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1 | The original list of papers can be found here: https://recsys.acm.org/recsys21/accepted-contributions/ | |||||||||||||||||||||||||||
2 | Title | Authors | URL | Classification | Notes | |||||||||||||||||||||||
3 | A Payload Optimization Method for Federated Recommender Systems | Farwa K. Khan, Adrian Flanagan, Kuan Eeik Tan, Zareen Alamgir, and Muhammad Ammad-ud-din | https://dl.acm.org/doi/10.1145/3460231.3474257 | Unrelated | Infrastructual | |||||||||||||||||||||||
4 | Accordion: A Trainable Simulator for Long-Term Interactive Systems | James McInerney, Ehtsham Elahi, Justin Basilico, Yves Raimond, and Tony Jebara | https://dl.acm.org/doi/10.1145/3460231.3474259 | Unrelated | Sufficiently general to be applied with non-RP elements, but not directly related to inferring preferences | |||||||||||||||||||||||
5 | An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes | Matus Tomlein, Branislav Pecher, Jakub Simko, Ivan Srba, Robert Moro, Elena Stefancova, Michal Kompan, Andrea Hrckova, Juraj Podrouzek, and Maria Bielikova | https://dl.acm.org/doi/10.1145/3460231.3474241 | Unrelated | Audit of misinformation rabbit holes / filter bubbles on YouTube | |||||||||||||||||||||||
6 | Black-Box Attacks on Sequential Recommenders via Data-Free Model Extraction | Zhenrui Yue, Zhankui He, Huimin Zeng, and Julian McAuley | https://dl.acm.org/doi/10.1145/3460231.3474275 | Unrelated | Deals with recommender attack, not preference learning | |||||||||||||||||||||||
7 | Burst-induced Multi-Armed Bandit for Learning Recommendation | Rodrigo Alves, Antoine Ledent, and Marius Kloft | https://dl.acm.org/doi/10.1145/3460231.3474250 | RP | Describes an approach to maximising engagement in a multi-armed bandit framework | |||||||||||||||||||||||
8 | cDLRM: Look Ahead Caching for Scalable Training of Recommendation Models | Keshav Balasubramanian, Abdulla Alshabanah, Joshua D Choe, and Murali Annavaram | https://dl.acm.org/doi/10.1145/3460231.3474246 | Unrelated | A method for more efficiently training deep neural recommenders - not directly related to preference learning | |||||||||||||||||||||||
9 | Cold Start Similar Artists Ranking with Gravity-Inspired Graph Autoencoders | Guillaume Salha-Galvan, Romain Hennequin, Benjamin Chapus, Viet-Anh Tran, and Michalis Vazirgiannis | https://dl.acm.org/doi/10.1145/3460231.3474252 | RP | A method for recommending similar artists, as where similarity is determined by users who have similar patterns of engagement | |||||||||||||||||||||||
10 | Debiased Explainable Pairwise Ranking from Implicit Feedback | Khalil Damak, Sami Khenissi, and Olfa Nasraoui | https://dl.acm.org/doi/10.1145/3460231.3474274 | RP | Focus on mitigating exposure bias by learning from which items were seen but not engaged with | |||||||||||||||||||||||
11 | Denoising User-aware Memory Network for Recommendation | Zhi Bian, Shaojun Zhou, Hao Fu, Qihong Yang, Zhenqi Sun, Junjie Tang, Guiquan Liu, kaikui liu, and Xiaolong Li | https://dl.acm.org/doi/10.1145/3460231.3474237 | RP | Focuses on Click Through Rate prediction | |||||||||||||||||||||||
12 | Designing Online Advertisements via Bandit and Reinforcement Learning | Yusuke Narita, Shota Yasui, and Kohei Yata | https://dl.acm.org/doi/10.1145/3460231.3474231 | RP | Also focuses on Click Through Rate prediction | |||||||||||||||||||||||
13 | Evaluating Off-Policy Evaluation: Sensitivity and Robustness | Yuta Saito, Takuma Udagawa, Haruka Kiyohara, Kazuki Mogi, Yusuke Narita, and Kei Tateno | https://dl.acm.org/doi/10.1145/3460231.3474245 | RP | Also focuses on Click Through Rate prediction | |||||||||||||||||||||||
14 | EX3: Explainable Attribute-aware Item-set Recommendations | Yikun Xian, Tong Zhao, Jin Li, Jim Chan, Andrey Kan, Jun Ma, Xin Luna Dong, Christos Faloutsos, George Karypis, S. Muthukrishnan, and Yongfeng Zhang | https://dl.acm.org/doi/10.1145/3460231.3474240 | Beyond RP | Tries to infer what attributes are important to a user in an e-commerce setting, and base future recommendations on those important attributes | |||||||||||||||||||||||
