Filters








42,150 Hits in 7.0 sec

Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect

Zheni Zeng, Chaojun Xiao, Yuan Yao, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun
2021 Frontiers in Big Data  
In this survey, we first provide a review of recommender systems with pre-training. In addition, we show the benefits of pre-training to recommender systems through experiments.  ...  Recently pre-trained models have shown their effectiveness in knowledge transfer between domains and tasks, which can potentially alleviate the data sparsity problem in recommender systems.  ...  Similar to the KG-based recommendation, many social recommender systems seek to integrate the pre-trained social network embeddings, which indicates the degree that a user is influenced by his/ her friends  ... 
doi:10.3389/fdata.2021.602071 pmid:33817631 pmcid:PMC8013982 fatcat:oz2da4xwz5ad3meqciozon2teq

Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect [article]

Zheni Zeng, Chaojun Xiao, Yuan Yao, Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin, Maosong Sun
2020 arXiv   pre-print
Finally, we discuss several promising directions for future research for recommender systems with pre-training.  ...  In this survey, we first provide a review of recommender systems with pre-training. In addition, we show the benefits of pre-training to recommender systems through experiments.  ...  Similar to the KG-based recommendation, many social recommender systems seek to integrate the pre-trained social network embeddings, which indicates the degree that a user is influenced by his/her friends  ... 
arXiv:2009.09226v1 fatcat:zgvbm4mqnbeq5d4wpl4x5jnf6e

Heterogeneous Edge Embeddings for Friend Recommendation [article]

Janu Verma, Srishti Gupta, Debdoot Mukherjee, Tanmoy Chakraborty
2019 arXiv   pre-print
We propose a friend recommendation system (an application of link prediction) using edge embeddings on social networks.  ...  We evaluate our model on a real-world, active social network where this system is deployed for friend recommendation for millions of users.  ...  We employ the pre-July network comprising of 3.3 million nodes and 32 million edges for training network embedding.  ... 
arXiv:1902.03124v1 fatcat:pp56atx43zfmbmmv4mdngvwy7q

A Deep Multimodal Approach for Cold-start Music Recommendation

Sergio Oramas, Oriol Nieto, Mohamed Sordo, Xavier Serra
2017 Proceedings of the 2nd Workshop on Deep Learning for Recommender Systems - DLRS 2017  
Second, track embeddings are learned from the audio signal and available feedback data. Finally, artist and track embeddings are combined in a multimodal network.  ...  Music streaming services often ingest all available music, but this poses a challenge: how to recommend new artists for which prior knowledge is scarce?  ...  We initialize the input embedding layer of the network with word2vec word embeddings pre-trained on the Google News dataset, and also with word embeddings trained in our own corpus of biographies.  ... 
doi:10.1145/3125486.3125492 dblp:conf/recsys/OramasNSS17 fatcat:srv74edtavccnp46cqxgjptuxm

A Light Heterogeneous Graph Collaborative Filtering Model using Textual Information [article]

Chaoyang Wang, Zhiqiang Guo, Guohui Li, Jianjun Li, Peng Pan, Ke Liu
2021 arXiv   pre-print
Specifically, to incorporate rich textual knowledge, we utilize a pre-trained NLP model to initialize the embeddings of text nodes.  ...  Due to the development of graph neural networks, graph-based representation learning methods have made great progress in recommender systems.  ...  Some researchers have applied these pre-trained NLP models in citation recommendations [38, 39] . Hebatallah et al.  ... 
arXiv:2010.07027v4 fatcat:syx5nj626banlo6f7bk2tye7r4

A Joint Deep Recommendation Framework for Location-Based Social Networks

Omer Tal, Yang Liu
2019 Complexity  
In this work we propose Textual and Contextual Embedding-based Neural Recommender (TCENR), a deep framework that employs contextual data, such as users' social networks and locations' geo-spatial data,  ...  In addition, we provide further insight into the design selections and hyperparameters of our recommender system, hoping to shed light on the benefit of deep learning for location-based social network  ...  Acknowledgments This work was supported in part by the National Natural Science Foundation of China Grant (61572289) and NSERC Discovery Grants.  ... 
doi:10.1155/2019/2926749 fatcat:hmdfns6pfrajjkgvm2aj6r3uwu

Multi-Auxiliary Augmented Collaborative Variational Auto-encoder for Tag Recommendation [article]

Jing Yi, Xubin Ren, Zhenzhong Chen
2022 arXiv   pre-print
In addition, an inductive variational graph auto-encoder is designed where new item nodes could be inferred in the test phase, such that item social embeddings could be exploited for new items.  ...  losses are added in the training phase to constrict the generation for feedback predictions via different auxiliary embeddings.  ...  We set the learn-ing_rate of pre-trained VAE, pre-trained VGAE, and pre-trained Mult-VAE to be 0.001, empirically.  ... 
arXiv:2204.09422v1 fatcat:zozcuc526fbjxp4d5t7kah3t24

Hierarchical Hyperedge Embedding-Based Representation Learning for Group Recommendation

