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Filters
A Joint Framework for Collaborative Filtering and Metric Learning
[chapter]
2016
Lecture Notes in Computer Science
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We have developed a framework for jointly conducting collaborative filtering and distance metric learning based on regularized singular value decomposition (RSVD), which discovers the user matrix and item ...
Experimental results show that our framework achieves a promising prediction performance and outperforms the existing works. ...
The work described in this paper is substantially supported by grants from the Education University of Hong Kong (Project Codes: RG 30/2014-2015R and RG 18/2015-2016R). ...
doi:10.1007/978-3-319-48051-0_14
fatcat:ntkqzhjjvva45ldymz77d6wivu
A joint framework for collaborative and content filtering
2004
Proceedings of the 27th annual international conference on Research and development in information retrieval - SIGIR '04
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This paper proposes a novel, unified, and systematic approach to combine collaborative and content-based filtering for ranking and user preference prediction. ...
The framework incorporates all available information by coupling together multiple learning problems and using a suitable kernel or similarity function between user-item pairs. ...
We pursue the philosophy that collaborative and content-based filtering are complementary views that should be unified in a common learning architecture. ...
doi:10.1145/1008992.1009115
dblp:conf/sigir/BasilicoH04
fatcat:4a7kvfj3czdy5obbxcbjnbbt4m
Collaborative Multi-agent Learning for MR Knee Articular Cartilage Segmentation
[article]
2019
arXiv
pre-print
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In this paper, we propose a novel framework to solve the MR knee cartilage segmentation task. The key contribution is the adversarial learning based collaborative multi-agent segmentation network. ...
The collaborative learning is driven by an adversarial sub-network. ...
The second term L m = mce [F(A f , A t , A p ), y i ] and the third one are applied on the fused multicartilage mask for joint-label learning. ...
arXiv:1908.04469v1
fatcat:gwlpezkk3vfehkwbzuzzbwnlsm
Criterion-based Heterogeneous Collaborative Filtering for Multi-behavior Implicit Recommendation
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2105.11876v1 fatcat:gb3t3z2u7rg2pbnyg6ke4du6wy
To tackle the above issue, we propose a novel non-sampling learning framework named Criterion-guided Heterogeneous Collaborative Filtering (CHCF). ...
We further theoretically demonstrate that the optimization of Collaborative Metric Learning can be approximately achieved by CHCF learning framework in a non-sampling form effectively. ...
of Collaborative Metric Learning can be approximately achieved by the CHCF learning framework in a non-sampling form effectively. ...
arXiv:2105.11876v2
fatcat:ynvqa25biray3gioatrh2h6usi
How Can High-Frequency Sensors Capture Collaboration? A Review of the Empirical Links between Multimodal Metrics and Collaborative Constructs
2021
Sensors
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of collaborative constructs (e.g., group performance, conditions for effective collaboration); (3) the connections found by researchers between sensor-based metrics and outcomes; and (4) how theory was ...
An added contribution is an interactive online visualization where researchers can explore collaborative sensor-based metrics, collaborative constructs, and how the two are connected. ...
This framework was useful for papers that attempted to combine multiple data sources and collaborative factors. ...
doi:10.3390/s21248185
pmid:34960278
pmcid:PMC8706197
fatcat:puclqibmhvew7aadu3xwgse2ly
Feature and Instance Joint Selection: A Reinforcement Learning Perspective
[article]
2022
arXiv
pre-print
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To address this challenge, we propose a reinforcement learning solution to accomplish the joint selection task and simultaneously capture the interaction between the selection of each feature and each ...
In particular, a sequential-scanning mechanism is designed as action strategy of agents, and a collaborative-changing environment is used to enhance agent collaboration. ...
We formulate the joint selection into a reinforcement learning framework with the tailor-designed sequentialscanning mechanism and collaboratively-changing environment, in order to simulate fine-grained ...
arXiv:2205.07867v1
fatcat:ymyhacnjijhptiw6lebkncbi2q
Recommendation with Multi-Source Heterogeneous Information
2018
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence
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Deep Network Embedding (CDNE for short) is proposed to learn different latent representations. ...
To model the multi-source heterogeneous information, we use two coupled neural networks to capture the deep network representations of items, based on which a new recommendation model Collaborative multi-source ...
Acknowledgments We would like to thank the anonymous reviewers for their valuable comments and suggestions. This work was supported by the National Key ...
doi:10.24963/ijcai.2018/469
dblp:conf/ijcai/GaoYWZLH18
fatcat:63fvrois7ffsfk2ojwp3yeymly
Leveraging Cross Feedback of User and Item Embeddings with Attention for Variational Autoencoder based Collaborative Filtering
[article]
2020
arXiv
pre-print
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Based on the ELBOs, we propose a VAE-based Bayesian MF framework. It leverages not only the data but also the embedding information to approximate the user-item joint distribution. ...
