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Recommender systems help people find relevant content in a personalized way.
The need for biases in learning generalizations
T. M. Mitchell · 1990
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Mining association rules between sets of items in large databases
R. Agrawal, T. Imieliński, and A. Swami · 1993
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A brief introduction to boosting
R. E. Schapire · 1999
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Personalization of supermarket product recommendations
R. Lawrence, G. Almasi, V. Kotlyar, M. Viveros, and S. Duri · 2001
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Getting to know you: Learning new user preferences in recommender systems
A. M. Rashid, I. Albert, D. Cosley, S. K. Lam, S. M. McNee, J. A. Konstan, and J. Riedl · 2002
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Towards more conversational and collaborative recommender systems
G. Carenini, J. Smith, and D. Poole · 2003
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Procedures for performing systematic reviews
B. Kitchenham · 2004
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Shilling recommender systems for fun and profit
S. K. Lam and J. Riedl · 2004
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The long tail: Why the future of business is selling less of more
C. Anderson · 2006
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Persuasive online-selling in quality and taste domains
M. Zanker, M. Bricman, S. Gordea, D. Jannach, and M. Jessenitschnig · 2006
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Evaluating the accuracy of implicit feedback from clicks and query reformulations in web search
T. Joachims, L. Granka, B. Pan, H. Hembrooke, F. Radlinski, and G. Gay · 2007
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Collaborative filtering and the missing at random assumption
B. M. Marlin, R. S. Zemel, S. Roweis, and M. Slaney · 2007
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Recommendation systems with purchase data
A. V. Bodapati · 2008
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From hits to niches? or how popular artists can bias music recommendation and discovery
Ò. Celma and P. Cano · 2008
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A new approach to evaluating novel recommendations
Ò. Celma and P. Herrera · 2008
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The long tail of recommender systems and how to leverage it
Y.-J. Park and A. Tuzhilin · 2008
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Blockbuster culture’s next rise or fall: The impact of recommender systems on sales diversity
D. Fleder and K. Hosanagar · 2009
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Bpr: Bayesian personalized ranking from implicit feedback
S. Rendle, C. Freudenthaler, Z. Gantner, and L. Schmidt-Thieme · 2009
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An improved multiple minimum support based approach to mine rare association rules
R. Uday Kiran and P. Krishna Re · 2009
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Evaluating the dynamic properties of recommendation algorithms
R. Burke · 2010
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Music Recommendation and Discovery: The Long Tail, Long Fail, and Long Play in the Digital Music Space
Ò. Celma · 2010
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Performance of recommender algorithms on top-n recommendation tasks
P. Cremonesi, Y. Koren, and R. Turrin · 2010
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Training and Testing of Recommender Systems on Data Missing Not at Random
H. Steck · 2010
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Improving aggregate recommendation diversity using ranking-based techniques
G. Adomavicius and Y. Kwon · 2011
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Precision-oriented evaluation of recommender systems: An algorithmic comparison
A. Bellogin, P. Castells, and I. Cantador · 2011
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Novel recommendation based on personal popularity tendency
J. Oh, S. Park, H. Yu, M. Song, and S.-T. Park · 2011
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A user-centric evaluation framework for recommender systems
P. Pu, L. Chen, and R. Hu · 2011
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Real-time, location-aware collaborative filtering of web content
T. Sandholm and H. Ung · 2011
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Item popularity and recommendation accuracy
H. Steck · 2011
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Rank and relevance in novelty and diversity metrics for recommender systems
S. Vargas and P. Castells · 2011
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Impact of data characteristics on recommender systems performance
G. Adomavicius and J. Zhang · 2012
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Recommender systems in computer science and information systems–a landscape of research
D. Jannach, M. Zanker, M. Ge, and M. Gröning · 2012
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Recommendation networks and the long tail of electronic commerce
G. Oestreicher-Singer and A. Sundararajan · 2012
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Challenging the long tail recommendation
H. Yin, B. Cui, J. Li, J. Yao, and C. Chen · 2012
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Personalized news recommendation with context trees
F. Garcin, C. Dimitrakakis, and B. Faltings · 2013
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Inductive bias
E. Hüllermeier, T. Fober, and M. Mernberger · 2013
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Efficiency improvement of neutrality-enhanced recommendation
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma · 2013
