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Rational and irrational bias in recommendation

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Pablo Castells

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IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Rational and irrational bias in recommendation The Information Retrieval General Reading Group Pablo Castells Universidad Autónoma de Madrid http://ir.ii.uam.es/castells RMIT, June 3, 2021
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Outline 1. Fairness and bias 2. Bias in recommendation 3. Majority bias in offline evaluation 4. Conclusion
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 1. Fairness and bias 2. Bias in recommendation 3. Majority bias in offline evaluation 4. Conclusion Outline
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Fairness angles  Fairness is complex  Has multiple definitions  Different angles  No universal formula Fair ≠ uniform Olteanu et al. Frontiers in Big Data 2019, Baeza Comm. ACM 2018, Ntoutsi et al. Rev. Data Min. Knowl. Discov. 2020
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Fairness angles Equal representation 𝑝 group 𝐴 hire = 𝑝 group 𝐵 hire Statistical parity 𝑝 hire group 𝐴 = 𝑝 hire group 𝐵 Equal opportunity 𝑝 hire qualifications = 𝑥, group 𝐴 = 𝑝 hire qualifications = 𝑥, group 𝐵 Fair ≠ uniform Olteanu et al. Frontiers in Big Data 2019, Baeza Comm. ACM 2018, Ntoutsi et al. Rev. Data Min. Knowl. Discov. 2020, Pleiss et al NIPS 2017.
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 > Bias for… • Rational thinking • Scientific method • Axioms of geometry • Trust on scientific authority Bias is… bad?
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Shared principles • Rationalism, ethics, human rights, well being … “Truth” is a construct • Based on method • Subjective, consensus • Model-dependent • Useful Kant Kuhn Hawking > Bias for… • Rational thinking • Scientific method • Axioms of geometry • Trust on scientific authority Bias is… bad? Fair ≠ neutral Our “axioms”?
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Truth vs. truth system Euclidean geometry Hyperbolic geometry Elliptic geometry Axioms involve a choice What’s a “rectangle”?
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Truth vs. truth system Let 𝑅 = 𝐴 𝐴 ∉ 𝐴 } Then 𝑅 ∈ 𝑅 ⇔ 𝑅 ∉ 𝑅 !! Axioms are not perfect – they are just meant to be useful Even in math, multiple alternative axiom systems exist, and they are often revised
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Bias and fairness My point • Fairness and bias are arbitrarily complex questions • The importance of bias awareness and understanding to better cope with bias
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Outline 1. Fairness and bias 2. Bias in recommendation 3. Majority bias in offline evaluation 4. Conclusion
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 1. Fairness and bias 2. Bias in recommendation 3. Majority bias in offline evaluation 4. Conclusion Outline
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Recommender systems ? ? ? ? ? ? ? ? ?
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Majority bias in recommendation Best sellers Long-tail items Items Users Popular items Rest of items (long tail) Items # Interactions Bias in the data
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Majority bias in recommendation Bias in offline evaluation Ranking by # positive ratings 0.3 0.2 0.1 0 nDCG@10 0 0.1 0.2 0.3 Random Avg. rating Nr. ratings User-based Matrix fact. nDCG@10 Netflix Cañamares & Castells SIGIR 2018, Cremonesi et al. RecSys 2010, Jannach et al. UMUAI 2015 Popular items (short head) Rest of items (long tail) Items Users User-item interaction Unobserved preference
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Majority bias in recommendation Cañamares & Castells SIGIR 2018, Cremonesi et al. RecSys 2010, Jannach et al. UMUAI 2015 Popular items (short head) Rest of items (long tail) Items Users Test data (relevant) Unobserved preference User-item interaction P@𝑘 ∼ 𝑘 Users 𝑘 Bias in offline evaluation Ranking by # positive ratings 0.3 0.2 0.1 0 nDCG@10 0 0.1 0.2 0.3 Random Avg. rating Nr. ratings User-based Matrix fact. nDCG@10 Netflix
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Majority bias in recommendation Bias in algorithms Cañamares & Castells SIGIR 2017, Jannach et al. UMUAI 2015, Zhu et al. WSDM 2021 MovieLens 1M dataset 0 400 800 0 1000 2000 Matrix factorization # positive ratings Nº times top 10 800 400 0 0 1000 2000 0 1000 2000 0 1000 2000 User-based kNN # positive ratings 2000 1000 0 0 1000 2000 0 1000 2000 3000 0 1000 2000 Item-based kNN # positive ratings 3000 1000 0 0 1000 2000 2000
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Majority bias in recommendation • “Bias = distortion” • Majority → herd behavior Are we getting it all wrong?
