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Learning to Transfer Graph Embeddings for Inductive Graph based Recommendation [article]

Le Wu, Yonghui Yang, Lei Chen, Defu Lian, Richang Hong, Meng Wang
<span title="2020-05-24">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we propose an inductive Graph based Transfer learning framework for personalized video highlight Recommendation (TransGRec).  ...  The transfer network is designed to approximate the learned item embeddings from graph neural networks by taking each item's visual content as input, in order to tackle the new segment problem in the test  ...  To this end, we propose a general framework: an inductive Graph based Transfer learning framework for personalized video highlight Recommendation (TransGRec).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.11724v1">arXiv:2005.11724v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rk5hqnj7fvbh7nzohte5ufajpe">fatcat:rk5hqnj7fvbh7nzohte5ufajpe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200904201758/https://arxiv.org/pdf/2005.11724v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/fb/47/fb473dc961acb1fccc85693ffa55791151cb0220.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2005.11724v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

RetaGNN: Relational Temporal Attentive Graph Neural Networks for Holistic Sequential Recommendation [article]

Cheng Hsu, Cheng-Te Li
<span title="2021-01-29">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Sequential recommendation (SR) is to accurately recommend a list of items for a user based on her current accessed ones.  ...  We propose a novel deep learning-based model, Relational Temporal Attentive Graph Neural Networks (RetaGNN), for holistic SR. The main idea of RetaGNN is three-fold.  ...  The reason is for inductive and transferable learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2101.12457v1">arXiv:2101.12457v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/i4pdzfjtifeq3g4mxz3b2e3se4">fatcat:i4pdzfjtifeq3g4mxz3b2e3se4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210202211203/https://arxiv.org/pdf/2101.12457v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e7/92/e79252dae7d8b666a7ad3630e783a207eebf83aa.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2101.12457v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Inductive Matrix Completion Based on Graph Neural Networks [article]

Muhan Zhang, Yixin Chen
<span title="2020-02-16">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we propose an Inductive Graph-based Matrix Completion (IGMC) model to address this problem.  ...  learned embeddings cannot generalize to unseen rows/columns or to new matrices.  ...  TRANSFER LEARNING A great advantage of an inductive model is its potential for transferring to other tasks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1904.12058v3">arXiv:1904.12058v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/bodp3t4hencl5fjck4ykj5yflu">fatcat:bodp3t4hencl5fjck4ykj5yflu</a> </span>
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Syndrome-aware Herb Recommendation with Multi-Graph Convolution Network [article]

Yuanyuan Jin, Wei Zhang, Xiangnan He, Xinyu Wang, Xiaoling Wang
<span title="2020-02-20">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
(GCNs) on both symptom-symptom and symptom-herb graphs to learn symptom embedding.  ...  Towards symptom embedding learning, we additionally construct a symptom-symptom graph from the input prescriptions for capturing the relations between symptoms; we then build graph convolution networks  ...  Graph Based Herb Recommendation. A graph is an effective tool to model complex relation data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.08575v1">arXiv:2002.08575v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cpes5orbcjdc7f6ngcvpuggzwy">fatcat:cpes5orbcjdc7f6ngcvpuggzwy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200321163059/https://arxiv.org/pdf/2002.08575v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.08575v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding [article]

Mingyang Chen, Wen Zhang, Yushan Zhu, Hongting Zhou, Zonggang Yuan, Changliang Xu, Huajun Chen
<span title="2022-05-06">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, to achieve inductive knowledge graph embedding, we propose a model MorsE, which does not learn embeddings for entities but learns transferable meta-knowledge that can be used to produce  ...  Knowledge graphs (KGs) consisting of a large number of triples have become widespread recently, and many knowledge graph embedding (KGE) methods are proposed to embed entities and relations of a KG into  ...  Inspired by this, in our paper, we propose a novel model, Meta-Knowledge Transfer for Inductive Knowledge Graph Embedding (MorsE), which can produce high-quality embeddings for new entities in the inductive  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.14170v3">arXiv:2110.14170v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tuarmvyiybebrnk3qphp55bfze">fatcat:tuarmvyiybebrnk3qphp55bfze</a> </span>
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A review of recommendation system research based on bipartite graph

