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Exploiting User Preference for Online Learning in Web Content Optimization Systems

Jiang Bian, Bo Long, Lihong Li, Taesup Moon, Anlei Dong, Yi Chang
<span title="2014-04-30">2014</span> <i title="Association for Computing Machinery (ACM)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/gmnsxns6fvf23c44lkzaej5laq" style="color: black;">ACM Transactions on Intelligent Systems and Technology</a> </i> &nbsp;
Further analysis illustrates that our new pairwise learning approaches can benefit personalized recommendation more than pointwise models, since the data sparsity is more critical for personalized content  ...  The state-of-the-art online learning methodology adapts dedicated pointwise models to independently estimate the attractiveness score for each candidate content item.  ...  Pointwise Model Pointwise model is the most straightforward approach adaptable to the online learning framework.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2493259">doi:10.1145/2493259</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qp4lvhadrbhstbafchk4rigrgy">fatcat:qp4lvhadrbhstbafchk4rigrgy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20140909222628/http://www.yichang-cs.com/yahoo/TIST13_cokeranking.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/18/bb/18bba828019bc3aa369d5ce9ba78e1c77c270ae4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2493259"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Conference Paper Recommendation for Academic Conferences

Shuchen Li, Peter Brusilovsky, Sen Su, Xiang Cheng
<span title="">2018</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;
Furthermore, we employ a pairwise learning to rank model which exploits the pairwise user preference to learn a function that predicts a user's preference towards a paper based on the extracted features  ...  In particular, besides the contents, we exploit the relationships between a user and a paper's authors for recommendation.  ...  In general, learning to rank approaches can be classified into three different types: pointwise, pairwise, and listwise.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2018.2817497">doi:10.1109/access.2018.2817497</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/khbmkdl7lvdl7ivtk6teany3gm">fatcat:khbmkdl7lvdl7ivtk6teany3gm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190430044157/http://d-scholarship.pitt.edu/34997/1/Li2018-access.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/11/6e/116e58816e855dbdfce5bf05f005647c3d586ea3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2018.2817497"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Neural Semantic Personalized Ranking for item cold-start recommendation

Travis Ebesu, Yi Fang
<span title="2017-03-03">2017</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/qkuxm4jkpnclphpwzqgsg7qnae" style="color: black;">Information retrieval (Boston)</a> </i> &nbsp;
To address the above challenges, we propose a probabilistic modeling approach called Neural Semantic Personalized Ranking (NSPR) to unify the strengths of deep neural network and pairwise learning.  ...  We demonstrate NSPR's versatility to integrate various pairwise probability functions and propose two variants based on the Logistic and Probit functions.  ...  Implicit feedback Matrix factorization has been adapted to learn from implicit feedback for recommendation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10791-017-9295-9">doi:10.1007/s10791-017-9295-9</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/t5hercdtcvblphotbrrsp4z7my">fatcat:t5hercdtcvblphotbrrsp4z7my</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170705000317/http://www.cse.scu.edu/%7Eyfang/NSPR.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/06/7f/067faa8ec03587537c568418c9d05334a7624212.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s10791-017-9295-9"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Towards Comprehensive Recommender Systems: Time-Aware Unified Recommendations Based on Listwise Ranking of Implicit Cross-Network Data

Dilruk Perera, Roger Zimmermann
<span title="2020-04-03">2020</span> <i title="Association for the Advancement of Artificial Intelligence (AAAI)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wtjcymhabjantmdtuptkk62mlq" style="color: black;">PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE</a> </i> &nbsp;
Therefore, we propose a novel deep learning based unified cross-network solution to mitigate cold-start and data sparsity issues and provide timely recommendations for new and existing users.  ...  Furthermore, we consider the ranking problem under implicit feedback as a classification task, and propose a generic personalized listwise optimization criterion for implicit data to effectively rank a  ...  We gratefully acknowledge the support of NVIDIA Corporation for the donation of the Titan Xp GPU used in this research.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aaai.v34i01.5350">doi:10.1609/aaai.v34i01.5350</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/vm4fsg5fsbhezabexk7wetqtpq">fatcat:vm4fsg5fsbhezabexk7wetqtpq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201106081855/https://aiinternational.org/ojs/index.php/AAAI/article/download/5350/5206" 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/92/ef/92ef4dba50b6c5b4bc022837f3a68691c6ce3b17.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aaai.v34i01.5350"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Towards Comprehensive Recommender Systems: Time-Aware UnifiedcRecommendations Based on Listwise Ranking of Implicit Cross-Network Data [article]

