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Sequence-Aware Recommender Systems [article]

Massimo Quadrana, Paolo Cremonesi, Dietmar Jannach
<span title="2018-02-23">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
we call sequence-aware recommender systems, and outline open challenges in the area.  ...  Based on this review, we propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what  ...  Other approaches rely on a cascade of algorithms [40] , where a sequential model is used to filter the predictions generated by a sequence-agnostic one, or a meta-level approach [87] , where a first  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1802.08452v1">arXiv:1802.08452v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/edjtdc6355cx3bbq2nkelogiqy">fatcat:edjtdc6355cx3bbq2nkelogiqy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191020195003/https://arxiv.org/pdf/1802.08452v1.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/30/57/305758a584ea6fa0941120d9d1615c254409a35b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1802.08452v1" 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>

Sequence-Aware Recommender Systems

Massimo Quadrana, Paolo Cremonesi, Dietmar Jannach
<span title="2018-07-06">2018</span> <i title="Association for Computing Machinery (ACM)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/eiea26iqqjcatatlgxdpzt637y" style="color: black;">ACM Computing Surveys</a> </i> &nbsp;
we call sequence-aware recommender systems, and outline open challenges in the area.  ...  Based on this review, we propose a categorization of the corresponding recommendation tasks and goals, summarize existing algorithmic solutions, discuss methodological approaches when benchmarking what  ...  Other approaches rely on a cascade of algorithms [40] , where a sequential model is used to filter the predictions generated by a sequence-agnostic one, or a meta-level approach [87] , where a first  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3190616">doi:10.1145/3190616</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p3wuhyx2yvgfzo5k6lugjuxobu">fatcat:p3wuhyx2yvgfzo5k6lugjuxobu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200309061050/https://re.public.polimi.it/retrieve/handle/11311/1084454/368312/2018-seqrec_survey.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/6a/d0/6ad092c3dc7c08631a2220ba08b09c680750d504.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3190616"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

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

Simon Werner, Achim Rettinger, Lavdim Halilaj, Jürgen Lüttin
<span title="2021-08-31">2021</span> <i title="IOS Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qv2vo27d4zghvkz2dhzcboly5e" style="color: black;">Applications and Practices in Ontology Design, Extraction, and Reasoning</a> </i> &nbsp;
In this paper, we investigate how well state-of-the-art approaches do exploit those different dimensions relevant to POI recommendation tasks.  ...  Learned latent vector representations are key to the success of many recommender systems in recent years.  ...  in a POI recommendation task.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3233/ssw210046">doi:10.3233/ssw210046</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rfsad4zo7zhybdjloyor4zjczu">fatcat:rfsad4zo7zhybdjloyor4zjczu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210907094714/https://ebooks.iospress.nl/pdf/doi/10.3233/SSW210046" 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/b9/30/b93063d222688998e12d235144d61afffb03c64f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3233/ssw210046"> <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>

Recommendation Through Mixtures of Heterogeneous Item Relationships [article]

Wang-Cheng Kang, Mengting Wan, Julian McAuley
<span title="2018-08-29">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Our method borrows ideas from mixtures-of-experts approaches as well as knowledge graph embeddings.  ...  Recommender Systems have proliferated as general-purpose approaches to model a wide variety of consumer interaction data.  ...  In addition, CNN-based approaches have also shown competitive performance on sequential and session-based recommendation [36, 38] . 'Relationship-aware' Recommendation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1808.10031v1">arXiv:1808.10031v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uqijrxoctfhqfptbb2ujjcfw3u">fatcat:uqijrxoctfhqfptbb2ujjcfw3u</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200924081006/https://arxiv.org/pdf/1808.10031v1.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/3d/79/3d7978b07fa08bfb30b03a79b70987229e07d29f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1808.10031v1" 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 Composite of Heterogeneous Sources Recommenders (CHR)

Md Masum, Jingyu Sun, Dong Wei
<span title="2020-03-17">2020</span> <i title="Foundation of Computer Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b637noqf3vhmhjevdfk3h5pdsu" style="color: black;">International Journal of Computer Applications</a> </i> &nbsp;
We see that our approach produces more specific recommendations than other options and also presenting instinctive explanations behind the recommendations.  ...  Our method adopts ideas from knowledge graph representations as well as several expert networks where each of them specializes in a different part of input space.  ...  Finally, the goal of sequential recommendation is to rank the next-item higher than unrelated items; the loss function we use is defined as: (7) Where We utilize a multi-task learning method mutually  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5120/ijca2020919915">doi:10.5120/ijca2020919915</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ogtu4xxhijgabm2d53jdbzomra">fatcat:ogtu4xxhijgabm2d53jdbzomra</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220308160341/https://www.ijcaonline.org/archives/volume177/number41/shagar-2020-ijca-919915.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/1c/f8/1cf8a44251a1a0797615c9d7287a7e7601daabf6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5120/ijca2020919915"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A Survey on Session-based Recommender Systems [article]

