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Recommendation in heterogeneous information network via dual similarity regularization
2016
International Journal of Data Science and Analytics
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The social recommendation methods tend to leverage social relations among users obtained from social network to alleviate data sparsity and cold-start problems in recommender systems. ...
With the dual similarity regularization, we further propose an optimization function to integrate the similarity information of users and items under different semantic meta-paths, and a gradient descend ...
[26] integrates heterogeneous information via flexible regularization of users and items for better recommendation. ...
doi:10.1007/s41060-016-0031-0
dblp:journals/ijdsa/ZhengLSZLW17
fatcat:o2wce62dyrhxxmd7xbbqpfaq5m
Dual Similarity Regularization for Recommendation
[chapter]
2016
Lecture Notes in Computer Science
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The social recommendation methods usually employ simple similarity information of users as social regularization on users. ...
In order to overcome the shortcomings of social regularization, we propose a new dual similarity regularization to impose the constraint on users and items with high and low similarities simultaneously ...
However, rich similarity information on users and items can be generated in a heterogeneous information network. ...
doi:10.1007/978-3-319-31750-2_43
fatcat:hxcbsvtufjfabpgiqfcf43pccy
Integrating Heterogeneous Information via Flexible Regularization Framework for Recommendation
[article]
2015
arXiv
pre-print
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In this paper, we organize objects and relations in recommendation system as a heterogeneous information network, and introduce meta path based similarity measure to evaluate the similarity of users or ...
Furthermore, a matrix factorization based dual regularization framework SimMF is proposed to flexibly integrate different types of information through adopting the similarity of users and items as regularization ...
CONCLUSION In this paper, we organize the objects and relations in recommendation system as a heterogeneous information network, and designed a unified and flexible matrix factorization based dual regularization ...
arXiv:1511.03759v1
fatcat:nlx76g3revdeviz6r2o7brvpye
Heterogeneous Information Network Embedding for Recommendation
[article]
2017
arXiv
pre-print
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Due to the flexibility in modelling data heterogeneity, heterogeneous information network (HIN) has been adopted to characterize complex and heterogeneous auxiliary data in recommender systems, called ...
In this paper, we propose a novel heterogeneous network embedding based approach for HIN based recommendation, called HERec. ...
. • DSR [12] : It is a MF based recommendation method with dual similarity regularization, which imposes the constraint on users and items with high and low similarities simultaneously. ...
arXiv:1711.10730v1
fatcat:g3z5i6gnd5aljeyscma2cco64m
RDF-to-Text Generation with Graph-augmented Structural Neural Encoders
2020
Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence
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To address these issues, we propose to jointly learn local and global structure information via combining two new graph-augmented structural neural encoders (i.e., a bidirectional graph encoder and a bidirectional ...
However, none of these methods can explicitly model both local and global structure information between and within the triples. ...
In contrast, the novel dual-target CDR has been recently proposed to improve the recommendation accuracies on both richer and sparser domains simultaneously by making good use of the information or knowledge ...
doi:10.24963/ijcai.2020/415
dblp:conf/ijcai/ZhuWCLZ20
fatcat:bfw4nsudpjbvlnbpjdyssk3mla
Dual Graph Attention Networks for Deep Latent Representation of Multifaceted Social Effects in Recommender Systems
2019
The World Wide Web Conference on - WWW '19
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2019 pre-print arXiv:1903.10433v1 fatcat:czqp43w2xfaetd34fczljc6ln4
Social recommendation leverages social information to solve data sparsity and cold-start problems in traditional collaborative filtering methods. ...
To relax this strong assumption, in this paper, we propose dual graph attention networks to collaboratively learn representations for two-fold social effects, where one is modeled by a user-specific attention ...
We are the rst to use GAT for social recommendation task, and our new architecture, dual GATs, can capture social information in both user and item networks. ...
doi:10.1145/3308558.3313442
dblp:conf/www/WuZGHWGC19
fatcat:2ciwch3szng63lobo5ug2ac55a
Recent Advances in Heterogeneous Relation Learning for Recommendation
[article]
2021
arXiv
pre-print
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We discuss the learning approaches in each category, such as matrix factorization, attention mechanism and graph neural networks, for effectively distilling heterogeneous contextual information. ...
Recommender systems have played a critical role in many web applications to meet user's personalized interests and alleviate the information overload. ...
(2) How to perform the information fusion based on the extracted knowledge via automatic machine learning frameworks, and endow the user preference modeling paradigms with heterogeneous context incorporation ...
arXiv:2110.03455v1
fatcat:fskj4qdsibfnxefklazdli3tgu
Self-supervised Learning for Large-scale Item Recommendations
[article]
2021
arXiv
pre-print
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Large scale recommender models find most relevant items from huge catalogs, and they play a critical role in modern search and recommendation systems. ...
