Abstract is missing.
- 4 Reasons Why Social Media Make Us Vulnerable to ManipulationFilippo Menczer. 1 [doi]
- Bias in Search and Recommender SystemsRicardo Baeza-Yates. 2 [doi]
- "You Really Get Me": Conversational AI Agents That Can Truly Understand and Help UsersMichelle Zhou. 3 [doi]
- A Method to Anonymize Business Metrics to Publishing Implicit Feedback DatasetsYoshifumi Seki, Takanori Maehara. 4-12 [doi]
- A Ranking Optimization Approach to Latent Linear Critiquing for Conversational Recommender SystemsHanze Li, Scott Sanner, Kai Luo, Ga Wu. 13-22 [doi]
- Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair ComparisonZhu Sun, Di Yu, Hui Fang 0002, Jie Yang 0028, Xinghua Qu, Jie Zhang 0002, Cong Geng. 23-32 [doi]
- Cascading Hybrid Bandits: Online Learning to Rank for Relevance and DiversityChang Li 0003, Haoyun Feng, Maarten de Rijke. 33-42 [doi]
- Content-Collaborative Disentanglement Representation Learning for Enhanced RecommendationYin Zhang, Ziwei Zhu, Yun He, James Caverlee. 43-52 [doi]
- Contextual and Sequential User Embeddings for Large-Scale Music RecommendationCasper Hansen, Christian Hansen 0004, Lucas Maystre, Rishabh Mehrotra, Brian Brost, Federico Tomasi, Mounia Lalmas. 53-62 [doi]
- Contextual User Browsing Bandits for Large-Scale Online Mobile RecommendationXu He, Bo An 0001, Yanghua Li, Haikai Chen, Qingyu Guo, Xin Li, Zhirong Wang. 63-72 [doi]
- Debiasing Item-to-Item Recommendations With Small Annotated DatasetsTobias Schnabel, Paul N. Bennett. 73-81 [doi]
- Deconstructing the Filter Bubble: User Decision-Making and Recommender SystemsGuy Aridor, Duarte Gonçalves, Shan Sikdar. 82-91 [doi]
- Doubly Robust Estimator for Ranking Metrics with Post-Click ConversionsYuta Saito. 92-100 [doi]
- Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of RelevanceMesut Kaya, Derek G. Bridge, Nava Tintarev. 101-110 [doi]
- Exploiting Performance Estimates for Augmenting Recommendation EnsemblesGustavo Penha, Rodrygo L. T. Santos. 111-119 [doi]
- Exploring Clustering of Bandits for Online Recommendation SystemLiu Yang, Bo Liu, Leyu Lin, Feng Xia 0006, Kai Chen 0005, Qiang Yang. 120-129 [doi]
- FISSA: Fusing Item Similarity Models with Self-Attention Networks for Sequential RecommendationJing Lin 0008, Weike Pan, Zhong Ming 0001. 130-139 [doi]
- From the lab to production: A case study of session-based recommendations in the home-improvement domainPigi Kouki, Ilias Fountalis, Nikolaos Vasiloglou, Xiquan Cui, Edo Liberty, Khalifeh Al Jadda. 140-149 [doi]
- Global and Local Differential Privacy for Collaborative BanditsHuazheng Wang, Qian Zhao, Qingyun Wu, Shubham Chopra, Abhinav Khaitan, Hongning Wang. 150-159 [doi]
- Goal-driven Command Recommendations for AnalystsSamarth Aggarwal, Rohin Garg, Abhilasha Sancheti, Bhanu Prakash Reddy Guda, Iftikhar Ahamath Burhanuddin. 160-169 [doi]
- ImRec: Learning Reciprocal Preferences Using ImagesJames Neve, Ryan McConville. 170-179 [doi]
- In-Store Augmented Reality-Enabled Product Comparison and RecommendationJesús Omar Álvarez Márquez, Jürgen Ziegler 0001. 180-189 [doi]
- Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning based Recommender SystemsJin Huang, Harrie Oosterhuis, Maarten de Rijke, Herke van Hoof. 190-199 [doi]
- KRED: Knowledge-Aware Document Representation for News RecommendationsDanyang Liu, Jianxun Lian, Shiyin Wang, Ying Qiao, Jiun-Hung Chen, Guangzhong Sun, Xing Xie 0001. 200-209 [doi]