Dilruk Perera, Roger Zimmermann
<span title="2020-08-25">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Therefore, we propose a novel deep learning based unified cross-network solution to mitigate cold-start and data sparsity issues and provide timely recommendations for new and existing users.Furthermore  ...  , we consider the ranking problem under implicit feedback as a classification task, and propose a generic personalized listwise optimization criterion for implicit data to effectively rank a list of items  ...  We gratefully acknowledge the support of NVIDIA Corporation for the donation of the Titan Xp GPU used in this research.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.13516v1">arXiv:2008.13516v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qw27hdqasjaatnqyw5ve5g6rje">fatcat:qw27hdqasjaatnqyw5ve5g6rje</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200907151612/https://arxiv.org/pdf/2008.13516v1.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/2008.13516v1" 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>

Co-factorization machines

Liangjie Hong, Aziz S. Doumith, Brian D. Davison
<span title="">2013</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/puezkhxc3rggrgb456avsvxi34" style="color: black;">Proceedings of the sixth ACM international conference on Web search and data mining - WSDM &#39;13</a> </i> &nbsp;
We explore an extensive set of features and conduct experiments on a real-world dataset, concluding that CoFM with ranking-based loss functions is superior to state-of-the-art methods and yields interpretable  ...  The task differs from conventional recommender systems as the cold-start problem is ubiquitous, and rich features, including textual content, need to be considered.  ...  Acknowledgements This material is based upon work supported in part by the National Science Foundation under Grant Number IIS-0803605.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2433396.2433467">doi:10.1145/2433396.2433467</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/wsdm/HongDD13.html">dblp:conf/wsdm/HongDD13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/7kfmnqwrxvac3d26tb53gipjzm">fatcat:7kfmnqwrxvac3d26tb53gipjzm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20141013234944/http://www.cse.lehigh.edu:80/~brian/pubs/2013/WSDM/co-factorization-machines.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/7b/eb/7beb0b3a6bed95c793c58a6800f2384505b28a77.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2433396.2433467"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

OpenRec

Longqi Yang, Eugene Bagdasaryan, Joshua Gruenstein, Cheng-Kang Hsieh, Deborah Estrin
<span title="">2018</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/puezkhxc3rggrgb456avsvxi34" style="color: black;">Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining - WSDM &#39;18</a> </i> &nbsp;
In this work, we propose OpenRec, an open and modular Python framework that supports extensible and adaptable research in recommender systems.  ...  Unfortunately, existing frameworks do not adequately support extensibility and adaptability and consequently pose signi cant challenges to rapid, iterative, and systematic, experimentation.  ...  ACKNOWLEDGMENTS We would like to sincerely thank the anonymous reviewers for their insightful comments and suggestions. is research is partly funded by Oath through the Connected Experiences Laboratory  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3159652.3159681">doi:10.1145/3159652.3159681</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/wsdm/YangBGHE18.html">dblp:conf/wsdm/YangBGHE18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ev43dr4bp5emnopg4gnrxjszua">fatcat:ev43dr4bp5emnopg4gnrxjszua</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20181125162049/http://www.cs.cornell.edu:80/~ylongqi/paper/YangBGHE18.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/4e/96/4e96e7d96740aa3ed470f68e98c5c1474de0416d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3159652.3159681"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Neural Collaborative Filtering

