Chen Weijian

Ma Muyang

Ren Pengjie

Sun Yueming

Yang Xun

Zhang Yongfeng

Zhu Hao

English

Natural language processing is becoming more and more important in recommender systems. This half day workshop explores challenges and potential research directions in Recommender Systems (RSs) combining Natural Language Processing (NLP). The focus will be on stimulating discussions around how to combine natural language processing technologies with recommendation. We welcome theoretical, experimental, and methodological studies that leverage NLP technologies to advance recommender systems, as well as emphasize the applicability in practical applications. The workshop aims to bring together a diverse set of researchers and practitioners interested in investigating the interaction between NLP and RSs to develop more intelligent RSs.</p

Ren, P.

Ren, Z.

Sun, F.

He, X.

Yin, D.

de Rijke, M.

International Migration, Integration and Social Cohesion online publications

UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl)UvA-DARE (Digital Academic Repository)NLP4REC: The WSDM 2020 Workshop on Natural Language Processing forRecommendationsRen, P.; Ren, Z.; Sun, F.; He, X.; Yin, D.; de Rijke, M.DOI10.1145/3336191.3371884Publication date2020Document VersionFinal published versionPublished inWSDM '20Link to publicationCitation for published version (APA):Ren, P., Ren, Z., Sun, F., He, X., Yin, D., & de Rijke, M. (2020). NLP4REC: The WSDM 2020Workshop on Natural Language Processing for Recommendations. In WSDM '20:proceedings of the 13th International Conference on Web Search and Data Mining : February3-7, 2020, Houston, TX, USA (pp. 907-908). Association for Computing Machinery.https://doi.org/10.1145/3336191.3371884General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s)and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an opencontent license (like Creative Commons).Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, pleaselet the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the materialinaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letterto: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. Youwill be contacted as soon as possible.Download date:10 Mar 2023NLP4REC: The WSDM 2020 Workshop on Natural LanguageProcessing for RecommendationsPengjie RenUniversity of Amsterdamp.ren@uva.nlZhaochun RenShandong Universityzhaochun.ren@sdu.edu.cnFei SunAlibaba Groupofey.sunfei@gmail.comXiangnan HeUniversity of Science and Technologyof Chinaxiangnanhe@gmail.comDawei YinJD.comyindawei@acm.orgMaarten de RijkeUniversity of Amsterdamderijke@uva.nlABSTRACTNatural language processing is becoming more and more import-ant in recommender systems. This half day workshop exploreschallenges and potential research directions in Recommender Sys-tems (RSs) combining Natural Language Processing (NLP). Thefocus will be on stimulating discussions around how to combinenatural language processing technologies with recommendation.We welcome theoretical, experimental, and methodological studiesthat leverage NLP technologies to advance recommender systems,as well as emphasize the applicability in practical applications. Theworkshop aims to bring together a diverse set of researchers andpractitioners interested in investigating the interaction betweenNLP and RSs to develop more intelligent RSs.website: https://wsdm2020-nlp4rec.github.io1 MOTIVATION AND FIT FORWSDMStudied for decades, recommender systems (RSs) attempt to identifythe most relevant piece of information solely based on an impli-citly expressed information need reflected in user-item interactionbehaviors. Most studies focus on optimizing the recommendationor ranking criteria, assuming that people are more likely to trustsources ranked higher in recommendation results. However, achiev-ing higher recommendation performance is far from meeting usersatisfaction. There are many other fundamental issues, such asexplainability, privacy. Recently, there are an increasing numberof studies trying to address these issues by combining NaturalLanguage Processing (NLP) technologies with RSs, e.g., addressingrecommendation explainability with knowledge graph reasoningand review generation [7].The motivation of this workshop is to bring together a diverseset of researchers and practitioners who are interested in exploringfundamental issues in RSs, and/or emphasizing the applicability inpractical applications (e.g., e-commence) by leveraging the mostrecent advances in NLP. We see a large space for discussion andfuture research in the development of more intelligent RSs.Permission to make digital or hard copies of part or all