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Tailored news in the palm of your hand: A multi-perspective transparent approach to news recommendation
M. Tavakolifard, J. A. Gulla, K. C. Almeroth, J. E. Ingvaldesn, G. Nygreen, and E. Berg · 2013
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Opinion-based collaborative filtering to solve popularity bias in recommender systems
X. Zhao, Z. Niu, and W. Chen · 2013
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Using novelty score of unseen items to handle popularity bias in recommender systems
P. Bedi, A. Gautam, C. Sharma, et al · 2014
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An enhanced collaborative filtering with flexible item popularity control for recommender systems
T. Chen, H. Tian, and X. Zhu · 2014
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Recommending without short head
P. Cremonesi, F. Garzotto, R. Pagano, and M. Quadrana · 2014
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User perception of differences in recommender algorithms
M. D. Ekstrand, F. M. Harper, M. C. Willemsen, and J. A. Konstan · 2014
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Empirical evaluation of active learning strategies in collaborative filtering
M. Elahi · 2014
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Correcting popularity bias by enhancing recommendation neutrality
T. Kamishima, S. Akaho, H. Asoh, and J. Sakuma · 2014
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Evaluating recommender behavior for new users
D. Kluver and J. A. Konstan · 2014
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Impact of recommender systems on sales volume and diversity
D. Lee and K. Hosanagar · 2014
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Context-aware recommender systems
G. Adomavicius and A. Tuzhilin · 2015
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The Netflix recommender system: Algorithms, business value, and innovation
C. A. Gomez-Uribe and N. Hunt · 2015
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Evaluating recommender systems
A. Gunawardana and G. Shani · 2015
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The MovieLens Datasets: History and Context
F. M. Harper and J. A. Konstan · 2015
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What recommenders recommend: an analysis of recommendation biases and possible countermeasures
D. Jannach, L. Lerche, I. Kamehkhosh, and M. Jugovac · 2015
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“i like to explore sometimes”: Adapting to dynamic user novelty preferences
K. Kapoor, V. Kumar, L. Terveen, J. A. Konstan, and P. Schrater · 2015
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Escaping your comfort zone: A graph-based recommender system for finding novel recommendations among relevant items
K. Lee and K. Lee · 2015
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A survey of active learning in collaborative filtering recommender systems
M. Elahi, F. Ricci, and N. Rubens · 2016
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Benchmarking News Recommendations: The CLEF NewsREEL Use Case
F. Hopfgartner, T. Brodt, J. Seiler, B. Kille, A. Lommatzsch, M. Larson, R. Turrin, and A. Serény · 2016
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Diversity, serendipity, novelty, and coverage: A survey and empirical analysis of beyond-accuracy objectives in recommender systems
M. Kaminskas and D. Bridge · 2016
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On the value of reminders within e-commerce recommendations
L. Lerche, D. Jannach, and M. Ludewig · 2016
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Recommendations as treatments: Debiasing learning and evaluation
T. Schnabel, A. Swaminathan, A. Singh, N. Chandak, and T. Joachims · 2016
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Statistical biases in information retrieval metrics for recommender systems
A. Bellogín, P. Castells, and I. Cantador · 2017
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Multisided fairness for recommendation
R. Burke · 2017
Cited alongside, same era.
Recommender response to diversity and popularity bias in user profiles
S. Channamsetty and M. D. Ekstrand · 2017
Cited alongside, same era.
Session-based item recommendation in e-commerce: On short-term intents, reminders, trends, and discounts
D. Jannach, M. Ludewig, and L. Lerche · 2017
Cited alongside, same era.
Beyond parity: Fairness objectives for collaborative filtering
S. Yao and B. Huang · 2017
Cited alongside, same era.
All the cool kids, how do they fit in?: Popularity and demographic biases in recommender evaluation and effectiveness
M. D. Ekstrand, M. Tian, I. M. Azpiazu, J. D. Ekstrand, O. Anuyah, D. McNeill, and M. S. Pera · 2018
Cited alongside, same era.
Balancing the popularity bias of object similarities for personalised recommendation
A unified optimization toolbox for solving popularity bias, fairness, and diversity in recommender systems
S. Seymen, H. Abdollahpouri, and E. C. Malthouse · 2021
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Model-agnostic counterfactual reasoning for eliminating popularity bias in recommender system
T. Wei, F. Feng, J. Chen, Z. Wu, J. Yi, and X. He · 2021
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Blockbuster: A new perspective on popularity-bias in recommender systems
E. Yalcin · 2021
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Investigating and counteracting popularity bias in group recommendations
E. Yalcin and A. Bilge · 2021
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Causal intervention for leveraging popularity bias in recommendation
Y. Zhang, F. Feng, X. He, T. Wei, C. Song, G. Ling, and Y. Zhang · 2021
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Disentangling user interest and conformity for recommendation with causal embedding
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L. Hou, X. Pan, and K. Liu · 2018
Cited alongside, same era.