Ziteng Wu, Chengyun Song, Yunqing Chen, Lingxuan Li, I. Barukčić
<span title="">2021</span> <i title="EDP Sciences"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4vlgvitw6fcmbay5hkyo2s2ime" style="color: black;">MATEC Web of Conferences</a> </i> &nbsp;
The whole paper is based on the bipartite graph.  ...  Neural network recommendation based on the user-item bipartite graph has a significant effect on alleviating the long-standing data sparseness and cold start of the recommendation system.  ...  Graph neural network is a deep learning-based method running on the graph domain, which makes up for the problem that traditional deep models cannot generalize to graph data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1051/matecconf/202133605010">doi:10.1051/matecconf/202133605010</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qyiiqjnev5eublqlmps4gcworq">fatcat:qyiiqjnev5eublqlmps4gcworq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210428020102/https://www.matec-conferences.org/articles/matecconf/pdf/2021/05/matecconf_cscns20_05010.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/71/78/71784cb64786aed89f5026657a7bbf243e2a3f4f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1051/matecconf/202133605010"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

SUGER: A Subgraph-based Graph Convolutional Network Method for Bundle Recommendation [article]

Zhenning Zhang, Boxin Du, Hanghang Tong
<span title="2022-05-05">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this work, we propose a subgraph-based Graph Neural Network model, SUGER, for bundle recommendation to handle these limitations.  ...  Bundle recommendation is an emerging research direction in the recommender system with the focus on recommending customized bundles of items for users.  ...  For the transfer learning problem, it might not be reasonable for encoders to learn the whole graph pattern dependent on the dataset domain.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.11231v1">arXiv:2205.11231v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/222frruovzci3ihhmkqbywaxsi">fatcat:222frruovzci3ihhmkqbywaxsi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220526222033/https://arxiv.org/pdf/2205.11231v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/83/23/832363b683229fcdb48d2b5127dc1a2de5ce857f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.11231v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Framework for Generalizing Graph-based Representation Learning Methods [article]

Nesreen K. Ahmed, Ryan A. Rossi, Rong Zhou, John Boaz Lee, Xiangnan Kong, Theodore L. Willke, Hoda Eldardiry
<span title="2017-09-14">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Our proposed framework enables these methods to be more widely applicable for both transductive and inductive learning as well as for use on graphs with attributes (if available).  ...  ., the features resulting from these methods are unable to transfer to new nodes and graphs as they are tied to node identity.  ...  Second, the features learned generalize to new nodes and across graphs and therefore are naturally inductive and able to be used for transfer learning tasks.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1709.04596v1">arXiv:1709.04596v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zmiuszcanzedjloacscgqjv4ey">fatcat:zmiuszcanzedjloacscgqjv4ey</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200912183205/https://arxiv.org/pdf/1709.04596v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4d/23/4d23e94d9c56ce2c7ae7adb105f49b2c51b055a5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1709.04596v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Hierarchical BiGraph Neural Network as Recommendation Systems [article]

Dom Huh
<span title="2020-07-27">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
These datasets intuitively can be mapped to and represented as networks or graphs.  ...  Graph neural networks emerge as a promising modeling method for applications dealing with datasets that are best represented in the graph domain.  ...  The suffix * denotes transfer learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.16000v1">arXiv:2007.16000v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ppy7s2367zfa3mxpqhajimwpum">fatcat:ppy7s2367zfa3mxpqhajimwpum</a> </span>
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Few-Shot Learning on Graphs: A Survey [article]

Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu
<span title="2022-03-17">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In light of this, few-shot learning on graphs (FSLG), which combines the strengths of graph representation learning and few-shot learning together, has been proposed to tackle the performance degradation  ...  Graph representation learning has attracted tremendous attention due to its remarkable performance in many real-world applications.  ...  To be specific, ON-FSL utilizes a GNN encoder to learn node embedding for node classification.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.09308v1">arXiv:2203.09308v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7tpke435jnevdhdverovyug4sa">fatcat:7tpke435jnevdhdverovyug4sa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220320105117/https://arxiv.org/pdf/2203.09308v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a5/97/a5974963ae26116346907046031375b1ea2b827a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2203.09308v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Towards Open-World Recommendation: An Inductive Model-based Collaborative Filtering Approach [article]