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua
<span title="">2017</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s4hirppq3jalbopssw22crbwwa" style="color: black;">Proceedings of the 26th International Conference on World Wide Web - WWW &#39;17</a> </i> &nbsp;
Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics.  ...  In this work, we strive to develop techniques based on neural networks to tackle the key problem in recommendation -collaborative filtering -on the basis of implicit feedback.  ...  Acknowledgement The authors thank the anonymous reviewers for their valuable comments, which are beneficial to the authors' thoughts on recommendation systems and the revision of the paper.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3038912.3052569">doi:10.1145/3038912.3052569</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/www/HeLZNHC17.html">dblp:conf/www/HeLZNHC17</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sb4tvd5e4jexblmbqcspzyomyq">fatcat:sb4tvd5e4jexblmbqcspzyomyq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170712124928/http://papers.www2017.com.au.s3-website-ap-southeast-2.amazonaws.com/proceedings/p173.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/d1/1c/d11c75ce793893b3e9d77375a9f8ff6a1b36e3cc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3038912.3052569"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Neural Collaborative Filtering [article]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, Tat-Seng Chua
<span title="2017-08-26">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Although some recent work has employed deep learning for recommendation, they primarily used it to model auxiliary information, such as textual descriptions of items and acoustic features of musics.  ...  By replacing the inner product with a neural architecture that can learn an arbitrary function from data, we present a general framework named NCF, short for Neural network-based Collaborative Filtering  ...  Acknowledgement The authors thank the anonymous reviewers for their valuable comments, which are beneficial to the authors' thoughts on recommendation systems and the revision of the paper.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1708.05031v2">arXiv:1708.05031v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gam2aezz2retvlf2cqqrqv7oni">fatcat:gam2aezz2retvlf2cqqrqv7oni</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200925052204/https://arxiv.org/pdf/1708.05031v2.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/5b/e5/5be5d82e4442478c355c1fd983301cc09c2d4c6b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1708.05031v2" 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>

Listwise Collaborative Filtering

Shanshan Huang, Shuaiqiang Wang, Tie-Yan Liu, Jun Ma, Zhumin Chen, Jari Veijalainen
<span title="">2015</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ibcfmixrofb3piydwg5wvir3t4" style="color: black;">Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR &#39;15</a> </i> &nbsp;
to previous ranking-oriented memory-based CF algorithms.  ...  from previous ranking-oriented memory-based CF algorithms that focus on predicting the pairwise preferences between items.  ...  Model-based rating-oriented CF learns a model based on the observed ratings to make rating predictions.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2766462.2767693">doi:10.1145/2766462.2767693</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/sigir/HuangWLMCV15.html">dblp:conf/sigir/HuangWLMCV15</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3j55zjitzbfa5nrumqwb2zbwsy">fatcat:3j55zjitzbfa5nrumqwb2zbwsy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170810235307/http://users.jyu.fi/~swang/publications/SIGIR15.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/12/d1/12d10a13c9ee6e0c8e7478593608880d1a558060.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2766462.2767693"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

CMBPR: Category-Aided Multi-Channel Bayesian Personalized Ranking for Short Video Recommendation

Xichen Wang, Chen Gao, Jingtao Ding, Yong Li, Depeng Jin
<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;
This paper proposes a content-based recommendation algorithm Category-aided Multi-channel Bayesian Personalized Ranking (CMBPR) for short video recommendation, which integrates users' rich preference information  ...  INDEX TERMS Video recommender system, Bayesian personalized ranking, long tail, sampling method.  ...  [28] also propose social Bayesian personalized ranking to leverage social connections to improve personalized ranking for collaborative filtering.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2907494">doi:10.1109/access.2019.2907494</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/drmd2gsp5jgnphhbqfhquqhr44">fatcat:drmd2gsp5jgnphhbqfhquqhr44</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429032026/https://ieeexplore.ieee.org/ielx7/6287639/8600701/08689028.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/08/53/08531051fb8e8ff7fbf9267b2b9a2bdd6ada5568.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2019.2907494"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

A dual-perspective latent factor model for group-aware social event recommendation