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Is the majority bias useful? Irrational
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Is the majority bias useful? Should I follow the crowd? Rational
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Popular recommendations Things to do in Paris 1. Musée d’Orsay 2. Notre-Dame de Paris 3. Sainte Chapelle 4. Palais Garnier 4. Eiffel Tower 6. Musée l’Orangerie 7. Arc de Triomphe 8. Louvre Museum 9. Montmartre
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Popular recommendations Things to do in Paris 1. Musée d’Orsay 2. Notre-Dame de Paris 3. Sainte Chapelle 4. Palais Garnier 4. Eiffel Tower 6. Musée l’Orangerie 7. Arc de Triomphe 8. Louvre Museum 9. Montmartre
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 1 2 3 4 5 6 7 8 9 10 0 20 30 Songs 0% 5% 10% How reliable is the majority bias? Salganik, Dodd & Watt’s experiment Salganik et al. Science 2006 ➢ Degree of randomness in success of song ➢ Social influence… • reinforces majority skew • increases irrationality 48 songs Listen & freely download See download count Don’t see download count 14k people in 8+1 parallel “worlds” Downloads 10% 5% 0% Worlds # downloads rank One example song
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Is the majority bias useful? Menczer’s simulation: popularity raking vs. quality “Follow the crowd” behavior Avg quality of choice “Laziness” (top-heaviness in popularity ranking) Ciampaglia et al. Sci. Rep. 2018 Simulation: • People choose items and interact • People see interaction count • Parameterized choice behavior: herd vs. quality, browsing effort Rational herd area: optimum = mild herd behavior + moderate effort budget
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Is the majority bias reliable Recommender systems: rational or irrational herd? Rec. system Rec. system
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Outline 1. Fairness and bias 2. Bias in recommendation 3. Majority bias in offline evaluation 4. Conclusion
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 1. Fairness and bias 2. Bias in recommendation 3. Majority bias in offline evaluation 4. Conclusion Outline
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 𝑢 Where do biases come from? Cañamares & Castells SIGIR 2018 Discover Engage 𝑖 Popularity distribution 𝑝 𝑂 𝑖 Recommender system Recommend Your ad here Advertisement Friends Random chance Search Observe External factors The feedback loop Internal factors Relevant
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Internal factors Relevant Where do biases come from? Cañamares & Castells SIGIR 2018 Popularity distribution 𝑝 𝑂 𝑖 Recommender system Your ad here Advertisement Friends Random chance Search External factors 𝑢 Engage 𝑖 Recommend Observe Discover Retrain (LTR) The feedback loop
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 The feedback loop Impressions High rank User engagement Model training More impressions Higher rank More user engagement Model training   · · ·  Model training Offline evaluation improvement Algorithm selection re-confirmed  Recommend Recommend
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Remove the bias  In the metrics Stratified recall Off-policy evaluation  In the algorithms Steck RecSys 2011, Schnabel et al. ICML 2016, Swaminathan et. al NIPS 2017, Yang et al. RecSys 2018, etc. Test data subsampling procedures Unbiased datasets, e.g. Yahoo! R3, Coat, CM100K  In the data Counterfactual learning Multi-armed bandits
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Remove the bias  In the metrics Stratified recall Off-policy evaluation  In the algorithms Steck RecSys 2011, Schnabel et al. ICML 2016, Swaminathan et. al NIPS 2017, Yang et al. RecSys 2018, etc. Test data subsampling procedures Unbiased datasets, e.g. Yahoo! R3, Coat, CM100K  In the data Counterfactual learning Multi-armed bandits • Model/learn the observation bias 𝑝 𝑂𝑢.𝑖 • Factor it out : explore beyond the feedback loop
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Biased vs. unbiased evaluation 10 random tracks per user 1,000 music tracks Missing data 5,400 users Yahoo! R3 dataset Free user interaction 130K ratings Marlin & Zemel RecSys 2009 Non-random data Random data
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Biased vs. unbiased evaluation Cañamares & Castells SIGIR 2018, Mena et al. ACM TOIS in press Unbiased evaluation Biased evaluation Users Non-random data Random data Items Training Missing data Test Items Training Missing data Users
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Biased vs. unbiased evaluation Mena et al. ACM TOIS in press, Cañamares & Castells SIGIR 2018 Yahoo! R3 – 16 algorithms Biased P@10 Unbiased P@10 Pop Item KNN LRMF Rnd SLIM SVD++ RSGD User KNN Avg PNMF EALS GBPR GPLSA BPMF WRMF WBPR 0 0.002 0.004 0.006 0.008 0.01 0 0.02 0.04 0.06 Real P @10 Where is the bias? • Why such agreement? • Can we always expect this? • When and when not?