- Learning to Collaborate in Multi-Module Recommendation via Multi-Agent Reinforcement Learning without CommunicationXu He, Bo An 0001, Yanghua Li, Haikai Chen, Rundong Wang, Xinrun Wang, Runsheng Yu, Xin Li, Zhirong Wang. 210-219 [doi]
- Making Neural Networks Interpretable with Attribution: Application to Implicit Signals PredictionDarius Afchar, Romain Hennequin. 220-229 [doi]
- MultiRec: A Multi-Relational Approach for Unique Item Recommendation in Auction SystemsAhmed Rashed, Shayan Jawed, Lars Schmidt-Thieme, Andre Hintsches. 230-239 [doi]
- Neural Collaborative Filtering vs. Matrix Factorization RevisitedSteffen Rendle, Walid Krichene, Li Zhang, John R. Anderson. 240-248 [doi]
- Offline Contextual Multi-armed Bandits for Mobile Health Interventions: A Case Study on Emotion RegulationMawulolo K. Ameko, Miranda L. Beltzer, Lihua Cai, Mehdi Boukhechba, Bethany A. Teachman, Laura E. Barnes. 249-258 [doi]
- On Target Item Sampling in Offline Recommender System EvaluationRocío Cañamares, Pablo Castells. 259-268 [doi]
- Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized RecommendationsHongyan Tang, Junning Liu, Ming Zhao, Xudong Gong. 269-278 [doi]
- PURS: Personalized Unexpected Recommender System for Improving User SatisfactionPan Li 0008, Maofei Que, Zhichao Jiang, Yao Hu, Alexander Tuzhilin. 279-288 [doi]
- Recommendations as Graph ExplorationsMarialena Kyriakidi, Georgia Koutrika, Yannis E. Ioannidis. 289-298 [doi]
- Recommending the Video to Watch Next: An Offline and Online Evaluation at YOUTV.dePanagiotis Symeonidis, Andrea Janes, Dmitry Chaltsev, Philip Giuliani, Daniel Morandini, Andreas Unterhuber, Ludovik Coba, Markus Zanker. 299-308 [doi]
- RecSeats: A Hybrid Convolutional Neural Network Choice Model for Seat Recommendations at Reserved Seating VenuesThéo Moins, Daniel Aloise, Simon J. Blanchard. 309-317 [doi]
- Revisiting Adversarially Learned Injection Attacks Against Recommender SystemsJiaxi Tang, Hongyi Wen, Ke Wang. 318-327 [doi]
- SSE-PT: Sequential Recommendation Via Personalized TransformerLiwei Wu, Shuqing Li, Cho-Jui Hsieh, James Sharpnack. 328-337 [doi]
- TAFA: Two-headed Attention Fused Autoencoder for Context-Aware RecommendationsJin Peng Zhou, Zhaoyue Cheng, Felipe Pérez, Maksims Volkovs. 338-347 [doi]
- Theoretical Modeling of the Iterative Properties of User Discovery in a Collaborative Filtering Recommender SystemSami Khenissi, Mariem Boujelbene, Olfa Nasraoui. 348-357 [doi]
- Towards Safety and Sustainability: Designing Local Recommendations for Post-pandemic WorldGourab K. Patro, Abhijnan Chakraborty, Ashmi Banerjee, Niloy Ganguly. 358-367 [doi]
- Unbiased Ad Click Prediction for Position-aware Advertising SystemsBo-Wen Yuan, Yaxu Liu, Jui-Yang Hsia, Zhenhua Dong, Chih-Jen Lin. 368-377 [doi]
- Unbiased Learning for the Causal Effect of RecommendationMasahiro Sato, Sho Takemori, Janmajay Singh, Tomoko Ohkuma. 378-387 [doi]
- What does BERT know about books, movies and music? Probing BERT for Conversational RecommendationGustavo Penha, Claudia Hauff. 388-397 [doi]
- "Who doesn't like dinosaurs?" Finding and Eliciting Richer Preferences for RecommendationTobias Schnabel, Gonzalo A. Ramos, Saleema Amershi. 398-407 [doi]
- ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based RecommendationFei Mi, Xiaoyu Lin, Boi Faltings. 408-413 [doi]
- Adaptive Pointwise-Pairwise Learning-to-Rank for Content-based Personalized RecommendationYagmur Gizem Cinar, Jean-Michel Renders. 414-419 [doi]
- Carousel Personalization in Music Streaming Apps with Contextual BanditsWalid Bendada, Guillaume Salha, Théo Bontempelli. 420-425 [doi]
- Causal Inference for Recommender SystemsYixin Wang, Dawen Liang, Laurent Charlin, David M. Blei. 426-431 [doi]
- ClusterExplorer: Enable User Control over Related Recommendations via Collaborative Filtering and ClusteringDenis Kotkov, Qian Zhao, Kati Launis, Mats Neovius. 432-437 [doi]
- Combining Rating and Review Data by Initializing Latent Factor Models with Topic Models for Top-N RecommendationFrancisco J. Peña, Diarmuid O'Reilly-Morgan, Elias Z. Tragos, Neil Hurley, Erika Duriakova, Barry Smyth, Aonghus Lawlor. 438-443 [doi]
- Contextual Meta-Bandit for Recommender Systems SelectionMarlesson R. O. Santana, Luckeciano C. Melo, Fernando H. F. Camargo, Bruno Brandão, Anderson Soares, Renan M. Oliveira, Sandor Caetano. 444-449 [doi]
- Deconfounding User Satisfaction Estimation from Response Rate BiasKonstantina Christakopoulou, Madeleine Traverse, Trevor Potter, Emma Marriott, Daniel Li, Chris Haulk, Ed H. Chi, Minmin Chen. 450-455 [doi]
- Deep Bayesian Bandits: Exploring in Online Personalized RecommendationsDalin Guo, Sofia Ira Ktena, Pranay Kumar Myana, Ferenc Huszar, Wenzhe Shi, Alykhan Tejani, Michael Kneier, Sourav Das. 456-461 [doi]
- Explainable Recommendation for Repeat ConsumptionKosetsu Tsukuda, Masataka Goto. 462-467 [doi]
- Explainable Recommendations via Attentive Multi-Persona Collaborative FilteringOren Barkan, Yonatan Fuchs, Avi Caciularu, Noam Koenigstein. 468-473 [doi]
- Exploring Longitudinal Effects of Session-based RecommendationsAndres Ferraro, Dietmar Jannach, Xavier Serra. 474-479 [doi]
- Fit to Run: Personalised Recommendations for Marathon TrainingJakim Berndsen, Barry Smyth, Aonghus Lawlor. 480-485 [doi]
- Free Lunch! Retrospective Uplift Modeling for Dynamic Promotions Recommendation within ROI ConstraintsDmitri Goldenberg, Javier Albert, Lucas Bernardi, Pablo Estevez. 486-491 [doi]
- History-Augmented Collaborative Filtering for Financial RecommendationsBaptiste Barreau, Laurent Carlier. 492-497 [doi]
- Improving One-class Recommendation with Multi-tasking on Various Preference IntensitiesChu-Jen Shao, Hao-Ming Fu, Pu-Jen Cheng. 498-502 [doi]
- Interpretable Contextual Team-aware Item Recommendation: Application in Multiplayer Online Battle Arena GamesAndrés Villa, Vladimir Araujo, Francisca Cattan, Denis Parra. 503-508 [doi]
- Long-tail Session-based RecommendationSiyi Liu, Yujia Zheng. 509-514 [doi]
- MEANTIME: Mixture of Attention Mechanisms with Multi-temporal Embeddings for Sequential RecommendationSung-Min Cho, Eunhyeok Park, Sungjoo Yoo. 515-520 [doi]
- Model Size Reduction Using Frequency Based Double Hashing for Recommender SystemsCaojin Zhang, Yicun Liu, Yuanpu Xie, Sofia Ira Ktena, Alykhan Tejani, Akshay Gupta, Pranay Kumar Myana, Deepak Dilipkumar, Suvadip Paul, Ikuhiro Ihara, Prasang Upadhyaya, Ferenc Huszar, Wenzhe Shi. 521-526 [doi]
- Performance of Hyperbolic Geometry Models on Top-N Recommendation TasksLeyla Mirvakhabova, Evgeny Frolov, Valentin Khrulkov, Ivan V. Oseledets, Alexander Tuzhilin. 527-532 [doi]