Yogesh Jhamb, Yi Fang
<span title="">2017</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/v5dch4enzne6phusiwdh25za24" style="color: black;">Information Processing &amp; Management</a> </i> &nbsp;
Due to the huge volume of events available in EBSNs, event recommendation becomes essential for users to find suitable events to attend.  ...  Pairwise learning is used to exploit unobserved RSVPs by modeling the individual probability of preference via Logistic and Probit sigmoid functions.  ...  Matrix factorization has been adapted to learn from relative pairwise preferences rather than absolute ones.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.ipm.2017.01.001">doi:10.1016/j.ipm.2017.01.001</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/l6tpel7d5vg6hih4tgmpfk7uyq">fatcat:l6tpel7d5vg6hih4tgmpfk7uyq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170708092707/http://www.cse.scu.edu:80/~yfang/dual-Fang.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/01/3b/013be67e73b1410047d5cf978ed3287fa263a4c6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.ipm.2017.01.001"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Combining latent factor model with location features for event-based group recommendation

Wei Zhang, Jianyong Wang, Wei Feng
<span title="">2013</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fqqihtxlu5bvfaqxjyvqcob35a" style="color: black;">Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD &#39;13</a> </i> &nbsp;
In this paper, we propose a method called Pairwise Tag-enhAnced and featuRe-based Matrix factorIzation for Group recommendAtioN (PTARMIGAN), which considers location features, social features, and implicit  ...  These characteristics determine that previously proposed approaches for group recommendation cannot be adapted to the new problem easily as they ignore the geographical influence and other explicit features  ...  Typically, learning to rank methods have been classified into three categories: pointwise approach, pairwise approach, and listwise approach.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2487575.2487646">doi:10.1145/2487575.2487646</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/kdd/ZhangWF13.html">dblp:conf/kdd/ZhangWF13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/aafhdtqalvf6jgr3b3xdnzebei">fatcat:aafhdtqalvf6jgr3b3xdnzebei</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170830030043/http://chbrown.github.io/kdd-2013-usb/kdd/p910.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/f0/83/f08308ff0a5b4be0d2996a4f21545f7b121636e0.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2487575.2487646"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Evaluating Stochastic Rankings with Expected Exposure [article]

Fernando Diaz and Bhaskar Mitra and Michael D. Ekstrand and Asia J. Biega and Ben Carterette
<span title="2020-04-27">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Importantly, this methodology relaxes classic information retrieval assumptions, allowing a system, in response to a query, to produce a distribution over rankings instead of a single fixed ranking.  ...  We argue that this principle is desirable for many retrieval objectives and scenarios, including topical diversity and fair ranking.  ...  As such, a classic pointwise learning to rank model [9] with Plackett-Luce randomization may be an effective approach for expected exposure.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.13157v1">arXiv:2004.13157v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hlj3mwt36fdivpkdp27b5nst2q">fatcat:hlj3mwt36fdivpkdp27b5nst2q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200925024758/https://arxiv.org/pdf/2004.13157v1.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/b7/70b7aa93a3d537bc611a4762bcdd18638e4cbd8b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2004.13157v1" 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 unified Neural Network Approach to E-CommerceRelevance Learning [article]

Yunjiang Jiang, Yue Shang, Rui Li, Wen-Yun Yang, Guoyu Tang, Chaoyi Ma, Yun Xiao, Eric Zhao
<span title="2021-04-26">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Several general enhancements were applied to further optimize eval/test metrics, including Siamese pairwise architecture, random batch negative co-training, and point-wise fine-tuning.  ...  We describe a highly-scalable feed-forward neural model to provide relevance score for (query, item) pairs, using only user query and item title as features, and both user click feedback as well as limited  ...  We categorize these models as traditional learning to rank based methods and deep learning based methods.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2104.12302v1">arXiv:2104.12302v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hm5ciew3jja4lochvs45etq5uq">fatcat:hm5ciew3jja4lochvs45etq5uq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429110300/https://arxiv.org/pdf/2104.12302v1.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/7c/ae7c58ebe378365df3118f6f3dbdb050b978b149.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2104.12302v1" 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>
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