Shoujin Wang, Longbing Cao, Yan Wang, Quan Z. Sheng, Mehmet Orgun, Defu Lian
<span title="2021-05-15">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In recent years, session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs.  ...  We propose a general problem statement of SBRSs, summarize the diversified data characteristics and challenges of SBRSs, and define a taxonomy to categorize the representative SBRS research.  ...  Several survey papers focus particularly on SRSs, including sequence-aware recommender systems [91] , deep learning for sequential recommendations [27] and sequential recommender systems [128] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1902.04864v3">arXiv:1902.04864v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oka5bvibzzbk5oreltrupehaey">fatcat:oka5bvibzzbk5oreltrupehaey</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210519153719/https://arxiv.org/pdf/1902.04864v3.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/56/10/56107ce1a3f5d6c0fdd32fa7ab95329ec207ba9c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1902.04864v3" 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 Survey on Deep Learning Based Point-Of-Interest (POI) Recommendations [article]

Md. Ashraful Islam, Mir Mahathir Mohammad, Sarkar Snigdha Sarathi Das, Mohammed Eunus Ali
<span title="2020-11-20">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To the best of our knowledge, this work is the first comprehensive survey of all major deep learning-based POI recommendation works.  ...  A POI recommendation technique essentially exploits users' historical check-ins and other multi-modal information such as POI attributes and friendship network, to recommend the next set of POIs suitable  ...  [109] propose a novel Hyperbolic Metric Embedding (HME) approach for the next-POI recommendation task.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.10187v1">arXiv:2011.10187v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3uampnqerfdvnpuzrxcrsjviwq">fatcat:3uampnqerfdvnpuzrxcrsjviwq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201124042102/https://arxiv.org/pdf/2011.10187v1.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/90/b0/90b0975dedd52e33526bf68bccca765f26b0d156.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.10187v1" 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>

Graph Neural Networks in Recommender Systems: A Survey [article]

Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, Bin Cui
<span title="2022-04-02">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
in graph representation learning.  ...  Specifically, we provide a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks.  ...  Gowalla is a classical dataset for POI recommendation and adopted in user-item collaborative filtering and sequential recommendation as well.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.02260v4">arXiv:2011.02260v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hvk22yyid5bzjnzmzchyti25ja">fatcat:hvk22yyid5bzjnzmzchyti25ja</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220216001525/https://arxiv.org/pdf/2011.02260v3.pdf" title="fulltext PDF download [not primary version]" 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] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/26/e9/26e95ce8a09cb7460bbaba0bdd01771071b15c5c.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.02260v4" 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>

Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams [article]

Dongjie Wang, Kunpeng Liu, Hui Xiong, Yanjie Fu
<span title="2022-01-19">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Specifically, we formulate the in-stream geo-human interaction modeling problem into a novel deep interactive reinforcement learning framework, where an agent is a recommender and an action is a next POI  ...  We uniquely model the reinforcement learning environment as a joint and connected composition of users and geospatial contexts (POIs, POI categories, functional zones).  ...  To overcome the sparsity challenge, we propose and leverage predefined meta-paths to select the most relevant POI candidates for recommendation with awareness of semantics and interests' context.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2201.10983v1">arXiv:2201.10983v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2fysztm4mfba7nugsibghmi5km">fatcat:2fysztm4mfba7nugsibghmi5km</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220129143441/https://arxiv.org/pdf/2201.10983v1.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/c9/b8/c9b823d4ac2fbc5774124f17149fe04e654884f7.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2201.10983v1" 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>

Research Commentary on Recommendations with Side Information: A Survey and Research Directions [article]