- Personality Bias of Music Recommendation AlgorithmsAlessandro B. Melchiorre, Eva Zangerle, Markus Schedl. 533-538 [doi]
- Providing Explainable Race-Time Predictions and Training Plan Recommendations to Marathon RunnersCiara Feely, Brian Caulfield 0001, Aonghus Lawlor, Barry Smyth. 539-544 [doi]
- Reducing energy waste in households through real-time recommendationsJanhavi Dahihande, Akshay Jaiswal, Akshay Anil Pagar, Ajinkya Thakare, Magdalini Eirinaki, Iraklis Varlamis. 545-550 [doi]
- Unbiased Implicit Recommendation and Propensity Estimation via Combinational Joint LearningZiwei Zhu, Yun He, Yin Zhang, James Caverlee. 551-556 [doi]
- Using conceptual incongruity as a basis for making recommendationsTushar Shandhilya, Nisheeth Srivastava. 557-561 [doi]
- A Human Perspective on Algorithmic SimilarityZachary A. Schendel, Faraz Farzin, Siddhi Sundar. 562 [doi]
- Balancing Relevance and Discovery to Inspire Customers in the IKEA AppBalázs Tóth, Sandhya Sachidanandan, Emil S. Jørgensen. 563 [doi]
- Behavior-based Popularity Ranking on Amazon VideoLakshmi Ramachandran. 564-565 [doi]
- Building a reciprocal recommendation system at scale from scratch: Learnings from one of Japan's prominent dating applicationsR. Ramanathan 0002, Nicolas K. Shinada, Sucheendra K. Palaniappan. 566-567 [doi]
- Counterfactual learning for recommender systemZhenhua Dong, Hong Zhu, Pengxiang Cheng, Xinhua Feng, Guohao Cai, Xiuqiang He, Jun Xu, Jirong Wen. 568-569 [doi]
- Developing Recommendation System to provide a Personalized Learning experience at CheggSanghamitra Deb. 570 [doi]
- Investigating Multimodal Features for Video Recommendations at GloboplayFelipe Ferreira, Daniele R. Souza, Igor Moura, Matheus Barbieri, Hélio Côrtes Vieira Lopes. 571-572 [doi]
- On the Heterogeneous Information Needs in the Job Domain: A Unified Platform for Student CareerMarkus Reiter-Haas, David Wittenbrink, Emanuel Lacic. 573-574 [doi]
- Query as Context for Item-to-Item RecommendationMoumita Bhattacharya, Amey Barapatre. 575-576 [doi]
- The Embeddings That Came in From the Cold: Improving Vectors for New and Rare Products with Content-Based InferenceJacopo Tagliabue, Bingqing Yu, Federico Bianchi. 577-578 [doi]
- A Federated Recommender System for Online ServicesBen Tan, Bo Liu, Vincent W. Zheng, Qiang Yang. 579-581 [doi]
- AutoRec: An Automated Recommender SystemTing-Hsiang Wang, Xia Hu, Haifeng Jin, Qingquan Song, Xiaotian Han, Zirui Liu. 582-584 [doi]
- Auto-Surprise: An Automated Recommender-System (AutoRecSys) Library with Tree of Parzens Estimator (TPE) OptimizationRohan Anand, Joeran Beel. 585-587 [doi]
- BETA-Rec: Build, Evaluate and Tune Automated Recommender SystemsZaiqiao Meng, Richard McCreadie, Craig Macdonald, Iadh Ounis, Siwei Liu, Yaxiong Wu, Xi Wang, Shangsong Liang, Yucheng Liang, Guangtao Zeng, Junhua Liang, Qiang Zhang. 588-590 [doi]
- Demonstrating Principled Uncertainty Modeling for Recommender Ecosystems with RecSim NGMartin Mladenov, Chih-Wei Hsu, Vihan Jain, Eugene Ie, Christopher Colby, Nicolas Mayoraz, Hubert Pham, Dustin Tran, Ivan Vendrov, Craig Boutilier. 591-593 [doi]
- Fairness-aware Recommendation with librec-autoNasim Sonboli, Robin Burke, Zijun Liu, Masoud Mansoury. 594-596 [doi]
- PicTouRe - A Picture-Based Tourism RecommenderMete Sertkan, Julia Neidhardt, Hannes Werthner. 597-599 [doi]