Matrix factorization (MF) has been widely applied to collaborative filtering in recommendation systems. ...
Conclusion In this paper, we propose a variational auto-encoder based Bayesian matrix factorization framework for collaborative filtering. ...
arXiv:2002.09145v2
fatcat:tuqkehg3f5d6decwz2nzpcrxmm
ZeroMat: Solving Cold-start Problem of Recommender System with No Input Data
[article]
2021
arXiv
pre-print
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2021
2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE)
unpublished
doi:10.1109/iciscae52414.2021.9590668 fatcat:y7un2nwozjgq7d2hehjvsyy5fy
doi:10.1109/iciscae52414.2021.9590668 fatcat:y7un2nwozjgq7d2hehjvsyy5fy
In this paper, we propose a new technique called ZeroMat that requries no input data at all and predicts the user item rating data that is competitive in Mean Absolute Error and fairness metric compared ...
However, nearly all recommender system faces a challenge called the cold-start problem. ...
SVDFeature：A
Toolkit for Feature-based Collaborative Filtering. The
Journal of Machine Learning Research, 2012.
[3] Y. Koren. ...
arXiv:2112.03084v1
fatcat:cavphgbzpneovomnizbbnsimre
Incorporating Social-Media to Enhance Collaboration in Online Learning
2022
Journal of Computer Science and Technology Studies
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Technology is advancing at a rapid pace, opening up new possibilities for learning. ...
This integration is especially important at stressful and difficult times, such as the Covid-19 period, which saw learning come to a halt for more than six months. ...
Sites such as Gaggle.net have provided extended collaborative features for enabling the incorporation of social media for joint learning. ...
doi:10.32996/jcsts.2022.4.1.3
fatcat:f4kbv3dij5bjxak4bmpmj6z2jm
Generative Collaborative Networks for Single Image Super-Resolution
[article]
2019
arXiv
pre-print
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The two networks (generator and extractor) are collaborative in the sense that the latter "helps" the former, by constructing discriminative and relevant features (not necessarily fixed and possibly learned ...
In this paper, we present a general framework named Generative Collaborative Networks (GCN), where the idea consists in optimizing the generator (the mapping of interest) in the feature space of a features ...
P/adv and P/adv,rec) and a particular learning strategies (joint-learning or disjoint-learning). ...
arXiv:1902.10467v2
fatcat:go526327avbldp5bnbdtdpxsyq
LCMR: Local and Centralized Memories for Collaborative Filtering with Unstructured Text
[article]
2018
arXiv
pre-print
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Collaborative filtering (CF) is the key technique for recommender systems. ...
In this paper, we develop a unified neural framework to exploit interaction data and content information seamlessly. ...
Conclusion We proposed a novel neural architecture, LCMR, to jointly model user-item interactions and integrate unstructured text for collaborative filtering with implicit feedback. ...
arXiv:1804.06201v2
fatcat:a2iemo2aenc2toqhj65j7n6hai
Joint Variational Autoencoders for Recommendation with Implicit Feedback
[article]
2020
arXiv
pre-print
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Our extensive experiments on several real-world datasets show that JoVA-Hinge outperforms a broad set of state-of-the-art collaborative filtering methods, under a variety of commonly-used metrics. ...
We introduce joint variational autoencoders (JoVA), an ensemble of two VAEs, in which VAEs jointly learn both user and item representations and collectively reconstruct and predict user preferences. ...
Similar to ensemble learning, JoVA combines user VAE and item VAE into one learning framework for the final prediction. ...
arXiv:2008.07577v1
fatcat:imcjmb4kmza7fbrt2w7iukkrx4
Collaborative Generative Hashing for Marketing and Fast Cold-start Recommendation
[article]
2020
arXiv
pre-print
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To this end, we propose a collaborative generated hashing (CGH) framework to improve the efficiency by denoting users and items as binary codes, then fast hashing search techniques can be used to speed ...
to learn compact and informative binary codes. ...
as discrete collaborative filtering (DCF) [12] and block-wise learning [13] , [14] . ...
arXiv:2011.00953v1
fatcat:l643dlp2g5gsve2pv4loir45ru
Joint Modeling and Optimization of Search and Recommendation
[article]
2018
arXiv
pre-print
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We propose a general framework that simultaneously learns a retrieval model and a recommendation model by optimizing a joint loss function. ...
In this paper, we present theoretical and practical reasons to motivate joint modeling of search and recommendation as a research direction. ...
The authors thank Qingyao Ai, John Foley, Helia Hashemi, and Ali Montazeralghaem for their insightful comments. ...
arXiv:1807.05631v1
fatcat:dyshotqo4ngcvbcou5tlxgmhyy
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