Zhu Sun, Qing Guo, Jie Yang, Hui Fang, Guibing Guo, Jie Zhang, Robin Burke
<span title="2019-11-08">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
One involves the different methodologies of recommendation: the memory-based methods, latent factor, representation learning, and deep learning models.  ...  To address these issues, a great number of recommendation algorithms have been proposed to leverage side information of users or items (e.g., social network and item category), demonstrating a high degree  ...  With knowledge graphs as an example, the current deep learning based recommendation approaches are limited to the usage of the most basic information in a knowledge graph, for example, paths or meta-paths  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.12807v2">arXiv:1909.12807v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2nj4crzcd5attidhd3kneszmki">fatcat:2nj4crzcd5attidhd3kneszmki</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200828130914/https://arxiv.org/pdf/1909.12807v2.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/25/45/2545904c939cae69a80025baac2be90b367b58c1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1909.12807v2" 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 Survey on Reinforcement Learning for Recommender Systems [article]

Yuanguo Lin, Yong Liu, Fan Lin, Lixin Zou, Pengcheng Wu, Wenhua Zeng, Huanhuan Chen, Chunyan Miao
<span title="2022-03-24">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
recommendatin, sequential recommendation, and explainable recommendation.  ...  To this end, we firstly provide a thorough overview, comparisons, and summarization of RL approaches applied in four typical recommendation scenarios, including interactive recommendation, conversational  ...  Fortunately, meta-RL, such as Meta-Strategy [136] and Meta-Q-Learning [137] , first learns a large number of RL tasks to obtain enough prior knowledge, and later can be quickly adapted to the new environment  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.10665v2">arXiv:2109.10665v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/wx5ghn66hzg7faxee54jf7gspq">fatcat:wx5ghn66hzg7faxee54jf7gspq</a> </span>
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Graph Neural Networks in Recommender Systems: A Survey

Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, Bin Cui
<span title="2022-05-05">2022</span> <i title="Association for Computing Machinery (ACM)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/eiea26iqqjcatatlgxdpzt637y" style="color: black;">ACM Computing Surveys</a> </i> &nbsp;
in graph representation learning.  ...  Specifically, we provide a taxonomy of GNN-based recommendation models according to the types of information used and recommendation tasks.  ...  Gowalla is a classical dataset for POI recommendation and adopted in user-item collaborative iltering and sequential recommendation as well.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3535101">doi:10.1145/3535101</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hgv2tbx3k5hzbnkupwsysqwjmy">fatcat:hgv2tbx3k5hzbnkupwsysqwjmy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220506204142/https://dl.acm.org/doi/pdf/10.1145/3535101" 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/ca/94/ca94471a095334eae06ee57170c5943d0b2893ca.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3535101"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

A Survey on Knowledge Graph-Based Recommender Systems [article]

Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, Qing He
<span title="2020-02-28">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Such an approach can not only alleviate the abovementioned issues for a more accurate recommendation, but also provide explanations for recommended items.  ...  In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field and summarize them from two perspectives.  ...  Next, it summarizes explainable rules, which is in the form of meta-paths in the KG. The corresponding weight for each rule is further learned.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2003.00911v1">arXiv:2003.00911v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qhyca7pu3beqtk6x55kpggowea">fatcat:qhyca7pu3beqtk6x55kpggowea</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200322210552/https://arxiv.org/pdf/2003.00911v1.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/2003.00911v1" 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>

GARG: Anonymous Recommendation of Point-of-Interest in Mobile Networks by Graph Convolution Network

Shiwen Wu, Yuanxing Zhang, Chengliang Gao, Kaigui Bian, Bin Cui
<span title="2020-07-29">2020</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4pfqfq76vvcxljfl7mvdl37n5q" style="color: black;">Data Science and Engineering</a> </i> &nbsp;
the collaborative, sequential and content-aware information.  ...  Today, the LBS applications expect the recommender systems to recommend the accurate next POI in an anonymous manner, without inquiring users' attributes or knowing the detailed features of the vast number  ...  NEXT [29] incorporates meta-data information, time interval and visit time, and leverages the DeepWalk method to encode such knowledge.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s41019-020-00135-z">doi:10.1007/s41019-020-00135-z</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qlo77xrwdzbknfvqco5qjxwcdy">fatcat:qlo77xrwdzbknfvqco5qjxwcdy</a> </span>
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Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations [article]

Hui Fang, Danning Zhang, Yiheng Shu, Guibing Guo
<span title="2020-10-10">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In the field of sequential recommendation, deep learning (DL)-based methods have received a lot of attention in the past few years and surpassed traditional models such as Markov chain-based and factorization-based  ...  Specifically,we illustrate the concept of sequential recommendation, propose a categorization of existing algorithms in terms of three types of behavioral sequence, summarize the key factors affecting  ...  [25] proposed 2 (Scenario-specific Sequential Meta learner) which combines the scenario-specific learning with a model-agnostic sequential meta learning for more effective recommendation.  ... 
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