of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for third-party components of this work must be honored.For all other uses, contact the owner/author(s).WSDM ’20, February 3–7, 2020, Houston, Texas, USA© 2020 Copyright held by the owner/author(s).ACM ISBN 978-1-4503-5940-5/19/02.https://doi.org/10.1145/3289600.3291375Given the current ubiquitous use of a variety of RSs, they havebeen and still are hot research topics in information retrieval or datamining related communities. As one of the premier conferenceson web-inspired research involving search and data mining, theWSDM community has the responsibility to care about the broaderimpact and implications of RSs that we research and the systemsthat we build in academia and industry.2 THEME AND PURPOSE OF THEWORKSHOPRecSys, SIGIR and WSDM all featured the workshops on recom-mender systems, i.e., KaRS1, RECOVER2, EARS3, CARS-BDA4. Theseworkshops focus on either recommender systems only or the inter-disciplinary researches with a particular task of natural languageprocessing.NLP4REC 2020 will be a forum for discussion about the chal-lenges in applying NLP technologies to real recommendation ap-plications as well as the theory behind the them. The purpose ofthis workshop is to establish a bridge for communication betweenindustrial researchers and academic researchers, and provide anopportunity for people to exchange ideas, and discuss the futuredirections. The themes of focus for the workshop include but notlimited to the applications of NLP technologies in the followingrecommendation scenarios.Knowledge-aware Recommendation. In most cases, RSs usu-ally suffer from the sparsity of user-item interactions and the coldstart problem. Recent studies indicate that an effective way to al-leviate these limitations is to incorporate side informations, e.g.,user profiles, item profiles. Knowledge graphs are ubiquitous inreality to represent the side informations and their relationships,e.g., social networks, medication networks, e-commerce networks.Deep learning architectures on graph-structured data have achievedremarkable performance in many NLP tasks [10]. This wave of re-search has also aroused great interest among researches in RSs [1].Explainable Recommendation. Explainable recommendationaims to improve the transparency, persuasiveness of RSs by provid-ing explanations to users or developers, which helps them to under-stand why certain items are recommended [9]. Much progress has1https://recsys.acm.org/recsys18/kars/2https://recsys.acm.org/recsys18/recover/3http://www.sigir.org/sigir2019/program/workshops/ears/4http://wise-conferences.org/CARS-BDA/CARS-BDA.htmlWorkshop Summary  WSDM ’20, February 3–7, 2020, Houston, TX, USA907been made to promote recommendation explainability by paying at-tend to certain user/item attributes [8]. More intelligent RSs shouldbe able to generate natural language explanations [2]. However,there is still a long way to go towards this goal.Conversational Recommendation. One of the key aspects inRSs is how to capture user interests precisely. Instead of relyingmerely on mining user interests from previous user-item interac-tion behaviors, conversational recommendation provides an altern-ative strategy through conducting conversations with the usersdirectly [6]. It is an interesting setting for the scientific explorationof both conversation and recommendation modeling.Sequential Recommendation. Sequential recommendation is aneffective paradigm to capture the dynamics of RSs by modeling theuser-item interactions as a sequence. Owing to the shared sequentialcharacteristics with natural languages, many NLP technologiesor mechanisms have been successfully applied to model variousscenarios in sequential recommendations [3–5].3 LIST OF ORGANIZERS• Dr. Pengjie Ren is postdoctoral researcher at the Informationand Language Processing Systems (ILPS) group, Universityof Amsterdam. His current research is focused on recom-mender systems and conversational agents. He has publishedmore than 30 research papers in conferences and journalsincluding SIGIR, WWW, EMNLP, AAAI, TOIS, TKDE, etc.He serves as a program committee member of several top-tier venues (such as SIGIR, WWW, AAAI, WSDM) and theregular reviewer for journals including TOIS, TKDE, TKDD,etc.• Prof. Dr. Zhaochun Ren is working as a professor at Shan-dong University. Prior to this, he worked as a senior researchmanager at JD.com and a research associate in UniversityCollege London. Zhaochun got his PhD from Universityof Amsterdam, supervised by Prof. Dr. Maarten de Rijke.Zhaochun is interested in information retrieval, social mediamining and content analysis in e-commerce.