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Aggregation of Preferences in Recommender Systems [chapter]

Gleb Beliakov, Tomasa Calvo, Simon James
<span title="2010-10-05">2010</span> <i title="Springer US"> Recommender Systems Handbook </i> &nbsp;
Introduction Aggregation of preferences, criteria or similarities happens at various stages in recommender systems.  ...  de la Computación, Universidad de Alcalá 28871-Alcalá de Henares (Madrid), Spain. e-mail: tomasa.calvo@uah.es Types of aggregation in Recommender Systems In general, recommender systems (RS) guide users  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-0-387-85820-3_22">doi:10.1007/978-0-387-85820-3_22</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cpyhyno2lbgbxjruj4zzxmx3fm">fatcat:cpyhyno2lbgbxjruj4zzxmx3fm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809150120/http://www.ise.bgu.ac.il/faculty/liorr/recsyshb/chAggregation.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/a4/74/a47420a0da50e37deda87972f4a31ee6b8c01cfb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-0-387-85820-3_22"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Design and evaluation of a group recommender system

Toon De Pessemier, Simon Dooms, Luc Martens
<span title="">2012</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vxvef3lblbcxbliuugiju5sify" style="color: black;">Proceedings of the sixth ACM conference on Recommender systems - RecSys &#39;12</a> </i> &nbsp;
In this paper, we present a recommender system for audio-visual content that generates suggestions for groups of people (such as families or friends) in the home environment.  ...  in order to maximize the efficiency of the group recommendations.  ...  Three group recommendation strategies are available in the current implementation of the video recommender system: aggregating recommendations, aggregating preferences, or a switching strategy that combines  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2365952.2366000">doi:10.1145/2365952.2366000</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/recsys/PessemierDM12.html">dblp:conf/recsys/PessemierDM12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pvsmrgjadvfhdd753rk6t33znq">fatcat:pvsmrgjadvfhdd753rk6t33znq</a> </span>
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Comparison of group recommendation algorithms

Toon De Pessemier, Simon Dooms, Luc Martens
<span title="2013-06-30">2013</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7inqmh346zfjjizjyieh7ijtma" style="color: black;">Multimedia tools and applications</a> </i> &nbsp;
In recent years recommender systems have become the common tool to handle the information overload problem of educational and informative web sites, content delivery systems, and online shops.  ...  Consequently for (commercial) group recommender systems, the grouping strategy and algorithm have to be chosen carefully in order to optimize the desired quality metrics of the group recommendations.  ...  The system detects the presence of people in the room by the radio frequency ID badges of every user and generates recommendations by aggregating the votes of all users (cfr. aggregating preferences strategy  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11042-013-1563-0">doi:10.1007/s11042-013-1563-0</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qjzo6fhgurfsvp2im36fpl2ioq">fatcat:qjzo6fhgurfsvp2im36fpl2ioq</a> </span>
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Context-Aware Recommendation by Aggregating User Context

Dongmin Shin, Jae-won Lee, Jongheum Yeon, Sang-goo Lee
<span title="">2009</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/65iimf2onnea5mg545qvlbna3m" style="color: black;">2009 IEEE Conference on Commerce and Enterprise Computing</a> </i> &nbsp;
Moreover, by aggregating the context information, we can improve the quality of recommendation.  ...  Traditional recommendation approaches do not consider the changes of user preferences according to context.  ...  Though the user A"s preferences are changed according to the context, conventional recommendation systems do not reflect the change in recommendation.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/cec.2009.38">doi:10.1109/cec.2009.38</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/wecwis/ShinLYL09.html">dblp:conf/wecwis/ShinLYL09</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gj4bsfttorcefooq2ktkykpoyi">fatcat:gj4bsfttorcefooq2ktkykpoyi</a> </span>
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A PERSONALITY-BASED AGGREGATION TECHNIQUE FOR GROUP RECOMMENDATION

Emre YALÇIN, Alper BİLGE
<span title="2020-12-28">2020</span> <i title="Anadolu Universitesi Bilim ve Teknoloji Dergisi-A: Uygulamali Bilimler ve Muhendislik"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/cesfrpfxtrcojbsduayhk7xwiu" style="color: black;">Eskişehir Technical University Journal of Science and Technology A - Applied Sciences and Engineering</a> </i> &nbsp;
The main goal of a group recommender system is to provide appropriate referrals to a group of users sharing common interests rather than individuals.  ...  Such group referrals are commonly produced by utilizing aggregation techniques that analyze the propensities of the whole group by combining the preferences of the users in the group.  ...  In such systems, group recommendations are generally produced based on group ratings, which are determined by aggregating the preferences of members in the group.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.18038/estubtda.743422">doi:10.18038/estubtda.743422</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/fu4xah3bvndo5agp6rquolndt4">fatcat:fu4xah3bvndo5agp6rquolndt4</a> </span>
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Dynamic Group Recommendation Algorithm Based on Member Activity Level