Qitian Wu, Hengrui Zhang, Xiaofeng Gao, Junchi Yan, Hongyuan Zha
<span title="2022-03-07">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The second model resorts to attention-based structure learning that estimates hidden relations from query to key users and learns to leverage meta latents to inductively compute embeddings for query users  ...  However, the user-specific embedding factors can only be learned in a transductive way, making it difficult to handle new users on-the-fly.  ...  Acknowledgement We would like to thank the anonymous reviewers for their valuable feedbacks and suggestions that help to improve this work.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2007.04833v3">arXiv:2007.04833v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/eit4thormfdtdkyhr3hwksitgq">fatcat:eit4thormfdtdkyhr3hwksitgq</a> </span>
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Communicative Subgraph Representation Learning for Multi-Relational Inductive Drug-Gene Interaction Prediction [article]

Jiahua Rao, Shuangjia Zheng, Sijie Mai, Yuedong Yang
<span title="2022-05-12">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To address these problems, we propose a novel Communicative Subgraph representation learning for Multi-relational Inductive drug-Gene interactions prediction (CoSMIG), where the predictions of drug-gene  ...  relations are made through subgraph patterns, and thus are naturally inductive for unseen drugs/genes without retraining or utilizing external domain features.  ...  IGMC, instead of learning transductive node-level features, learns local graph patterns related to the interactions inductively based on relational graph neural network (R-GCN), showing highly competitive  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.05957v1">arXiv:2205.05957v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yc56c4nzmrgwzkwvsb7acbme4a">fatcat:yc56c4nzmrgwzkwvsb7acbme4a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220514052258/https://arxiv.org/pdf/2205.05957v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3a/b0/3ab05ffb6e8e069e912f53dcb8263a18050623d9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2205.05957v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Inductive learning for product assortment graph completion [article]

Haris Dukic, Georgios Deligiorgis, Pierpaolo Sepe, Davide Bacciu, Marco Trincavelli
<span title="2021-10-04">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose to use inductive learning to enhance a graph encoding style compatibility of a fashion assortment, leveraging rich node information comprising textual descriptions and visual data.  ...  Graphs are a natural representation for assortments, where products are nodes and relations are edges.  ...  Our hypothesis is that the inductive learning model manages to learn the patterns of the connected nodes and transfer them to the sparsely connected nodes, making the structure of the graph more regular  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.01677v1">arXiv:2110.01677v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zpfry4bpcfa2daz77okwncprxi">fatcat:zpfry4bpcfa2daz77okwncprxi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211010003649/https://arxiv.org/pdf/2110.01677v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/45/f7/45f7f09e6ea26265a33f689b3212d072af7eab90.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.01677v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

USING GRAPH EMBEDDINGS FOR WIKIPEDIA LINK PREDICTION

Roman Vitaliyovych Shaptala, Gennadiy Dmytrovych Kyselev
<span title="2019-07-13">2019</span> <i title="National Technical University Kharkiv Polytechnic Institute"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/uh4yq5osibgzrctlu4hn7wo2je" style="color: black;">Bulletin of National Technical University KhPI Series System Analysis Control and Information Technologies</a> </i> &nbsp;
Recently, graph embedding methods have risen to popularity because of their effectiveness and the ability to transfer knowledge between tasks.  ...  Inspired by the famous in machine learning and natural language processing research Word2Vec approach, these methods try to learn a distributed vector representation, called an embedding, of graph nodes  ...  Firstly, it is naturally inductive as the learned embeddings generalize to new entities and across networks and therefore might be used for transfer learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.20998/2079-0023.2019.01.09">doi:10.20998/2079-0023.2019.01.09</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/okncwmfjund5fjl6jeckd6iu2m">fatcat:okncwmfjund5fjl6jeckd6iu2m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200305113511/http://repository.kpi.kharkov.ua/bitstream/KhPI-Press/42356/1/vestnik_KhPI_2019_1_SAUI_Shaptala_Using.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/63/12/63122867d3d4d2df5628231f15a74e21041b98b6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.20998/2079-0023.2019.01.09"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Recent Advances in Heterogeneous Relation Learning for Recommendation [article]

Chao Huang
<span title="2021-10-07">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Finally, we present an exploratory outlook to highlight several promising directions and opportunities in heterogeneous relational learning frameworks for recommendation.  ...  To address this problem, recent research developments can fall into three major lines: social recommendation, knowledge graph-enhanced recommender system, and multi-behavior recommendation.  ...  learning for recommendation.  ... 
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