To model the input space with large-vocab categorical features, a typical recommender model learns a joint embedding space through neural networks for both queries and items from user feedback data. ...
The goal is to recommend highly similar apps given a seed app. This is also formulated as an item-to-item recommendation problem via a multi-class classification loss. ...
arXiv:2007.12865v4
fatcat:euu7phtharckdbwki3cfceqmq4
DDTCDR: Deep Dual Transfer Cross Domain Recommendation
[article]
2019
arXiv
pre-print
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To address these concerns, in this paper we propose a novel approach to cross-domain recommendations based on the mechanism of dual learning that transfers information between two related domains in an ...
Combining with autoencoder approach to extract the latent essence of feature information, we propose Deep Dual Transfer Cross Domain Recommendation (DDTCDR) model to provide recommendations in respective ...
and Dual Regularization [36] . ...
arXiv:1910.05189v1
fatcat:y5mqqv3gebgqbgakxk4qzubgmq
Social Role Identification via Dual Uncertainty Minimization Regularization
2014
2014 IEEE International Conference on Data Mining
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Realizing the natural setting of social nodes associated with dual view information, i.e., the local node characteristics and the global network influence, we present a novel model that explores graph ...
regularization techniques and integrates such information to achieve improved prediction performance. ...
Motivated by multi-view learning idea [9] , [10] , here we propose a graph regularization based learning framework that integrates heterogeneous information. ...
doi:10.1109/icdm.2014.31
dblp:conf/icdm/ChengACLZ14
fatcat:xsjzh26hrrhgnpwk3rqw4wfnxm
A Unified Framework for Cross-Domain and Cross-System Recommendations
[article]
2021
arXiv
pre-print
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In this paper, we focus on three new scenarios, i.e., Dual-Target CDR (DTCDR), Multi-Target CDR (MTCDR), and CDR+CSR, and aim to improve the recommendation accuracy in all datasets simultaneously for all ...
In GA, we first construct separate heterogeneous graphs to generate more representative user and item embeddings. ...
Then, we leverage rating and content information of each domain to construct a heterogeneous graph, representing user-item interaction relationships, user-user similarity relationships, and itemitem similarity ...
arXiv:2108.07976v1
fatcat:gfie4f5b4ncuvotz7wiqlhaice
A Comprehensive Survey on Community Detection with Deep Learning
[article]
2021
arXiv
pre-print
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in handling high dimensional network data. ...
A community reveals the features and connections of its members that are different from those in other communities in a network. Detecting communities is of great significance in network analysis. ...
To imbalanced communities, Dual-Regularized Graph Convolutional Networks (DR-GCN) [95] utilizes a conditional GAN into the dual-regularized GCN model, i.e., a latent distribution alignment regularization ...
arXiv:2105.12584v2
fatcat:matipshxnzcdloygrcrwx2sxr4
ICDE conference 2015 detailed author index
2015
2015 IEEE 31st International Conference on Data Engineering
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, and Visualizing
Geotagged Microblogs
Muthulingam, Sujatha
1253
Oracle Database In-Memory: A Dual Format In-Memory Database
Mylopoulos, John
1538
Goals in Social Media, Information Retrieval ...
Users in Social Networks with Limited Information Vijayarajendran, Priya 1400 Advanced Analytics on SAP HANA: Churn Risk Scoring Using Call Network Analysis Voigt, Hannes 1460 Enjoy FRDM -Play with a ...
doi:10.1109/icde.2015.7113260
fatcat:ep7pomkm55f45j33tkpoc5asim
Hybrid social media network
2012
Proceedings of the 20th ACM international conference on Multimedia - MM '12
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However, there are many heterogeneous entities and relations in such networks, making it difficult to fully represent and exploit the diverse array of information. ...
The network can be used to generate personalized information recommendation in response to specific targets of interests, e.g., personalized multimedia albums, target advertisement and friend/topic recommendation ...
The heterogeneous information network [10, 12] in the data mining community also attempts to model the relations between heterogeneous entities. ...
doi:10.1145/2393347.2393438
dblp:conf/mm/LiuYCYC12
fatcat:gncqk24wsjewpiqi5og5rqgp6q
DyDiff-VAE: A Dynamic Variational Framework for Information Diffusion Prediction
[article]
2021
arXiv
pre-print
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similar interests. ...
Inferring user interests from diffusion data lies the foundation of diffusion prediction, because users often forward the information in which they are interested or the information from those who share ...
ACKNOWLEDGEMENTS This work was funded in part by DARPA under award W911NF-17-C-0099, and by DoD Basic Research Office under award HQ00342110002. ...
arXiv:2106.03251v1
fatcat:forh47cfcnhb3dlk44wq4mqr2u
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