Fairness aware regularization on a learning-to-rank recommender system for controlling popularity bias in e-commerce domain
D. Kiswanto, D. Nurjanah, and R. Rismala · 2018
Cited alongside, same era.
Sequence-aware recommender systems
M. Quadrana, P. Cremonesi, and D. Jannach · 2018
Cited alongside, same era.
Mitigating popularity bias in twitter-recommending novel hashtags using pooled tweets
C. Sharma and P. Bedi · 2018
Cited alongside, same era.
Calibrated recommendations
H. Steck · 2018
Cited alongside, same era.
Quantitative analysis of matthew effect and sparsity problem of recommender systems
H. Wang, Z. Wang, and W. Zhang · 2018
Cited alongside, same era.
MMCF: Multimodal collaborative filtering for automatic playlist continuation
H. Yang, Y. Jeong, M. Choi, and J. Lee · 2018
Cited alongside, same era.
Y. Zheng, C. Gao, X. Li, X. He, Y. Li, and D. Jin · 2021
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Serendipity in recommender systems: A systematic literature review
R. J. Ziarani and R. Ravanmehr · 2021
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Popularity bias in recommender systems - A Review
A. B. Ahanger, S. W. Aalam, M. R. Bhat, and A. Assad · 2022
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Consumer fairness in recommender systems: Contextualizing definitions and mitigations
L. Boratto, G. Fenu, M. Marras, and G. Medda · 2022
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Fairness in information access systems
M. D. Ekstrand, A. Das, R. Burke, and F. Diaz · 2022
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Evaluating recommender systems
A. Gunawardana, G. Shani, and S. Yogev · 2022
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Mitigating popularity bias in recommendation via counterfactual inference
M. He, C. Li, X. Hu, X. Chen, and J. Wang · 2022
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It is different when items are older: Debiasing recommendations when selection bias and user preferences are dynamic
J. Huang, H. Oosterhuis, and M. de Rijke · 2022
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Intra-list similarity and human diversity perceptions of recommendations: The details matter
M. Jesse, C. Bauer, and D. Jannach · 2022
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Mitigating popularity bias in recommendation: Potential and limits of calibration approaches
A. Klimashevskaia, M. Elahi, D. Jannach, C. Trattner, and L. Skjærven · 2022
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Bignn: A bilateral-branch graph neural network to solve popularity bias in recommendation
Y. Kou, N. Gao, Y. Zhang, C. Tu, and C. Ma · 2022
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Popularity bias in collaborative filtering-based multimedia recommender systems
D. Kowald and E. Lacic · 2022
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What drives readership? an online study on user interface types and popularity bias mitigation in news article recommendations
E. Lacic, L. Fadljevic, F. Weissenboeck, S. Lindstaedt, and D. Kowald · 2022
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Exploring cross-group discrepancies in calibrated popularity for accuracy/fairness trade-off optimization
O. Lesota, S. Brandl, M. Wenzel, A. B. Melchiorre, E. Lex, N. Rekabsaz, and M. Schedl · 2022
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Quantifying and mitigating popularity bias in conversational recommender systems
A. Lin, J. Wang, Z. Zhu, and J. Caverlee · 2022
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Altrec: Adversarial learning for autoencoder-based tail recommendation
J. Liu, D. Liu, W. Pan, and Z. Ming · 2022
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The unfairness of popularity bias in book recommendation
M. Naghiaei, H. A. Rahmani, and M. Dehghan · 2022
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Revisiting popularity and demographic biases in recommender evaluation and effectiveness
N. Neophytou, B. Mitra, and C. Stinson · 2022
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The unfairness of active users and popularity bias in point-of-interest recommendation
H. A. Rahmani, Y. Deldjoo, A. Tourani, and M. Naghiaei · 2022
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Mitigating popularity bias in recommendation with unbalanced interactions: A gradient perspective
W. Ren, L. Wang, K. Liu, R. Guo, L. E. Peng, and Y. Fu · 2022
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Countering popularity bias by regularizing score differences
W. Rhee, S. M. Cho, and B. Suh · 2022
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An optimized recommendation framework exploiting textual review based opinion mining for generating pleasantly surprising, novel yet relevant recommendations
R. Shrivastava, D. S. Sisodia, N. K. Nagwani, and U. R. BP · 2022
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Addressing popularity bias in citizen science
A. Sultan, A. Segal, G. Shani, and Y. Gal · 2022
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Novel approaches to measuring the popularity inclination of users for the popularity bias problem
Y. Tacli, E. Yalcin, and A. Bilge · 2022
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Cross pairwise ranking for unbiased item recommendation
Q. Wan, X. He, X. Wang, J. Wu, W. Guo, and R. Tang · 2022