15 | Fast Multi-Step Critiquing for VAE-based Recommender Systems | Diego Antognini and Boi Faltings | https://dl.acm.org/doi/10.1145/3460231.3474249 | Beyond RP | Proposes a method for learning from user critiques of recommendations | |||||||||||||||||||||||
16 | Follow the guides: disentangling human and algorithmic curation in online music consumption | Quentin Villermet, Jérémie Poiroux, Manuel Moussallam, Thomas Louail, and Camille Roth | https://dl.acm.org/doi/10.1145/3460231.3474269 | Unrelated | Empirical study looking at the interaction between a music recommender and diversity of content consumption | |||||||||||||||||||||||
17 | Hierarchical Latent Relation Modeling for Collaborative Metric Learning | Viet-Anh Tran, Guillaume Salha-Galvan, Romain Hennequin, and Manuel Moussallam | https://dl.acm.org/doi/10.1145/3460231.3474230 | RP | Method for better learning different types of preferences from behavior data | |||||||||||||||||||||||
18 | I want to break free! Recommending friends from outside the echo chamber | Antonela Tommasel, Juan Manuel Rodriguez, and Daniela Godoy | https://dl.acm.org/doi/10.1145/3460231.3474270 | RP | Aim to predict interaction between users (behavior) | |||||||||||||||||||||||
19 | Information Interactions in Outcome Prediction: Quantification and Interpretation using Stochastic Block Models | Gaël Poux-Médard, Julien Velcin, and Sabine Loudcher | https://dl.acm.org/doi/10.1145/3460231.3474254 | RP | Aim to improve predictions of behavior by accounting for interaction effects between historical behaviors | |||||||||||||||||||||||
20 | Large-scale Interactive Conversational Recommendation System | Ali Montazeralghaem, James Allan, and Philip S. Thomas | https://dl.acm.org/doi/10.1145/3460231.3474271 | Beyond RP | Propose a conversational recommender in the e-commerce setting that learns in part by asking the user questions to clarify their product search | |||||||||||||||||||||||
21 | Large-Scale Modeling of Mobile User Click Behaviors Using Deep Learning | Xin Zhou and Yang Li | https://dl.acm.org/doi/10.1145/3460231.3474264 | RP | Modelling future clicks based on previous clicks | |||||||||||||||||||||||
22 | Learning An Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems | Danni Peng, Sinno Jialin Pan, Jie Zhang, and Anxiang Zeng | https://dl.acm.org/doi/10.1145/3460231.3474239 | Unrelated | A method for updating recommenders based on newly available data while mitigating the risk of forgetting useful information learned in prior training rounds. As far as I can tell this is model agnostic and may or may not be applied beyond RP | |||||||||||||||||||||||
23 | Learning to Represent Human Motives for Goal-directed Web Browsing | Jyun-Yu Jiang, Chia-Jung Lee, Longqi Yang, Bahareh Sarrafzadeh, Brent Hecht, Jaime Teevan | https://dl.acm.org/doi/10.1145/3460231.3474260 | Beyond RP | Models web browsing behavior as a function of the users' goals, which are learned | |||||||||||||||||||||||
24 | Local Factor Models for Large-Scale Inductive Recommendation | Longqi Yang, Tobias Schnabel, Paul N. Bennett, and Susan Dumais | https://dl.acm.org/doi/10.1145/3460231.3474276 | RP | Method for efficiently scaling a recommender framework that explicitly identifies clusters of users who have similar patterns of behavior | |||||||||||||||||||||||
25 | Matrix Factorization for Collaborative Filtering Is Just Solving an Adjoint Latent Dirichlet Allocation Model After All | Florian Wilhelm | https://dl.acm.org/doi/10.1145/3460231.3474266 | RP | Show an equivalence between matrix factorisation and a generalised LDA model | |||||||||||||||||||||||
26 | Mitigating Confounding Bias in Recommendation via Information Bottleneck | Dugang Liu, Pengxiang Cheng, Hong Zhu, Zhenhua Dong, Xiuqiang He, Weike Pan, and Zhong Ming | https://dl.acm.org/doi/10.1145/3460231.3474263 | Unrelated | Methods for mitigating selection bias, popularity bias and position bias in recommenders generally | |||||||||||||||||||||||
27 | Negative Interactions for Improved Collaborative-Filtering: Don’t go Deeper, go Higher | Harald Steck and Dawen Liang | https://dl.acm.org/doi/10.1145/3460231.3474273 | RP | Improving engagement prediction by including interaction terms between historical items engaged with among the predictor variables | |||||||||||||||||||||||