• Dr. Fei Sun is a research scientist in Search & Recommend-ation Group at Alibaba. Prior to joining Alibaba, he ob-tained Ph.D. (2013) from Institute of Computing Technology,Chinese Academy of Sciences supervised by Prof. JiafengGuo and Prof. Jun Xu. His current research is focused on textrepresentation learning and neural models for recommendersystems. He has published about 20 research papers in topconferences including SIGIR, ACL, WWW, KDD, EMNLP,AAAI, IJCAI. He also serves as a program committee mem-ber of several top-tier venues (such as SIGIR, ACL, AAAI,CIKM, and EMNLP).• Prof. Dr. Xiangnan He is a professor with the Universityof Science and Technology of China (USTC). He receivedhis Ph.D. in Computer Science from National University ofSingapore (NUS) in 2016. His research interests span inform-ation retrieval, data mining, and multi-media analytics. Hehas over 60 publications appeared in several top conferencessuch as SIGIR, WWW, KDD, and MM, and journals includ-ing TKDE and TOIS. His work on recommender systemshas received the Best Paper Award Honourable Mention inWWW 2018 and ACM SIGIR 2016. Moreover, he has servedas the PC chair of CCIS 2019, area chair of MM 2019 andCIKM 2019, and PC member for several top conferences in-cluding SIGIR, WWW, KDD etc., and the regular reviewerfor journals including TKDE, TOIS, TMM, etc.• Dr. Dawei Yin is a senior Director of Research at JD.com. Heis managing the recommendation engineering team, buildingthe uni ed recommender system of JD.com, one of the largestonline retailers in China. He also founded JD.com Data Sci-ence Lab, leading the science efforts for recommendation,search, metrics and knowledge graph, etc.• Prof. Dr. Maarten de Rijke is a University Professor of Artifi-cial Intelligence and Information Retrieval at the Universityof Amsterdam. He works on different types of technologythat connect people to information, both its algorithmic un-derpinnings, its uses in domains ranging from news andretail to security and well-being and its broader implications.Maarten is a member of the Royal Dutch Academy of Artsand Sciences (KNAW) and the founding director of the na-tional Innovation Center for Artificial Intelligence. He haspreviously helped to organize various conferences (CLEF,ECIR, ICTIR, SIGIR, WSDM) and workshops (at CIKM, ECIR,SIGIR, WWW).ACKNOWLEDGMENTSThis work is supported by Ahold Delhaize, the Association of Uni-versities in the Netherlands (VSNU), the Innovation Center forArtificial Intelligence (ICAI), the Natural Science Foundation ofChina (61672324, 61672322, 61972234, 61902219, 6197237), the Nat-ural Science Foundation of Shandong province (2016ZRE27468), theTencent AI Lab Rhino-Bird Focused Research Program (JR201932),and the Fundamental Research Funds of Shandong University. Allcontent represents the opinion of the authors, which is not ne-cessarily shared or endorsed by their respective employers and/orsponsors.REFERENCES[1] Weijian Chen, Yulong Gu, Zhaochun Ren, Xiangnan He, Hongtao Xie, Tong Guo,Dawei Yin, and Yongdong Zhang. 2019. Semi-supervised User Profiling withHeterogeneous Graph Attention Networks. In IJCAI ’19.[2] Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma, and Maarten deRijke. 2019. Explainable Outfit Recommendation with Joint Outfit Matching andComment Generation. TKDE (2019).[3] Muyang Ma, Pengjie Ren, Yujie Lin, Zhumin Chen, Jun Ma, and Maarten deRijke. 2019. PI-Net: A Parallel Information-sharing Network for Shared-accountCross-domain Sequential Recommendations. In SIGIR ’19.[4] Pengjie Ren, Zhumin Chen, Jing Li, Zhaochun Ren, Jun Ma, and Maarten deRijke. 2019. RepeatNet: A Repeat Aware Neural Recommendation Machine forSession-based Recommendation. In AAAI ’19.[5] Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Ji-ang. 2019. BERT4Rec: Sequential Recommendation with Bidirectional EncoderRepresentations from Transformer. In CIKM ’19.[6] Yueming Sun and Yi Zhang. 2018. Conversational Recommender System. InSIGIR ’18.[7] XiangWang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019. KGAT:Knowledge Graph Attention Network for Recommendation. In KDD ’19.[8] Xun Yang, Xiangnan He, Xiang Wang, Yunshan Ma, Fuli Feng, Meng Wang, andTat-Seng Chua. 2019. Interpretable Fashion Matching with Rich Attributes. InSIGIR ’19.[9] Yongfeng Zhang and Xu Chen. 2018. Explainable Recommendation: A Surveyand New Perspectives. CoRR (2018).[10] Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-Seng Chua, and Maosong Sun. 2019.Graph Neural Networks with Generated Parameters for Relation Extraction. InACL ’19.Workshop Summary  WSDM ’20, February 3–7, 2020, Houston, TX, USA908

