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Augmenting the User-Item Graph with Textual Similarity Models [article]

Federico López and Martin Scholz and Jessica Yung and Marie Pellat and Michael Strube and Lucas Dixon
<span title="2021-09-20">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
A paraphrase similarity model is applied to widely available textual data, such as reviews and product descriptions, yielding new semantic relations that are added to the user-item graph.  ...  Results show that the data augmentation technique provides significant improvements to all types of models, with the most pronounced gains for knowledge graph-based recommenders, particularly in cold-start  ...  GRK 1994/1 and the Klaus Tschira Foundation, Heidelberg, Germany.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.09358v1">arXiv:2109.09358v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vveigeq64fdchbvtvuph6igq6m">fatcat:vveigeq64fdchbvtvuph6igq6m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210922124323/https://arxiv.org/pdf/2109.09358v1.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/96/66/9666d4afaf376b0dd49f81255063b92e3d5ed991.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2109.09358v1" 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>

Trails of experiences

Andrea Schweer, Steve Jones, Annika Hinze
<span title="">2009</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xw76b2hb7ben3eqwmzaj4i76iy" style="color: black;">Proceedings of the 10th International Conference NZ Chapter of the ACM&#39;s Special Interest Group on Human-Computer Interaction - CHINZ &#39;09</a> </i> &nbsp;
Its major contribution is the combination of a graph-based data model with navigation mechanisms based on various types of context and on associations.  ...  Systems to augment personal information aim to support people in remembering both past experiences and specific information associated with past experiences.  ...  The Digital Parrot's data model is graph-based. Users can take advantage of this structure to navigate among information items. In the graph view, this type of navigation is direct and intuitive.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1577782.1577801">doi:10.1145/1577782.1577801</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/chinz/SchweerJH09.html">dblp:conf/chinz/SchweerJH09</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/akb744cgvbbs5gyqhnaeoutp3y">fatcat:akb744cgvbbs5gyqhnaeoutp3y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20150122123357/http://researchcommons.waikato.ac.nz/bitstream/handle/10289/8110/SchweerPaperCHINZ2009.pdf?sequence=1" 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/55/76/5576ef229e4dcd8fc95516405d0f5652015e2069.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/1577782.1577801"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Towards a Semantic Graph-based Recommender System. A Case Study of Cultural Heritage

Sara Qassimi, El Hassan Abdelwahed
<span title="2021-07-28">2021</span> <i title="Pensoft Publishers"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qvdzjysvg5hozjo6vlhyz6qa4a" style="color: black;">Journal of universal computer science (Online)</a> </i> &nbsp;
place and (2) effectively modeling the emerging graphs representing the semantic relatedness of similar cultural heritage places and their related tags.  ...  Our proposed approach represents a semantic graph-based recommender system of cultural heritage places through two steps; (1) constructing an emergent semantic description that semantically augments the  ...  For instance, using the similarity matrix (i.e., user-item matrix) for recommending movies is unable to model and merge the explicit taste of a user about its favorite genres with its ratings and tagging  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3897/jucs.70330">doi:10.3897/jucs.70330</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7m7h5whp4jhvzatit5hbd6hdu4">fatcat:7m7h5whp4jhvzatit5hbd6hdu4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210804164759/https://lib.jucs.org/lib/ajax_srv/generate_pdf.php?document_id=70330&amp;readonly_preview=1&amp;file_id=0" 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/97/01/97018f686ab50d114c172da4261a2475a9262fcd.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3897/jucs.70330"> <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>

Latent Structure Mining with Contrastive Modality Fusion for Multimedia Recommendation [article]

Jinghao Zhang, Yanqiao Zhu, Qiang Liu, Mengqi Zhang, Shu Wu, Liang Wang
<span title="2022-03-17">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Firstly, only collaborative item-item relationships are implicitly modeled through high-order item-user-item co-occurrences.  ...  Previous studies focus on modeling user-item interactions with multimodal features included as side information. However, this scheme is not well-designed for multimedia recommendation.  ...  2 The proportion (%) of users buying semantically similar items with respect to different k.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.00678v2">arXiv:2111.00678v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/boqsb2twpjd45gbtol5tpkirqa">fatcat:boqsb2twpjd45gbtol5tpkirqa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220326172817/https://arxiv.org/pdf/2111.00678v2.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/4b/6d/4b6d723aacffd050619694075e48fe47138d65ef.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2111.00678v2" 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>

