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Temporal Meta-path Guided Explainable Recommendation [article]

Hongxu Chen, Yicong Li, Xiangguo Sun, Guandong Xu, Hongzhi Yin
<span title="2021-01-05">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
recommendations.  ...  This paper utilizes well-designed item-item path modelling between consecutive items with attention mechanisms to sequentially model dynamic user-item evolutions on dynamic knowledge graph for explainable  ...  MODEL In this section, we introduce the proposed model Temporal Metapath Guided Explainable Recommendation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2101.01433v1">arXiv:2101.01433v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hmwunjchvfashccozecbkswtra">fatcat:hmwunjchvfashccozecbkswtra</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210112105659/https://arxiv.org/pdf/2101.01433v1.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/04/c6/04c63ded1bd875bcc991573919c0c5e3c726f9e8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2101.01433v1" 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>

Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation [article]

Yicong Li, Hongxu Chen, Yile Li, Lin Li, Philip S. Yu, Guandong Xu
<span title="2021-11-24">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
In this paper, we propose a novel Temporal Meta-path Guided Explainable Recommendation leveraging Reinforcement Learning (TMER-RL), which utilizes reinforcement item-item path modelling between consecutive  ...  Recent advances in path-based explainable recommendation systems have attracted increasing attention thanks to the rich information provided by knowledge graphs.  ...  METHODOLOGY In this section, we introduce the proposed model Reinforcement Learning based Temporal Meta-path Instances Guided Explainable Recommendation (TMER-RL).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.12262v1">arXiv:2111.12262v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rcgzx3akvjamjjvj4zvy6kptle">fatcat:rcgzx3akvjamjjvj4zvy6kptle</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211127153913/https://arxiv.org/pdf/2111.12262v1.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/0a/7d/0a7d906e158949575ba438bfddf135a99388fc11.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.12262v1" 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>

Leaping Through Time with Gradient-based Adaptation for Recommendation [article]

Nuttapong Chairatanakul, Hoang NT, Xin Liu, Tsuyoshi Murata
<span title="2021-12-28">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
By design, GTL learns long-term patterns by finding the shortest learning path across unordered temporal data.  ...  Such a problem is known as the temporal dynamics problem, and it is one of the main challenges in recommender system modeling.  ...  explain the dynamic behavior of LeapRec.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2112.05914v2">arXiv:2112.05914v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/al45nkju6bd7voo23bvhtn5ama">fatcat:al45nkju6bd7voo23bvhtn5ama</a> </span>
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Survey on entity linking for domain specific with heterogeneous information networks

S. Mythrei, S. Singaravelan
<span title="2019-12-31">2019</span> <i title="Croatian Communication Association"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/c3stkl273zeghabapo5ehqr2zm" style="color: black;">Informatologia</a> </i> &nbsp;
This Meta path guides mining of the network and searching network, it also understands the semantic meaning of network objects.  ...  Finally, explained the effectiveness of the proposed technique over several real datasets. /8/ explained the entity recommendation in the HIN.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.32914/i.52.3-4.5">doi:10.32914/i.52.3-4.5</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zd2ypopvpjfqnd3xw4tg57uuh4">fatcat:zd2ypopvpjfqnd3xw4tg57uuh4</a> </span>
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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>
To address this problem, recent research developments can fall into three major lines: social recommendation, knowledge graph-enhanced recommender system, and multi-behavior recommendation.  ...  Recommender systems have played a critical role in many web applications to meet user's personalized interests and alleviate the information overload.  ...  Overall, the limitation of path-based methods lies in the requirement of domain-specific knowledge for effective meta-path construction, which hinders the flexibility of those KG-enhanced recommendation  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.03455v1">arXiv:2110.03455v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fskj4qdsibfnxefklazdli3tgu">fatcat:fskj4qdsibfnxefklazdli3tgu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211009102552/https://arxiv.org/pdf/2110.03455v1.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/85/63/85630d6ac84cf744f155915e70c073bdbe0ea824.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.03455v1" 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 Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources [article]

Xiao Wang and Deyu Bo and Chuan Shi and Shaohua Fan and Yanfang Ye and Philip S. Yu
<span title="2020-11-30">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
It first finds the co-occurrence of users and items based on the meta-path guided random walks on user-item HG.  ...  Then, a meta-path-guided HGNN, called MEIRec, is designed to learn the nodes' embeddings of users and queries through aggregating the neighbors along the given meta-paths in an end-to-end manner.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2011.14867v1">arXiv:2011.14867v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/phfoxj7qsrfshfednomeok7pau">fatcat:phfoxj7qsrfshfednomeok7pau</a> </span>
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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>
We design an exit mechanism to address the expired information challenge, devise a meta-path method to address the recommendation candidate generation challenge, and develop a new deep policy network structure  ...  In this paper, we focus on the problem of modeling dynamic geo-human interactions in streams for online POI recommendations.  ...  Xian et al. proposed the PGPR framework that employs the meta-path in KG for explainable recommendations [32] .  ... 
<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>
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Moving Object Trajectories Meta-Model and Spatio-Temporal Queries