Lei Guo, Hongzhi Yin, Tong Chen, Xiangliang Zhang, Kai Zheng
2022 ACM Transactions on Information Systems (TOIS; Formerly: ACM Transactions on Office Information Systems)  
In this work, we study group recommendation in a particular scenario, namely occasional group recommendation, where groups are formed ad hoc and users may just constitute a group for the first time—that  ...  In this work, we focus on addressing the aforementioned challenges in the group representation learning task, and devise a hierarchical hyperedge embedding-based group recommender, namely HyperGroup.  ...  ., friends) in a social network [23, 40] .  ... 
doi:10.1145/3457949 fatcat:cjrlpr7gvjc23jjimocmn6dkfm

Hyperbolic Hypergraphs for Sequential Recommendation [article]

Yicong Li, Hongxu Chen, Xiangguo Sun, Zhenchao Sun, Lin Li, Lizhen Cui, Philip S. Yu, Guandong Xu
2021 arXiv   pre-print
Specifically, we design three self-supervised tasks to obtain the pre-training item embeddings to feed or fuse into the following recommendation architecture (with two ways to use the pre-trained embeddings  ...  (H2SeqRec) with pre-training phase.  ...  [43] design a multi-channel hypergraph convolutional network to enhance social recommendation by exploiting high-order user relations, which shows great improvement.  ... 
arXiv:2108.08134v1 fatcat:zifmaxbgovdffazhmeyop447y4

Image Recommendation With Reciprocal Social Influence

Yuan Meng, Chunyan Han, Yongfeng Zhang, Yanjie Li, Guibing Guo
2019 IEEE Access  
In this paper, we propose a deep neural network for image recommendation (dubbed RSIM) by leveraging reciprocal social influence, and optimize the preferences of users and friends simultaneously.  ...  Image recommendation plays an important role for exploring user potential interests in largescale image sharing websites (e.g., Flickr and Instagram).  ...  We designed a deep neural network called RSIM to exploit the reciprocal social influence for recommendations.  ... 
doi:10.1109/access.2019.2939403 fatcat:loklgbbntnexphzgmwfpnv5ave

Graph Neural Networks for Social Recommendation [article]

Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin
2019 arXiv   pre-print
To address the three aforementioned challenges simultaneously, in this paper, we present a novel graph neural network framework (GraphRec) for social recommendations.  ...  These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning  ...  ACKNOWLEDGMENTS The work described in this paper has been supported, in part, by a general research fund from the Hong Kong Research Grants Council  ... 
arXiv:1902.07243v2 fatcat:demjqw6ptvhcrkbkks7xlkg2wy

Graph Embedding for Scholar Recommendation in Academic Social Networks

Chengzhe Yuan, Yi He, Ronghua Lin, Yong Tang
2021 Frontiers in Physics  
Different from friend recommendation in conventional social networks, scholar recommendation in ASNs usually involves different academic entities (e.g., scholars, scientific publications, and status updates  ...  The academic social networks (ASNs) play an important role in promoting scientific collaboration and innovation in academic society.  ...  Graph embedding has been exploited in heterogeneous information networks for various recommendation scenarios, such as “co-author recommendation,” “social recommendation,” and “movie recommendation” [13  ... 
doi:10.3389/fphy.2021.768006 fatcat:qjmv3eyvmncktdqoid5vhldke4

UPRec: User-Aware Pre-training for Recommender Systems [article]

Chaojun Xiao, Ruobing Xie, Yuan Yao, Zhiyuan Liu, Maosong Sun, Xu Zhang, Leyu Lin
2021 arXiv   pre-print
In this paper, we propose a method to enhance pre-trained models with heterogeneous user information, called User-aware Pre-training for Recommendation (UPRec).  ...  Specifically, UPRec leverages the user attributes andstructured social graphs to construct self-supervised objectives in the pre-training stage and proposes two user-aware pre-training tasks.  ...  network embeddings for social graphs [56] , [57] .  ... 
arXiv:2102.10989v1 fatcat:fsur7dod6vcurlauxqxtlkbosi

Embedding Taxonomical, Situational or Sequential Knowledge Graph Context for Recommendation Tasks [chapter]

Simon Werner, Achim Rettinger, Lavdim Halilaj, Jürgen Lüttin
2021 Applications and Practices in Ontology Design, Extraction, and Reasoning  
In this paper, we investigate how well state-of-the-art approaches do exploit those different dimensions relevant to POI recommendation tasks.  ...  Naturally, we represent such a scenario as a temporal knowledge graph and compare plain knowledge graph, a taxonomy and a hypergraph embedding approach, as well as a recurrent neural network architecture  ...  An early approach for POI recommendation based on models for human mobility and their dynamics in social networks is described in [6] .  ... 
doi:10.3233/ssw210046 fatcat:rfsad4zo7zhybdjloyor4zjczu

Graph Neural Networks for Social Recommendation

Wenqi Fan, Yao Ma, Qing Li, Yuan He, Eric Zhao, Jiliang Tang, Dawei Yin
2019 The World Wide Web Conference on - WWW '19  
To address the three aforementioned challenges simultaneously, in this paper, we present a novel graph neural network framework (GraphRec) for social recommendations.  ...  These advantages of GNNs provide great potential to advance social recommendation since data in social recommender systems can be represented as user-user social graph and user-item graph; and learning  ...  ACKNOWLEDGMENTS The work described in this paper has been supported, in part, by a general research fund from the Hong Kong Research Grants Council  ... 
doi:10.1145/3308558.3313488 dblp:conf/www/Fan0LHZTY19 fatcat:t56gqjjtnnbvdgno5eu5eh4yka
« Previous Showing results 1 — 15 out of 42,150 results