Centrality-Based Paper Citation Recommender System

Abdul Samad, Muhammad Arshad Islam, Muhammad Azhar Iqbal, Muhammad Aleem
<span title="2019-06-13">2019</span> <i title="European Alliance for Innovation n.o."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/l7xphlcxlzcopemlfe46zdv2ui" style="color: black;">EAI Endorsed Transactions on Industrial Networks and Intelligent Systems</a> </i> &nbsp;
Researchers cite papers in order to connect the new research ideas with previous research.  ...  This work analyzes textual and topological similarity measures (i.e., Jaccard and Cosine) to evaluate which one performs well in finding similar papers?  ...  Philip [23] uses a keyword-based vector space model to make item recommendations for digital libraries. They built a system with user interactions in order to build a user profile.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4108/eai.13-6-2019.159121">doi:10.4108/eai.13-6-2019.159121</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/b2ktyxzeljfu3jlacmwbsqfqgu">fatcat:b2ktyxzeljfu3jlacmwbsqfqgu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200306034959/https://eudl.eu/pdf/10.4108/eai.13-6-2019.159121" 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/29/73/29734be55c7de65ad825f7aacc3b838974206424.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4108/eai.13-6-2019.159121"> <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>

Exploiting Visual Content of Book Front Cover to Aggrandize the Content Based Book Recommendation System

<span title="2019-10-10">2019</span> <i title="Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cj3bm7tgcffurfop7xzswxuks4" style="color: black;">VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE</a> </i> &nbsp;
Content based recommendation model is one of the recommendation models which purely rely on product features and the current user preferences.  ...  Our experiment shows that augmenting the visual features to the existing content-based recommender models performed well.  ...  Later these textual descriptors and object features are augmented with the CNN extracted features, and then augmented model is able to identify the similar books of the same genre.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.35940/ijitee.l3158.1081219">doi:10.35940/ijitee.l3158.1081219</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ciqbxtlnvvaoxegmbmhbe3guie">fatcat:ciqbxtlnvvaoxegmbmhbe3guie</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220308075622/https://www.ijitee.org/wp-content/uploads/papers/v8i12/L31581081219.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/a8/76/a87625c64b511c33ec01d38fef4eb2976aeb9929.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.35940/ijitee.l3158.1081219"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Early Anomaly Detection by Learning and Forecasting Behavior [article]

Tong Zhao, Bo Ni, Wenhao Yu, Meng Jiang
<span title="2020-10-20">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The behavior graph is augmented with the predicted behaviors such that the graph-based anomaly detection methods can achieve better performance, and the detection results can support the behavior predictor  ...  With the augmented methods in Eland, the performance of anomaly detection at an earlier stage is comparable with or better than non-augmented methods on a greater amount of observation.  ...  Behavior Forecasting Module The behavior forecasting module provides data augmentation on the user-item bipartite graph with a sequence-to-sequence neural model.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2010.10016v1">arXiv:2010.10016v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/j7lohwpsj5fodduy5q66oagaga">fatcat:j7lohwpsj5fodduy5q66oagaga</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201023214210/https://arxiv.org/pdf/2010.10016v1.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/10/0a/100a6c5f486a5e32c0cdbfd60938e280c9a4136f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2010.10016v1" 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 graph model for E-commerce recommender systems

Zan Huang, Wingyan Chung, Hsinchun Chen
<span title="">2004</span> <i title="Wiley"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/irrfofzyh5dn5jykwm6u5zvb5e" style="color: black;">Journal of the American Society for Information Science and Technology</a> </i> &nbsp;
In this research, we developed a graph model that provides a generic data representation and can support different recommendation methods.  ...  However, comparisons among different methods showed that high-degree association retrieval did not perform significantly better than the association mining method or the direct retrieval method in our  ...  We would like to thank books.com.tw for providing the data set and assistance during the project.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/asi.10372">doi:10.1002/asi.10372</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/uyuo5j6ykbdibc6wojchezapne">fatcat:uyuo5j6ykbdibc6wojchezapne</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20060622083852/http://dlist.sir.arizona.edu:80/447/01/huang2.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/ae/4c/ae4c949029f5520a32adbe8f238b0b6c8c366042.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1002/asi.10372"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> wiley.com </button> </a>

An Academic Text Recommendation Method Based on Graph Neural Network

Jie Yu, Chenle Pan, Yaliu Li, Junwei Wang
<span title="2021-04-16">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dmr4kpn2yreovpdxpdiqtjcrnu" style="color: black;">Information</a> </i> &nbsp;
Based on the graph neural network and attention mechanism, this paper proposes a session-based text recommendation model (TXT-SR) incorporating the semantic relations, which is applied to the academic  ...  The relationship between adjacent items in the session is not noticed.  ...  After integrating the textual semantic relationship between items, we replace the weights of the edges of the graph with the similarity of the text.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/info12040172">doi:10.3390/info12040172</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yolo6jvexrdttou25nikhq36oq">fatcat:yolo6jvexrdttou25nikhq36oq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210425175115/https://res.mdpi.com/d_attachment/information/information-12-00172/article_deploy/information-12-00172-v2.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/2e/8f/2e8fa20658c45ecea5567e9eb0928490a7d76bde.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/info12040172"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Survey for Trust-aware Recommender Systems: A Deep Learning Perspective [article]