28 | Next-item Recommendations in Short Sessions | Wenzhuo Song, Shoujin Wang, Yan Wang, and SHENGSHENG WANG | https://dl.acm.org/doi/10.1145/3460231.3474238 | RP | Make 'up next' recommendations for short sessions in a few shot learning framework, drawing on the user's previous sessions and sessions of similar users | |||||||||||||||||||||||
29 | Online Evaluation Methods for the Causal Effect of Recommendations | Masahiro Sato | https://dl.acm.org/doi/10.1145/3460231.3474235 | Unrelated | Method for measuring the causal effect of recommendations | |||||||||||||||||||||||
30 | Page-level Optimization of e-Commerce Item Recommendations | Chieh Lo, Hongliang Yu, Xin Yin, Krutika Shetty, Changchen He, Kathy Hu, Justin M Platz, Adam Ilardi, and Sriganesh Madhvanath | https://dl.acm.org/doi/10.1145/3460231.3474242 | RP | Method for personalizing 'related items' recommendations in an e-commerce setting, with the goal of maximising click-through rates | |||||||||||||||||||||||
31 | Partially Observable Reinforcement Learning for Dialog-based Interactive Recommendation | Yaxiong Wu, Craig Macdonald, and Iadh Ounis, | https://dl.acm.org/doi/10.1145/3460231.3474256 | Beyond RP | Reinforcement learning method for modelling user preferences based on natural language feedback in a conversational recommender | |||||||||||||||||||||||
32 | Pessimistic Reward Models for Off-Policy Learning in Recommendation | Olivier Jeunen and Bart Goethals | https://dl.acm.org/doi/10.1145/3460231.3474247 | Unrelated | As best I can tell, proposes a reward learning policy that is agnostic to whether the modelling framework goes beyond RP | |||||||||||||||||||||||
33 | Privacy Preserving Collaborative Filtering by Distributed Mediation | Alon Ben Horin, and Tamir Tassa | https://dl.acm.org/doi/10.1145/3460231.3474251 | Unrelated | A method for privacy-preserving aggregation of collaborative filtering datasets across multiple vendors | |||||||||||||||||||||||
34 | ProtoCF: Prototypical Collaborative Filtering for Few-shot Item Recommendation | Aravind Sankar, Junting Wang, Adit Krishnan, and Hari Sundaram | https://dl.acm.org/doi/10.1145/3460231.3474268 | RP | Formulate long-tail recommendations as a few-shot learning problem based on the limited click data available | |||||||||||||||||||||||
35 | Recommendation on Live-Streaming Platforms: Dynamic Availability and Repeat Consumption | Jeremie Rappaz, Julian McAuley, and Karl Aberer | https://dl.acm.org/doi/10.1145/3460231.3474267 | RP | Fairly standard RP approach, applied in the context of live stream recommendation | |||||||||||||||||||||||
36 | Reverse Maximum Inner Product Search: How to efficiently find users who would like to buy my item? | Daichi Amagata and Takahiro Hara | https://dl.acm.org/doi/10.1145/3460231.3474229 | Unrelated | Efficient search/sort algorithm for finding the largest inner products between query, item and user vectors | |||||||||||||||||||||||
37 | Semi-Supervised Visual Representation Learning for Fashion Compatibility | Ambareesh Revanur, Vijay Kumar, and Deepthi Sharma | https://dl.acm.org/doi/10.1145/3460231.3474233 | Unrelated | A semi-supervised method for learning embeddings for fashion items that capture whether they match with each other - only indirectly related to preference learning | |||||||||||||||||||||||
38 | “Serving Each User”: Supporting Different Eating Goals Through a Multi-List Recommender Interface | Alain Starke, Edis Asotic, and Christoph Trattner | https://dl.acm.org/doi/10.1145/3460231.3474232 | Unrelated | Empirical study in which no preference learning occurred, all participants saw the same recommendations | |||||||||||||||||||||||
39 | Shared Neural Item Representations for Completely Cold Start Problem | Ramin Raziperchikolaei, Guannan Liang, and Young-joo Chung | https://dl.acm.org/doi/10.1145/3460231.3474228 | RP | A hybrid method for overcoming the item cold start problem. | |||||||||||||||||||||||
40 | Sparse Feature Factorization for Recommender Systems with Knowledge Graphs | Antonio Ferrara, Vito Walter Anelli, Tommaso Di Noia, and Alberto Carlo Maria Mancino | https://dl.acm.org/doi/10.1145/3460231.3474243 | Beyond RP | A method for efficiently learning embeddings by incorporating explicit user feedback on the attributes of the items they used when making decisions. | |||||||||||||||||||||||