- Recommender-Systems.com: A Central Platform for the Recommender-System CommunityJoeran Beel. 600-603 [doi]
- VMI-PSL: Visual Model Inspector for Probabilistic Soft LogicAaron Rodden, Tarun Salh, Eriq Augustine, Lise Getoor. 604-606 [doi]
- 3rd FAccTRec Workshop: Responsible RecommendationMichael D. Ekstrand, Pierre-Nicolas Schwab, Jean Garcia-Gathright, Toshihiro Kamishima, Nasim Sonboli. 607-608 [doi]
- ComplexRec 2020: Workshop on Recommendation in Complex EnvironmentsToine Bogers, Marijn Koolen, Casper Petersen, Bamshad Mobasher, Alexander Tuzhilin. 609-610 [doi]
- Fifth International Workshop on Health Recommender Systems (HealthRecSys 2020)Alan Said, Hanna Schäfer, Helma Torkamaan, Christoph Trattner. 611-612 [doi]
- Interfaces and Human Decision Making for Recommender SystemsPeter Brusilovsky, Marco de Gemmis, Alexander Felfernig, Pasquale Lops, John O'Donovan, Giovanni Semeraro, Martijn C. Willemsen. 613-618 [doi]
- ORSUM - Workshop on Online Recommender Systems and User ModelingJoão Vinagre, Alípio Mário Jorge, Marie Al-Ghossein, Albert Bifet. 619-620 [doi]
- PodRecs: Workshop on Podcast RecommendationsChing-Wei Chen, Longqi Yang, Hongyi Wen, Rosie Jones, Vladan Radosavljevic, Hugues Bouchard. 621-622 [doi]
- RecSys 2020 Challenge Workshop: Engagement Prediction on Twitter's Home TimelineVito Walter Anelli, Amra Delic, Gabriele Sottocornola, Jessie Smith, Nazareno Andrade, Luca Belli, Michael M. Bronstein, Akshay Gupta, Sofia Ira Ktena, Alexandre Lung-Yut-Fong, Frank Portman, Alykhan Tejani, Yuanpu Xie, Xiao Zhu, Wenzhe Shi. 623-627 [doi]
- REVEAL 2020: Bandit and Reinforcement Learning from User InteractionsThorsten Joachims, Yves Raimond, Olivier Koch, Maria Dimakopoulou, Flavian Vasile, Adith Swaminathan. 628-629 [doi]
- Second Workshop on the Impact of Recommender Systems at ACM RecSys '20Oren Sar Shalom, Dietmar Jannach, Joseph A. Konstan. 630-631 [doi]
- Second Workshop on Recommender Systems in Fashion - fashionXrecsys2020Shatha Jaradat, Nima Dokoohaki, Humberto Jesús Corona Pampín, Reza Shirvany. 632-634 [doi]
- Workshop on Context-Aware Recommender SystemsGediminas Adomavicius, Konstantin Bauman, Bamshad Mobasher, Francesco Ricci 0001, Alexander Tuzhilin, Moshe Unger. 635-637 [doi]
- Workshop on Online Misinformation- and Harm-Aware Recommender SystemsAntonela Tommasel, Daniela Godoy, Arkaitz Zubiaga. 638-639 [doi]
- A College Major Recommendation SystemSamuel A. Stein, Gary M. Weiss, Yiwen Chen, Daniel D. Leeds. 640-644 [doi]
- A Joint Dynamic Ranking System with DNN and Vector-based Clustering BanditYu Liu, Xiaoxiao Xu, Jincheng Wang, Yong Li, Changping Peng, Yongjun Bao, Weipeng P. Yan. 645-650 [doi]
- Closed-Form Models for Collaborative Filtering with Side-InformationOlivier Jeunen, Jan Van Balen, Bart Goethals. 651-656 [doi]
- Context-aware Graph Embedding for Session-based News RecommendationHeng-Shiou Sheu, Sheng Li. 657-662 [doi]
- Do Channels Matter? Illuminating Interpersonal Influence on Music RecommendationsHyun-Jeong Kim, So-Yeon Park, Minju Park, Kyogu Lee. 663-668 [doi]
- "Don't Judge a Book by its Cover": Exploring Book Traits Children FavorAshlee Milton, Levesson Batista, Garrett Allen, Siqi Gao, Yiu-Kai Ng, Maria Soledad Pera. 669-674 [doi]