Junjie Jia, Yewang Yao, Zhipeng Lei, Pengtao Liu, Sebastiano Fabio Schifano
<span title="2021-08-28">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/fw4azkpu65d2thmrwfkoawyxse" style="color: black;">Scientific Programming</a> </i> &nbsp;
at the recommended moment, and preference is aggregated based on the recent activity level of members.  ...  Existing group recommender systems focus too much on member preferences and ignore the impact of member activity level on recommendation results.  ...  of Gansu Province (Grant no. 2019A-006).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/1969118">doi:10.1155/2021/1969118</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3ykp4uu3ffdgneypzi6pdyalre">fatcat:3ykp4uu3ffdgneypzi6pdyalre</a> </span>
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A Recommender System Based on Multi-Criteria Aggregation

Soumana Fomba, Pascale Zarate, Marc Kilgour, Guy Camilleri, Jacqueline Konate, Fana Tangara
<span title="">2017</span> <i title="IGI Global"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/xv3xbuqjijfbfgfzfkw2drfsc4" style="color: black;">International Journal of Decision Support System Technology</a> </i> &nbsp;
Then we develop a multicriteria recommender system, STROMa (SysTem of RecOmmendation Multi-criteria), to support decisions by aggregating measures of performance contained in a performance matrix.  ...  We review briefly tools of Multi-Criteria Decision Analysis (MCDA), including aggregation operators, that could be the basis for a recommender system.  ...  Recommendation systems Recommendation systems are as interactive decision support systems to take into account evolving preferences of users with a view to make recommendations.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4018/ijdsst.2017100101">doi:10.4018/ijdsst.2017100101</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rmluljelljghvbed7u3d3kye4y">fatcat:rmluljelljghvbed7u3d3kye4y</a> </span>
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TruGRC: Trust-Aware Group Recommendation with Virtual Coordinators

Ximeng Wang, Yun Liu, Jie Lu, Fei Xiong, Guangquan Zhang
<span title="">2019</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/idqeix2ukbd5fbgjjdkjmaj3ka" style="color: black;">Future generations computer systems</a> </i> &nbsp;
• We model trust information and personal influence in group recommender systems. • Comprehensive experiments indicate the proposed method outperforms most of baselines.  ...  h i g h l i g h t s • We integrate the result and profile aggregation strategies to improve group recommendation. • We introduce a virtual coordinator to create a balanced set of group recommendations.  ...  Moreover, most of current studies improve group recommendation only based on a single aggregation strategy and explicit trust information is still ignored in group recommender systems.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.future.2018.11.030">doi:10.1016/j.future.2018.11.030</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/a4xajshvznbm5cgs4me23btbqe">fatcat:a4xajshvznbm5cgs4me23btbqe</a> </span>
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Enhancing Recommendation using Ranking in Multidimensional Space

R. Suganya Devi, A. P. Chitra, D. Manjula
<span title="2016-07-21">2016</span> <i title="Indian Society for Education and Environment"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wffwpj3q45g5zfjzfeyagk5uea" style="color: black;">Indian Journal of Science and Technology</a> </i> &nbsp;
Search engines play their vital part in building ranking algorithms. Product Recommendation systems is a business activity which involves ranking to fulfill customer needs among the competitors.  ...  (TES) approach, a rank aggregation algorithm which employs B+ trees for indexing.  ...  Anaya 1 analyze the recommender system in collaborative environment using the influence diagram, and how Collaborative filtering is used in recommender system.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.17485/ijst/2016/v9i27/89853">doi:10.17485/ijst/2016/v9i27/89853</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/llqifxetwndsdmxe7frlsfvn3y">fatcat:llqifxetwndsdmxe7frlsfvn3y</a> </span>
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Observing Group Decision Making Processes

Amra Delic, Julia Neidhardt, Thuy Ngoc Nguyen, Francesco Ricci, Laurens Rook, Hannes Werthner, Markus Zanker
<span title="">2016</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vxvef3lblbcxbliuugiju5sify" style="color: black;">Proceedings of the 10th ACM Conference on Recommender Systems - RecSys &#39;16</a> </i> &nbsp;
Most research on group recommender systems relies on the assumption that individuals have conflicting preferences; in order to generate group recommendations the system should identify a fair way of aggregating  ...  In this paper, we propose to approach group recommendation from the group dynamics perspective and analyze the group decision making process for a particular task (in the travel domain).  ...  STATE OF THE ART Several researches in group recommender systems focused on core preference aggregations algorithms.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2959100.2959168">doi:10.1145/2959100.2959168</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/recsys/DelicNNRRWZ16.html">dblp:conf/recsys/DelicNNRRWZ16</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mamitxr3zvdehhpv5wva25qeeu">fatcat:mamitxr3zvdehhpv5wva25qeeu</a> </span>
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Performance Evaluation of Aggregation-based Group Recommender Systems for Ephemeral Groups