NLP4REC: The WSDM 2020 Workshop on Natural Language Processing for Recommendations

UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl)UvA-DARE (Digital Academic Repository)NLP4REC: The WSDM 2020 Workshop on Natural Language Processing forRecommendationsRen, P.; Ren, Z.; Sun, F.; He, X.; Yin, D.; de Rijke, M.DOI10.1145/3336191.3371884Publication date2020Document VersionFinal published versionPublished inWSDM '20Link to publicationCitation for published version (APA):Ren, P., Ren, Z., Sun, F., He, X., Yin, D., & de Rijke, M. (2020). NLP4REC: The WSDM 2020Workshop on Natural Language Processing for Recommendations. In WSDM '20:proceedings of the 13th International Conference on Web Search and Data Mining : February3-7, 2020, Houston, TX, USA (pp. 907-908). Association for Computing Machinery.https://doi.org/10.1145/3336191.3371884General rightsIt is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s)and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an opencontent license (like Creative Commons).Disclaimer/Complaints regulationsIf you believe that digital publication of certain material infringes any of your rights or (privacy) interests, pleaselet the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the materialinaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letterto: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. Youwill be contacted as soon as possible.Download date:11 Feb 2023NLP4REC: The WSDM 2020 Workshop on Natural LanguageProcessing for RecommendationsPengjie RenUniversity of Amsterdamp.ren@uva.nlZhaochun RenShandong Universityzhaochun.ren@sdu.edu.cnFei SunAlibaba Groupofey.sunfei@gmail.comXiangnan HeUniversity of Science and Technologyof Chinaxiangnanhe@gmail.comDawei YinJD.comyindawei@acm.orgMaarten de RijkeUniversity of Amsterdamderijke@uva.nlABSTRACTNatural language processing is becoming more and more import-ant in recommender systems. This half day workshop exploreschallenges and potential research directions in Recommender Sys-tems (RSs) combining Natural Language Processing (NLP). Thefocus will be on stimulating discussions around how to combinenatural language processing technologies with recommendation.We welcome theoretical, experimental, and methodological studiesthat leverage NLP technologies to advance recommender systems,as well as emphasize the applicability in practical applications. Theworkshop aims to bring together a diverse set of researchers andpractitioners interested in investigating the interaction betweenNLP and RSs to develop more intelligent RSs.website: https://wsdm2020-nlp4rec.github.io1 MOTIVATION AND FIT FORWSDMStudied for decades, recommender systems (RSs) attempt to identifythe most relevant piece of information solely based on an impli-citly expressed information need reflected in user-item interactionbehaviors. Most studies focus on optimizing the recommendationor ranking criteria, assuming that people are more likely to trustsources ranked higher in recommendation results. However, achiev-ing higher recommendation performance is far from meeting usersatisfaction. There are many other fundamental issues, such asexplainability, privacy. Recently, there are an increasing numberof studies trying to address these issues by combining NaturalLanguage Processing (NLP) technologies with RSs, e.g., addressingrecommendation explainability with knowledge graph reasoningand review generation [7].The motivation of this workshop is to bring together a diverseset of researchers and practitioners who are interested in exploringfundamental issues in RSs, and/or emphasizing the applicability inpractical applications (e.g., e-commence) by leveraging the mostrecent advances in NLP. We see a large space for discussion andfuture research in the development of more intelligent RSs.Permission to make digital or hard copies of part or all of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for third-party components of this work must be honored.For all other uses, contact the owner/author(s).WSDM ’20, February 3–7, 2020, Houston, Texas, USA© 2020 Copyright held by the owner/author(s).ACM ISBN 978-1-4503-5940-5/19/02.https://doi.org/10.1145/3289600.3291375Given the current ubiquitous use of a variety of RSs, they havebeen and still are hot research topics in information retrieval or datamining related communities. As one of the premier conferenceson web-inspired research involving