Azedine Boulmakoul
<span title="2012-04-30">2012</span> <i title="Academy and Industry Research Collaboration Center (AIRCC)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dh256fzd25e4rj4t2ex6em7nti" style="color: black;">International Journal of Database Management Systems</a> </i> &nbsp;
Our proposed meta-model is based on ontology and event approach, incorporates existing presentations of trajectory and integrates new patterns like space-time path to describe activities in geographical  ...  We introduce recursive Region of Interest concepts and deal mobile objects trajectories with diverse spatio-temporal sampling protocols and different sensors available that traditional data model alone  ...  Instantiation of our Meta model allows producing existing trajectories models as well as allowing creation of space-time path concept (trajectories sequence's of activities).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5121/ijdms.2012.4203">doi:10.5121/ijdms.2012.4203</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/y5ikhlsfvjeitpnhtid2oyltwm">fatcat:y5ikhlsfvjeitpnhtid2oyltwm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180604070418/http://www.airccse.org/journal/ijdms/papers/4212ijdms03.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/99/57/99571e0244541e8f7ce773326e347774c27f9f58.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5121/ijdms.2012.4203"> <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>

Trends in content-based recommendation

Pasquale Lops, Dietmar Jannach, Cataldo Musto, Toine Bogers, Marijn Koolen
<span title="2019-03-07">2019</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/yrjf5yycp5abxkr5hcdpxjoj5i" style="color: black;">User modeling and user-adapted interaction</a> </i> &nbsp;
explain the internal mechanisms that guide otherwise black-box-like algorithms.  ...  Algorithm-related trends Meta-path based approaches From an algorithmic perspective, the availability of typed links between different types of objects in HINs inspired the development of meta-path based  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11257-019-09231-w">doi:10.1007/s11257-019-09231-w</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ftunw4mq5vgojifno3yqklfwbq">fatcat:ftunw4mq5vgojifno3yqklfwbq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190503043820/https://link.springer.com/content/pdf/10.1007%2Fs11257-019-09231-w.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/70/b0/70b0c38b5c07aa178c1c5ca6c9d15969bcd4591f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11257-019-09231-w"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

A Theory-Driven Design Framework for Social Recommender Systems

Ofer Arazy, Nanda Kumar, Bracha Shapira
<span title="">2010</span> <i title="Association for Information Systems"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/zi576ij4kzbizn6deimehswscu" style="color: black;">Journal of the AIS</a> </i> &nbsp;
This striking correspondence highlights the importance of behavioral theory in guiding system design. We discuss implications for design science and for research on recommender systems.  ...  We designed, developed, and tested a recommender system based on these principles, and found that the same types of relationships yield the best recommendation accuracy.  ...  s (192) argument for the paths from kernel theories to meta-requirements (path A in Figure 3 ), and then to meta-design (path B) and design propositions (path C), we make the case for additional possible  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17705/1jais.00237">doi:10.17705/1jais.00237</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pklj2rtkincvxbul6ecoadnhiq">fatcat:pklj2rtkincvxbul6ecoadnhiq</a> </span>
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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.  ...  Nevertheless, there are various challenges of applying RL in recommender systems.  ...  to guide path-finding.  ... 
<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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A Survey of Heterogeneous Information Network Analysis [article]

Chuan Shi, Yitong Li, Jiawei Zhang, Yizhou Sun, Philip S. Yu
<span title="2015-11-16">2015</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
[181] study how to discover meta paths automatically which can best explain the relationship between node pairs. Another important issue is to automatically determine the weights of meta paths.  ...  [15] combine meta-path selection and user-guided information for clustering. Some interesting works are still worth doing.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1511.04854v1">arXiv:1511.04854v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/n2k3sulq3fbq3e34lrfrv3uoou">fatcat:n2k3sulq3fbq3e34lrfrv3uoou</a> </span>
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Reinforcement learning on graphs: A survey [article]

Nie Mingshuo, Chen Dongming, Wang Dongqi
<span title="2022-04-27">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
[148] propose a temporal path-based RL model to solve the temporal knowledge graph reasoning task and design a temporal-based reward function to guide the learning of the model.  ...  the manual design of meta-paths with agent.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2204.06127v2">arXiv:2204.06127v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7wf6qxnxzza7xbiwjgjmrsrdjq">fatcat:7wf6qxnxzza7xbiwjgjmrsrdjq</a> </span>
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Improving Collaborative Business Process Execution by Traceability and Expressiveness

Michael Zeising, Stefan Schönig, Stefan Jablonski
<span title="">2012</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pzyiigk6mnbcfmpdtfilb6u2hu" style="color: black;">Proceedings of the 8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing</a> </i> &nbsp;
One of those is traceability which means that the actions proposed by the execution engine are explained and justified.  ...  Furthermore, expressivity is mostly limited to static temporal dependencies that lead to a simple temporal ordering of process steps without a proper connection to process perspectives like incorporated  ...  Due to its architecture, not recommended actions may be traced back and explained by the original process constraints.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4108/icst.collaboratecom.2012.250345">doi:10.4108/icst.collaboratecom.2012.250345</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/colcom/ZeisingSJ12.html">dblp:conf/colcom/ZeisingSJ12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cbakdvacvrdohi2q6h2oy5efvm">fatcat:cbakdvacvrdohi2q6h2oy5efvm</a> </span>
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MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation [article]

Yuntao Du, Xinjun Zhu, Lu Chen, Ziquan Fang, Yunjun Gao
<span title="2022-02-08">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Recently, KGs are popularly used as the auxiliary information to enable more accurate, explainable, and diverse user preference recommendations.  ...  meta learner, to capture meta users' preference and entities' knowledge for cold-start recommendations.  ...  The path-based methods aim to find different semantic paths in KG, and then connect items and users to guide recommendation process.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2202.03851v1">arXiv:2202.03851v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ue5s7yvno5bnzi5mj5widt2obq">fatcat:ue5s7yvno5bnzi5mj5widt2obq</a> </span>
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