41 | Stronger Privacy for Federated Collaborative Filtering With Implicit Feedback | Lorenzo Minto, Moritz Haller, Ben Livshits, and Hamed Haddadi | https://dl.acm.org/doi/10.1145/3460231.3474262 | RP | Privacy focused variant of collaborative filtering that addressed privacy concerns related to implicit feedback | |||||||||||||||||||||||
42 | The Dual Echo Chamber: Modeling Social Media Polarization for Interventional Recommending | Tim Donkers and Jürgen Ziegler | https://dl.acm.org/doi/10.1145/3460231.3474261 | Unrelated | Agent-based model exploring differences between two possible types of echo chamber, and the effectiveness of recommender related interventions in each | |||||||||||||||||||||||
43 | The role of preference consistency, defaults and musical expertise in users’ exploration behavior in a genre exploration recommender | Yu Liang and Martijn C. Willemsen | https://dl.acm.org/doi/10.1145/3460231.3474253 | Unrelated | User study exploring the causal effects of recommendations/nudges on users' music genre preferences | |||||||||||||||||||||||
44 | Together is Better: Hybrid Recommendations Combining Graph Embeddings and Contextualized Word Representations | Marco Polignano, Cataldo Musto, Marco de Gemmis, Pasquale Lops, and Giovanni Semeraro | https://dl.acm.org/doi/10.1145/3460231.3474272 | Unrelated | Exploring the benefits from combining graph and word embeddings in content-based or hybrid recommenders. | |||||||||||||||||||||||
45 | Top-K Contextual Bandits with Equity of Exposure | Olivier Jeunen and Bart Goethals | https://dl.acm.org/doi/10.1145/3460231.3474248 | RP | Method for increasing 'equity of exposure' for sellers in a two-sided platform, without significantly impacting the click through rates for buyers. | |||||||||||||||||||||||
46 | Tops, Bottoms, and Shoes: Building Capsule Wardrobes via Cross-Attention Tensor Network | Huiyuan Chen, Yusan Lin, Fei Wang, and Hao Yang | https://dl.acm.org/doi/10.1145/3460231.3474258 | RP | Learning fashion outfit compatibility scores from human chosen examples. | |||||||||||||||||||||||
47 | Towards Source-Aligned Variational Models for Cross-Domain Recommendation | Aghiles Salah, Thanh Binh Tran, and Hady Lauw | https://dl.acm.org/doi/10.1145/3460231.3474265 | Unrelated | Method for incorporating knowledge from recommenders in other domains. As far as I can tell, agnostic to whether the two models go beyond RP. | |||||||||||||||||||||||
48 | Towards Unified Metrics for Accuracy and Diversity for Recommender Systems | Javier Parapar and Filip Radlinski | https://dl.acm.org/doi/10.1145/3460231.3474234 | Beyond RP | Propose a metric for combining evaluation of accuracy and diversity, modelling the probability that a user think an item has particular attributes. | |||||||||||||||||||||||
49 | Transformers4Rec: Bridging the Gap between NLP and Sequential / Session-Based Recommendation | Gabriel de Souza Pereira Moreira, Sara Rabhi, Jeong Min Lee, Ronay Ak, and Even Oldridge | https://dl.acm.org/doi/10.1145/3460231.3474255 | Unrelated | Infastructural. Introduces a library that provides an interface between Huggingface NLP transformer models and recommendation tasks. | |||||||||||||||||||||||
50 | User Bias in Beyond-Accuracy Measurement of Recommendation Algorithms | Ningxia Wang, and Li Chen | https://dl.acm.org/doi/10.1145/3460231.3474244 | Beyond RP | Evaluate user perceptions of different kinds of recommendation biases and their relationship to non-behavioral user attributes including demographic variables and the big 5 personality traits. | |||||||||||||||||||||||
51 | Values of Exploration in Recommender Systems | Minmin Chen, Yuyan Wang, Can Xu, Ya Le, mohit sharma, Lee Richardson, and Ed Chi | https://dl.acm.org/doi/10.1145/3460231.3474236 | RP | A bit hard to categorise, but fundamentally they use 'conversion of casual users to core users as an indication of holistic long-term user experience' so I'm think it's fundamentally RP. | |||||||||||||||||||||||
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