search and data mining, theWSDM community has the responsibility to care about the broaderimpact and implications of RSs that we research and the systemsthat we build in academia and industry.2 THEME AND PURPOSE OF THEWORKSHOPRecSys, SIGIR and WSDM all featured the workshops on recom-mender systems, i.e., KaRS1, RECOVER2, EARS3, CARS-BDA4. Theseworkshops focus on either recommender systems only or the inter-disciplinary researches with a particular task of natural languageprocessing.NLP4REC 2020 will be a forum for discussion about the chal-lenges in applying NLP technologies to real recommendation ap-plications as well as the theory behind the them. The purpose ofthis workshop is to establish a bridge for communication betweenindustrial researchers and academic researchers, and provide anopportunity for people to exchange ideas, and discuss the futuredirections. The themes of focus for the workshop include but notlimited to the applications of NLP technologies in the followingrecommendation scenarios.Knowledge-aware Recommendation. In most cases, RSs usu-ally suffer from the sparsity of user-item interactions and the coldstart problem. Recent studies indicate that an effective way to al-leviate these limitations is to incorporate side informations, e.g.,user profiles, item profiles. Knowledge graphs are ubiquitous inreality to represent the side informations and their relationships,e.g., social networks, medication networks, e-commerce networks.Deep learning architectures on graph-structured data have achievedremarkable performance in many NLP tasks [10]. This wave of re-search has also aroused great interest among researches in RSs [1].Explainable Recommendation. Explainable recommendationaims to improve the transparency, persuasiveness of RSs by provid-ing explanations to users or developers, which helps them to under-stand why certain items are recommended [9]. Much progress has1https://recsys.acm.org/recsys18/kars/2https://recsys.acm.org/recsys18/recover/3http://www.sigir.org/sigir2019/program/workshops/ears/4http://wise-conferences.org/CARS-BDA/CARS-BDA.htmlWorkshop Summary  WSDM ’20, February 3–7, 2020, Houston, TX, USA907been made to promote recommendation explainability by paying at-tend to certain user/item attributes [8]. More intelligent RSs shouldbe able to generate natural language explanations [2]. However,there is still a long way to go towards this goal.Conversational Recommendation. One of the key aspects inRSs is how to capture user interests precisely. Instead of relyingmerely on mining user interests from previous user-item interac-tion behaviors, conversational recommendation provides an altern-ative strategy through conducting conversations with the usersdirectly [6]. It is an interesting setting for the scientific explorationof both conversation and recommendation modeling.Sequential Recommendation. Sequential recommendation is aneffective paradigm to capture the dynamics of RSs by modeling theuser-item interactions as a sequence. Owing to the shared sequentialcharacteristics with natural languages, many NLP technologiesor mechanisms have been successfully applied to model variousscenarios in sequential recommendations [3–5].3 LIST OF ORGANIZERS• Dr. Pengjie Ren is postdoctoral researcher at the Informationand Language Processing Systems (ILPS) group, Universityof Amsterdam. His current research is focused on recom-mender systems and conversational agents. He has publishedmore than 30 research papers in conferences and journalsincluding SIGIR, WWW, EMNLP, AAAI, TOIS, TKDE, etc.He serves as a program committee member of several top-tier venues (such as SIGIR, WWW, AAAI, WSDM) and theregular reviewer for journals including TOIS, TKDE, TKDD,etc.• Prof. Dr. Zhaochun Ren is working as a professor at Shan-dong University. Prior to this, he worked as a senior researchmanager at JD.com and a research associate in UniversityCollege London. Zhaochun got his PhD from Universityof Amsterdam, supervised by Prof. Dr. Maarten de Rijke.Zhaochun is interested in information retrieval, social mediamining and content analysis in e-commerce.• Dr. Fei Sun is a research scientist in Search & Recommend-ation Group at Alibaba. Prior to joining Alibaba, he ob-tained Ph.D. (2013) from Institute of Computing Technology,Chinese Academy of Sciences supervised by Prof. JiafengGuo and Prof. Jun Xu. His current research is focused on textrepresentation learning and neural models for recommendersystems. He has published about 20 research papers in topconferences including SIGIR, ACL, WWW, KDD, EMNLP,AAAI, IJCAI. He also serves as a program committee mem-ber of several top-tier venues (such as SIGIR, ACL, AAAI,CIKM, and EMNLP).