- DRecPy: A Python Framework for Developing Deep Learning-Based RecommendersFábio Colaço, Márcia Barros, Francisco M. Couto. 675-680 [doi]
- Exploring Data Splitting Strategies for the Evaluation of Recommendation ModelsZaiqiao Meng, Richard McCreadie, Craig Macdonald, Iadh Ounis. 681-686 [doi]
- Inferring the Causal Impact of New Track Releases on Music Recommendation Platforms through Counterfactual PredictionsRishabh Mehrotra, Prasanta Bhattacharya, Mounia Lalmas. 687-691 [doi]
- Investigating Listeners' Responses to Divergent RecommendationsRishabh Mehrotra, Chirag Shah, Benjamin A. Carterette. 692-696 [doi]
- Investigating the Impact of Audio States & Transitions for Track Sequencing in Music Streaming SessionsAaron Ng, Rishabh Mehrotra. 697-702 [doi]
- Learning Representations of Hierarchical Slates in Collaborative FilteringEhtsham Elahi, Ashok Chandrashekar. 703-707 [doi]
- Towards Multi-Language Recipe Personalisation and RecommendationNiall Twomey, Mikhail Fain, Andrey Ponikar, Nadine Sarraf. 708-713 [doi]
- Recommending in changing timesShruti Kunde, Mayank Mishra, Amey Pandit, Rekha Singhal, Manoj Karunakaran Nambiar, Gautam M. Shroff, Shashank Gupta. 714-719 [doi]
- Smart Targeting: A Relevance-driven and Configurable Targeting Framework for Advertising SystemYong Li, Zihao Zhao, Zhiwei Fang, Kui Ma, Yafei Yao, Changping Peng, Yongjun Bao, Weipeng Yan. 720-725 [doi]
- The Connection Between Popularity Bias, Calibration, and Fairness in RecommendationHiman Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher. 726-731 [doi]
- Tuning Word2vec for Large Scale Recommendation SystemsBenjamin Paul Chamberlain, Emanuele Rossi, Dan Shiebler, Suvash Sedhain, Michael M. Bronstein. 732-737 [doi]
- Adversarial Learning for Recommendation: Applications for Security and Generative Tasks - Concept to CodeVito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra. 738-741 [doi]
- Bayesian Value Based Recommendation: A modelling based alternative to proxy and counterfactual policy based recommendationDavid Rohde, Flavian Vasile, Sergey Ivanov, Otmane Sakhi. 742-744 [doi]
- Counteracting Bias and Increasing Fairness in Search and Recommender SystemsRuoyuan Gao, Chirag Shah. 745-747 [doi]
- Introduction to Bandits in Recommender SystemsAndrea Barraza-Urbina, Dorota Glowacka. 748-750 [doi]
- Tutorial on Conversational Recommendation SystemsZuohui Fu, Yikun Xian, Yongfeng Zhang, Yi Zhang. 751-753 [doi]
- Tutorial: Feature Engineering for Recommender SystemsBenedikt Schifferer, Chris Deotte, Even Oldridge. 754-755 [doi]
- Characterizing and Mitigating the Impact of Data Imbalance for Stakeholders in Recommender SystemsElizabeth Gómez. 756-757 [doi]
- Conversational Agents for Recommender SystemsAndrea Iovine. 758-763 [doi]
- Developing Work in Confidence, Similarity Structure, and Modeling User Event TimeJacob Munson. 764-769 [doi]
- Efficiency-Effectiveness Trade-offs in Recommendation SystemsIulia Paun. 770-775 [doi]
- Evolutionary Approach in Recommendation Systems for Complex Structured ObjectsBartolomé Ortiz Viso. 776-781 [doi]
- Exploratory Methods for Evaluating Recommender SystemsJoey De Pauw. 782-786 [doi]
- Online Recommender system for Accessible Tourism DestinationsLuchiana Cezara Brodeala. 787-791 [doi]
- Taking advantage of images and texts in recommender systems: semantics and explainabilityPablo Pérez-Núñez. 792-796 [doi]