Edgar Ceh-Varela, Huiping Cao, Hady W. Lauw
<span title="2022-06-03">2022</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;
Recommender Systems ( RecSys ) provide suggestions in many decision-making processes.  ...  A wide range of Group Recommender Systems ( GRecSys ) has been developed to aggregate individual preferences to group preferences. We analyze 175 studies related to GRecSys .  ...  This metric represents the percentage of items in the recommendation list that a recommender system can recommend among the number of potential items (i.e., catalog).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3542804">doi:10.1145/3542804</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/u6bgxtenhzbgtlwcuw4nnfmqdi">fatcat:u6bgxtenhzbgtlwcuw4nnfmqdi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220609070606/https://dl.acm.org/doi/pdf/10.1145/3542804" 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/f8/56/f8569d219d661b8ecba8610af7e7e9bf1c31d8aa.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3542804"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Preference Elicitation and Negotiation in a Group Recommender System [chapter]

Jesús Omar Álvarez Márquez, Jürgen Ziegler
<span title="">2015</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;
Individual preferences are continuously aggregated and immediate feedback of the resulting recommendations is provided.  ...  We present a novel approach to group recommender systems that better takes into account the social interaction in a group when formulating, discussing and negotiating the features of the item to be jointly  ...  of group recommender systems.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-22668-2_2">doi:10.1007/978-3-319-22668-2_2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mguiwrujjzar7feegf43kmipzy">fatcat:mguiwrujjzar7feegf43kmipzy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180724025813/https://hal.inria.fr/hal-01599849/file/346942_1_En_2_Chapter.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/43/f0/43f01d778c1432aaa5adf4f6cf62d2463919f3fb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-22668-2_2"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Negative implicit feedback in e-commerce recommender systems

Ladislav Peska, Peter Vojtas
<span title="">2013</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tdrsvefnbzb4blr74qezzvb7l4" style="color: black;">Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics - WIMS &#39;13</a> </i> &nbsp;
In this paper, we imagine the situation of a typical e-commerce portal employing personalized recommendation.  ...  The implicit feedback is generally understood as positive only, however we present several methods how to identify some of the implicit feedback as negative user preference, how to aggregate various feedback  ...  The considered implicit factors are listed in Table 1 , learning of local preference function PREF() is described in section 2.1, aggregating various feedback types (@() method) in section 2.2.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2479787.2479800">doi:10.1145/2479787.2479800</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/wims/PeskaV13.html">dblp:conf/wims/PeskaV13</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/565ki6dpcfgztofbxhyplfm6ke">fatcat:565ki6dpcfgztofbxhyplfm6ke</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170829082111/http://www.ksi.mff.cuni.cz/~peska/wims13.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/22/40/22407b45450e663b7a76b0c300ded8cfb16cd50e.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2479787.2479800"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Building User profiles for Recommender Systems from incomplete preference relations

Luis G. Perez, Manuel Barranco, Luis Martinez
<span title="">2007</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/pmfa2lertvhkneabiydt7skrey" style="color: black;">IEEE International Fuzzy Systems conference proceedings</a> </i> &nbsp;
The most successful tool in this field have been the Recommender Systems.  ...  In this contribution we present a knowledge-based recommender system that is able to compute successful recommendations using a few numbers of examples about the items that the users are looking for.  ...  One of them is the knowledge based recommender systems. In this kind of systems, users state their preferences choosing an example that represents their preferences.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/fuzzy.2007.4295499">doi:10.1109/fuzzy.2007.4295499</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/fuzzIEEE/PerezBM07.html">dblp:conf/fuzzIEEE/PerezBM07</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dodwmink5jak3oln4ec5hrdi3q">fatcat:dodwmink5jak3oln4ec5hrdi3q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20100328220821/http://sinbad2.ujaen.es/sinbad2/files/publicaciones/120.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/df/da/dfda7f08de1ef6df3db772a5117e9f23a885e070.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/fuzzy.2007.4295499"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Preference elicitation for narrowing the recommended list for groups

Lihi Naamani-Dery, Meir Kalech, Lior Rokach, Bracha Shapira
<span title="">2014</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vxvef3lblbcxbliuugiju5sify" style="color: black;">Proceedings of the 8th ACM Conference on Recommender systems - RecSys &#39;14</a> </i> &nbsp;
Our final goal is to output top-preferred items to the group out of the top-recommendations provided by the recommender system ( < ), where one of the items is a necessary winner.  ...  We offer to narrow down the recommended list of items by eliciting the users' actual preferences.  ...  The problem we investigate in this paper is how to narrow down the list of items recommended by the system by eliciting the users' actual preferences.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2645710.2645760">doi:10.1145/2645710.2645760</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/recsys/DeryKRS14.html">dblp:conf/recsys/DeryKRS14</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ut5op4eufzagtgymnxbnitjrw4">fatcat:ut5op4eufzagtgymnxbnitjrw4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20151217051934/http://www.ariel.ac.il/images/stories/site/personalSites/LihiDery/Naamani-Dery_RECSYS_2014.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/33/cd/33cda411a2ecb9767e0f2d14b8caac49e77916e2.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2645710.2645760"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>
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