• Prof. Dr. Xiangnan He is a professor with the Universityof Science and Technology of China (USTC). He receivedhis Ph.D. in Computer Science from National University ofSingapore (NUS) in 2016. His research interests span inform-ation retrieval, data mining, and multi-media analytics. Hehas over 60 publications appeared in several top conferencessuch as SIGIR, WWW, KDD, and MM, and journals includ-ing TKDE and TOIS. His work on recommender systemshas received the Best Paper Award Honourable Mention inWWW 2018 and ACM SIGIR 2016. Moreover, he has servedas the PC chair of CCIS 2019, area chair of MM 2019 andCIKM 2019, and PC member for several top conferences in-cluding SIGIR, WWW, KDD etc., and the regular reviewerfor journals including TKDE, TOIS, TMM, etc.• Dr. Dawei Yin is a senior Director of Research at JD.com. Heis managing the recommendation engineering team, buildingthe uni ed recommender system of JD.com, one of the largestonline retailers in China. He also founded JD.com Data Sci-ence Lab, leading the science efforts for recommendation,search, metrics and knowledge graph, etc.• Prof. Dr. Maarten de Rijke is a University Professor of Artifi-cial Intelligence and Information Retrieval at the Universityof Amsterdam. He works on different types of technologythat connect people to information, both its algorithmic un-derpinnings, its uses in domains ranging from news andretail to security and well-being and its broader implications.Maarten is a member of the Royal Dutch Academy of Artsand Sciences (KNAW) and the founding director of the na-tional Innovation Center for Artificial Intelligence. He haspreviously helped to organize various conferences (CLEF,ECIR, ICTIR, SIGIR, WSDM) and workshops (at CIKM, ECIR,SIGIR, WWW).ACKNOWLEDGMENTSThis work is supported by Ahold Delhaize, the Association of Uni-versities in the Netherlands (VSNU), the Innovation Center forArtificial Intelligence (ICAI), the Natural Science Foundation ofChina (61672324, 61672322, 61972234, 61902219, 6197237), the Nat-ural Science Foundation of Shandong province (2016ZRE27468), theTencent AI Lab Rhino-Bird Focused Research Program (JR201932),and the Fundamental Research Funds of Shandong University. Allcontent represents the opinion of the authors, which is not ne-cessarily shared or endorsed by their respective employers and/orsponsors.REFERENCES[1] Weijian Chen, Yulong Gu, Zhaochun Ren, Xiangnan He, Hongtao Xie, Tong Guo,Dawei Yin, and Yongdong Zhang. 2019. Semi-supervised User Profiling withHeterogeneous Graph Attention Networks. In IJCAI ’19.[2] Yujie Lin, Pengjie Ren, Zhumin Chen, Zhaochun Ren, Jun Ma, and Maarten deRijke. 2019. Explainable Outfit Recommendation with Joint Outfit Matching andComment Generation. TKDE (2019).[3] Muyang Ma, Pengjie Ren, Yujie Lin, Zhumin Chen, Jun Ma, and Maarten deRijke. 2019. PI-Net: A Parallel Information-sharing Network for Shared-accountCross-domain Sequential Recommendations. In SIGIR ’19.[4] Pengjie Ren, Zhumin Chen, Jing Li, Zhaochun Ren, Jun Ma, and Maarten deRijke. 2019. RepeatNet: A Repeat Aware Neural Recommendation Machine forSession-based Recommendation. In AAAI ’19.[5] Fei Sun, Jun Liu, Jian Wu, Changhua Pei, Xiao Lin, Wenwu Ou, and Peng Ji-ang. 2019. BERT4Rec: Sequential Recommendation with Bidirectional EncoderRepresentations from Transformer. In CIKM ’19.[6] Yueming Sun and Yi Zhang. 2018. Conversational Recommender System. InSIGIR ’18.[7] XiangWang, Xiangnan He, Yixin Cao, Meng Liu, and Tat-Seng Chua. 2019. KGAT:Knowledge Graph Attention Network for Recommendation. In KDD ’19.[8] Xun Yang, Xiangnan He, Xiang Wang, Yunshan Ma, Fuli Feng, Meng Wang, andTat-Seng Chua. 2019. Interpretable Fashion Matching with Rich Attributes. InSIGIR ’19.[9] Yongfeng Zhang and Xu Chen. 2018. Explainable Recommendation: A Surveyand New Perspectives. CoRR (2018).[10] Hao Zhu, Yankai Lin, Zhiyuan Liu, Jie Fu, Tat-Seng Chua, and Maosong Sun. 2019.Graph Neural Networks with Generated Parameters for Relation Extraction. InACL ’19.Workshop Summary  WSDM ’20, February 3–7, 2020, Houston, TX, USA908

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