Manqing Dong, Feng Yuan, Lina Yao, Xianzhi Wang, Xiwei Xu, Liming Zhu
<span title="2020-10-06">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
A significant remaining challenge for existing recommender systems is that users may not trust the recommender systems for either lack of explanation or inaccurate recommendation results.  ...  We focus on the work based on deep learning techniques, an emerging area in the recommendation research.  ...  Both of the users and items are learned via Multi-Graph CNNs: the users are modeled by their relationships, and the items are modeled with the images.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.03774v2">arXiv:2004.03774v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/q7mehir7hbbzpemw3q5fkby5ty">fatcat:q7mehir7hbbzpemw3q5fkby5ty</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201010114329/https://arxiv.org/pdf/2004.03774v2.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/2004.03774v2" 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>

Recommendation with Multi-Source Heterogeneous Information

Li Gao, Hong Yang, Jia Wu, Chuan Zhou, Weixue Lu, Yue Hu
<span title="">2018</span> <i title="International Joint Conferences on Artificial Intelligence Organization"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vfwwmrihanevtjbbkti2kc3nke" style="color: black;">Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence</a> </i> &nbsp;
To model the multi-source heterogeneous information, we use two coupled neural networks to capture the deep network representations of items, based on which a new recommendation model Collaborative multi-source  ...  First, these models partially use item information and mostly ignore important contextual information in social networks such as textual content and social tag information.  ...  Acknowledgments We would like to thank the anonymous reviewers for their valuable comments and suggestions. This work was supported by the National Key  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24963/ijcai.2018/469">doi:10.24963/ijcai.2018/469</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/ijcai/GaoYWZLH18.html">dblp:conf/ijcai/GaoYWZLH18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/63fvrois7ffsfk2ojwp3yeymly">fatcat:63fvrois7ffsfk2ojwp3yeymly</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190218163200/http://pdfs.semanticscholar.org/0fdc/50efdfb7c193b6626116622340d4d6002ffc.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/0f/dc/0fdc50efdfb7c193b6626116622340d4d6002ffc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24963/ijcai.2018/469"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Personalised Network Activity Feeds: Finding Needles in the Haystacks [chapter]

Shlomo Berkovsky, Jill Freyne
<span title="2014-12-25">2014</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
still greatly exceeds the capacity of information that any one user can deal with.  ...  discovery for the user.  ...  In addition, the graph allows for content discovery, as relationships with users who are non-direct friends, but to whom the user is close in a network graph, can be seen to provide relevant feed items  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-14723-9_2">doi:10.1007/978-3-319-14723-9_2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jgfdcxe7nrbetjfagxlmjr3efy">fatcat:jgfdcxe7nrbetjfagxlmjr3efy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170814185154/http://www.springer.com/cda/content/document/cda_downloaddocument/9783319147222-c2.pdf?SGWID=0-0-45-1491881-p177203714" 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/ed/94/ed94280e6ecb6d82602c9f24acd6feef8c56b7c9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-14723-9_2"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

A Text-based Deep Reinforcement Learning Framework Using Self-supervised Graph Representation for Interactive Recommendation

Chaoyang Wang, Zhiqiang Guo, Jianjun Li, Guohui Li, Peng Pan
<span title="2021-11-30">2021</span> <i title="Association for Computing Machinery (ACM)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/efbaz2qivvacnnxhhw7wwtiqva" style="color: black;">ACM/IMS Transactions on Data Science</a> </i> &nbsp;
Specifically, we leverage textual information to map items and users into a same feature space by a self-supervised embedding method based on the graph convolutional network, which greatly alleviates data  ...  Since the action space of IRS is discrete, it is natural to implement TRGIR with Deep Q-learning Network (DQN).  ...  By learning the embeddings of users and items with a GCN-based self-supervised embedding method on a relation graph that contains textual information, we gain user and item vectors with semantics, which  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3522596">doi:10.1145/3522596</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/k5fajvd3frey7fxzasz3h2pwdi">fatcat:k5fajvd3frey7fxzasz3h2pwdi</a> </span>
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SocialRec: A Context-aware Recommendation Framework with Explicit Sentiment Analysis

Rizwana Irfan, Osman Khalid, Muhammad U. S. Khan, Faisal Rehman, Atta Ur Rehman Khan, Raheel Nawaz
<span title="">2019</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
However, simply relying on users' ratings about an item can produce biased opinions, as a user's textual feedback may differ from the item rating provided by the user.  ...  We propose a greedy heuristic to produce ranking of items based on users' social similarities and matching preferences.  ...  The similarity of the active user is computed with all of the reviewer nodes in the graph using (11) and only those nodes are connected with active user with which the similarity is greater than zero  ... 
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Multi-Auxiliary Augmented Collaborative Variational Auto-encoder for Tag Recommendation [article]

Jing Yi, Xubin Ren, Zhenzhong Chen
<span title="2022-04-20">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
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.  ...  Specifically, the model learns deep latent embeddings from different item auxiliary information using variational auto-encoders (VAE), which could form a generative distribution over each auxiliary information  ...  [34] proposed to encompass variational auto-encoders through augmenting structures to model the auxiliary information and to model the implicit user feedback, where a conditional VAE [35] for modeling  ... 
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