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Providing item-side individual fairness for deep recommender systems
X. Wang and W. H. Wang · 2022
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PopHybrid: a novel item popularity-aware hybrid approach for long-tail recommendation
E. Yalcin · 2022
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Treating adverse effects of blockbuster bias on beyond-accuracy quality of personalized recommendations
E. Yalcin and A. Bilge · 2022
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Popularity Bias Analysis of Recommendation Algorithm Based on ABM Simulation
C. Yu, D. Li, T. Lu, and Y. Jiang · 2022
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Balancing the trade-off between accuracy and diversity in recommender systems with personalized explanations based on linked open data
A. L. Zanon, L. C. D. da Rocha, and M. G. Manzato · 2022
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Pipattack: Poisoning federated recommender systems for manipulating item promotion
S. Zhang, H. Yin, T. Chen, Z. Huang, Q. V. H. Nguyen, and L. Cui · 2022
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Popularity bias is not always evil: Disentangling benign and harmful bias for recommendation
Z. Zhao, J. Chen, S. Zhou, X. He, X. Cao, F. Zhang, and W. Wu · 2022
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Adap- τ 𝜏 \tau italic_τ : Adaptively modulating embedding magnitude for recommendation
J. Chen, J. Wu, J. Wu, X. Cao, S. Zhou, and X. He · 2023
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Fairness in recommender systems: Research landscape and future directions
Y. Deldjoo, D. Jannach, A. Bellogin, A. Difonzo, and D. Zanzonelli · 2023
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Amplifying artists’ voices: Item provider perspectives on influence and fairness of music streaming platforms
K. Dinnissen and C. Bauer · 2023
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I Don’t Care How Popular You Are! Investigating Popularity Bias in Music Recommendations from a User’s Perspective
B. Ferwerda, E. Ingesson, M. Berndl, and M. Schedl · 2023
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Calibrating popularity bias based on quality for recommendation fairness
Z. Guo, Y. Zhu, Z. Wang, and M. Jing · 2023
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Eqbal-rs: Mitigating popularity bias in recommender systems
S. Gupta, K. Kaur, and S. Jain · 2023
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Candidate set sampling for evaluating top-n recommendation
N. Ihemelandu and M. D. Ekstrand · 2023
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De-biasing user conformity bias and item popularity bias in group recommendation
J. Jia, T. Shang, L. Li, and S. Chen · 2023
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Test-time embedding normalization for popularity bias mitigation
D. Kim, J. Park, and D. Kim · 2023
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A study on accuracy, miscalibration, and popularity bias in recommendations
D. Kowald, G. Mayr, M. Schedl, and E. Lex · 2023
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Computational versus perceived popularity miscalibration in recommender systems
O. Lesota, G. Escobedo, Y. Deldjoo, B. Ferwerda, S. Kopeinik, E. Lex, N. Rekabsaz, and M. Schedl · 2023
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An explicitly weighted gcn aggregator based on temporal and popularity features for recommendation
X. Li, G. Xiao, Y. Chen, Z. Tang, W. Jiang, and K. Li · 2023
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User-dependent learning to debias for recommendation
F. Luo and J. Wu · 2023
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Dealing with popularity bias in recommender systems for third-party libraries: How far are we?
P. T. Nguyen, R. Rubei, J. Di Rocco, C. Di Sipio, D. Di Ruscio, and M. Di Penta · 2023
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Curse of “low” dimensionality in recommender systems
N. Ohsaka and R. Togashi · 2023
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Skellam rank: Fair learning to rank algorithm based on poisson process and skellam distribution for recommender systems
H. Wang · 2023
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Debiased contrastive learning for sequential recommendation
Y. Yang, C. Huang, L. Xia, C. Huang, D. Luo, and K. Lin · 2023
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A model-agnostic popularity debias training framework for click-through rate prediction in recommender system
F. Zhang and Q. Shen · 2023
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Cipl: Counterfactual interactive policy learning to eliminate popularity bias for online recommendation
Y. Zheng, J. Qin, P. Wei, Z. Chen, and L. Lin · 2023
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How graph convolutions amplify popularity bias for recommendation?
J. Chen, J. Wu, J. Chen, X. Xin, Y. Li, and X. He · 2024
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Maximum entropy policy for long-term fairness in interactive recommender systems
X. Shi, Q. Liu, H. Xie, Y. Bai, and M. Shang · 2024
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Improving recommendation lists through topic diversification
C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen · 2024
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