to deal with one-class feedbacks via confidence modeling.  ... 
doi:10.1145/2488388.2488441 dblp:conf/www/HuCXCGZ13 fatcat:a5iwil257vcuthzchh5bpdllem

Dual Relations Network for Collaborative Filtering

Daomin Ji, Zhenglong Xiang, Yuanxiang Li
2020 IEEE Access  
Collaborative filtering (CF) is one of the most effective and popular recommendation methods.  ...  Comprehensive experimental results on four real-world datasets demonstrate the effectiveness of our proposed model. INDEX TERMS Collaborative filtering, deep learning, recommender systems.  ...  PROBLEM STATEMENT Since implicit feedback data is more accessible and abundant than explicit feedback data in real world [31] , [32] , in this paper, we focus on recommendation with implicit feedback  ... 
doi:10.1109/access.2020.3002102 fatcat:nuvg5mv4frecnhwf22y2m3wojm

Advances in Collaborative Filtering and Ranking [article]

Liwei Wu
2020 arXiv   pre-print
both explicit and implicit feedback over pointwise and pairwise loss; chapter 5 is about the new regularization technique Stochastic Shared Embeddings (SSE) we proposed for embedding layers and how it  ...  In chapter 1, we give a brief introduction of the history and the current landscape of collaborative filtering and ranking; chapter 2 we first talk about pointwise collaborative filtering problem with  ...  We show that our Graph DNA encoding can be used with several collaborative filtering algorithms: graph-regularized matrix factorization with explicit and implicit feedback [89, 128] , co-factoring [67  ... 
arXiv:2002.12312v1 fatcat:eam7lntrrremlpgs4dcq427dm4

Song Recommendation with Non-Negative Matrix Factorization and Graph Total Variation [article]

Kirell Benzi, Vassilis Kalofolias, Xavier Bresson, Pierre Vandergheynst
2016 arXiv   pre-print
This work formulates a novel song recommender system as a matrix completion problem that benefits from collaborative filtering through Non-negative Matrix Factorization (NMF) and content-based filtering  ...  via total variation (TV) on graphs.  ...  Collaborative filtering models are known to perform better as more observed ratings are available [9] .  ... 
arXiv:1601.01892v2 fatcat:nfqecn7475h7ld6hzo5rvlkldi

Song recommendation with non-negative matrix factorization and graph total variation

Kirell Benzi, Vassiiis Kalofolias, Xavier Bresson, Pierre Vandergheynst
2016 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
This work formulates a novel song recommender system 1 as a matrix completion problem that benefits from collaborative filtering through Non-negative Matrix Factorization (NMF) and content-based filtering  ...  via total variation (TV) on graphs.  ...  Collaborative filtering models are known to perform better as more observed ratings are available [9] .  ... 
doi:10.1109/icassp.2016.7472115 dblp:conf/icassp/BenziKBV16 fatcat:x6ejpbojcvd3towlddywiivk2e

CATA++: A Collaborative Dual Attentive Autoencoder Method for Recommending Scientific Articles

Meshal Alfarhood, Jianlin Cheng
2020 IEEE Access  
Extensive experiments on three scientific-article datasets have shown that our dual-process learning strategy has significantly improved MF performance in comparison with other state-of-the-art MF-based  ...  Therefore, we introduce a Collaborative Dual Attentive Autoencoder (CATA++) method that utilizes an item's content and learns its latent space via two parallel autoencoders.  ...  Simultaneously, the recommendation problem with implicit feedback is typically defined as the following: r ij = 1, if user i interacts with item j 0, if otherwise (3) where the ones refer to positive (  ... 
doi:10.1109/access.2020.3029722 fatcat:avwhiea7cfgnjc5wm4k5ut2lvq

Collaborative Distillation for Top-N Recommendation [article]

Jae-woong Lee, Minjin Choi, Jongwuk Lee, Hyunjung Shim
2019 arXiv   pre-print
To address the issues, we propose a new KD model for the collaborative filtering approach, namely collaborative distillation (CD).  ...  associated with the top-N recommendation.  ...  Such ambiguity has been explicitly discussed in one-class collaborative filtering (OCCF) [12] - [19] .  ... 
arXiv:1911.05276v1 fatcat:bymyvyvsnvgo3ckeqcczgdw3ji

Collaborative Filtering with Topic and Social Latent Factors Incorporating Implicit Feedback

Guang-Neng HU, Xin-Yu Dai, Feng-Yu Qiu, Rui Xia, Tao Li, Shu-Jian Huang, Jia-Jun Chen
2018 ACM Transactions on Knowledge Discovery from Data  
Latent factors based collaborative filtering (CF) has become the popular approaches for RSs due to its accuracy and scalability.  ...  Second, we incorporate the implicit feedback from ratings into the proposed model to enhance its capability and to demonstrate its flexibility.  ...  Implicit feedback from ratings is also reviewed in the corresponding category. Collaborative Filtering.  ... 
doi:10.1145/3127873 fatcat:obkdtalzdrcvdnizhce3gwdy5u

Knowledge-Based Intelligent Education Recommendation System with IoT Networks

Xin Xin, Tianlei Shi, Mishal Sohail, Muhammad Arif
2022 Security and Communication Networks  
In addition, this paper constructs the framework of the intelligent education recommendation system with IoT networks based on the analysis of functional requirements.  ...  and decomposes the matrix with a higher dimension into several matrices with relatively small dimensions through matrix transformation.  ...  Acknowledgments is study was supported by the Educational Department of Jilin Province (the research of English online class of high vocational college on political character of curriculum) (2020ZCY349  ... 
doi:10.1155/2022/4140774 fatcat:l2vn3bijtneefpquhrgbuog6ba

A Unified Model for Recommendation with Selective Neighborhood Modeling [article]

Jingwei Ma and Jiahui Wen and Panpan Zhang and Guangda Zhang and Xue Li
2020 arXiv   pre-print
Neighborhood-based recommenders are a major class of Collaborative Filtering (CF) models.  ...  The confidence in the neighborhood is also addressed by putting higher weights on the neighborhood representations if we are confident with the neighborhood information, and vice versa.  ...  Neighborhood-based Recommendation Neighborhood-based approaches for recommendation is another major class of collaborative filtering.  ... 
arXiv:2010.08547v1 fatcat:ajekzn5ec5bcvatgz3nlk6p4um

Adaptive Information Filtering [chapter]

Yi Zhang
2009 Text Mining  
Implicit feedback has also been explored for the task of filtering [54] [16] [53] [56] [89] . [54] suggested a list of potential implicit feedbacks.  ...  Adaptive filtering is extremely useful for handling new documents/items with little or no user feedback, while collaborative filtering leverages information from other users with similar tastes and preferences  ... 
doi:10.1201/9781420059458.ch8 fatcat:mokfgfbh4neerl4b6bo3oickja
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