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Conditional (in)dependences between variables Cañamares & Castells SIGIR 2018 𝑢 Engage Observe Relevant Popularity distribution 𝑝 𝑂 𝑖 0.3 0.2 0.1 0 nDCG@10 0 0.1 0.2 0.3 Random Avg. rating Nr. ratings User-based Matrix fact. nDCG@10 Netflix Evaluation Recommender system Discover 𝑖 𝑅 𝑂 𝑖 𝑅 𝑂
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 𝑢 Conditional (in)dependences between variables 1. Observation depends just on relevance 2. Observation independent from relevance 3. Observation depends both on the item and relevance Discover Popularity distribution 𝑝 𝑂 𝑖 0.3 0.2 0.1 0 nDCG@10 0 0.1 0.2 0.3 Random Avg. rating Nr. ratings User-based Matrix fact. nDCG@10 Netflix Evaluation Engage Recommender system 𝑖 𝑅 𝑂 𝑖 𝑅 𝑂 𝑖 𝑅 𝑂 Relevant Observe 𝑝 𝑂 𝑅, 𝑖 = 𝑝 𝑂 𝑅 𝑝 𝑂 𝑅, 𝑖 = 𝑝 𝑂 𝑖 𝑖 𝑅 𝑂
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Harmless bias Cañamares & Castells SIGIR 2018 Unbiased P@10 Biased P@10 Comparisons are preserved Rational majority emerges 1. Observation depends strongly on relevance (simulation) • You find items through search engines, good recommender systems, good friends • You engage based on whether or not you like the choices 𝑖 𝑅 𝑂
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 • Heavy advertisement • Conformity, manipulation • Reinforcement loops Harmless vs. misleading bias 0 0.1 0.2 0.3 0.4 0 0.1 0.2 0.3 0.4 0.5 𝑝 𝑂 𝑖 Uncorrelated observation and relevance 𝑝 𝑅 𝑖 Unbiased P@10 Biased P@10 Just noise Cañamares & Castells SIGIR 2018, Mena et al. ACM TOIS in press 0 0.1 0.2 0.3 0.4 0 0.1 0.2 0.3 0.4 0.5 𝑝 𝑂 𝑖 𝑝 𝑅 𝑖 Observation opposes relevance Unbiased P@10 Biased P@10 2. Observation independent from relevance given item (simulations) 𝑖 𝑅 𝑂 Misleading bias
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Misleading bias • Negativity + virality • Item quality increase over time Cañamares & Castells SIGIR 2018, Mena et al. ACM TOIS in press Misleading crowd effects emerge Misleading bias Unbiased P@10 Biased P@10 0 0.1 0.2 0.3 0.4 0 0.1 0.2 0.3 0.4 0.5 𝑝 𝑂 𝑖 𝑝 𝑅 𝑖 Observation opposes relevance • Heavy advertisement • Conformity, manipulation • Reinforcement loops 2. Observation independent from relevance given item 𝑖 𝑅 𝑂
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 General case: real data Mena et al. ACM TOIS in press Biased P@10 Unbiased P@10 Pop Item KNN LRMF Rnd SLIM SVD++ RSGD User KNN Avg PNMF EALS GBPR GPLSA BPMF WRMF WBPR 0 0.002 0.004 0.006 0.008 0.01 0 0.02 0.04 0.06 Real P @10 Results would suggest that the typical case is a mix of 1. noticeable relevance dependence 2. item dependence with positive relevance correlation Rational trend 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 𝑝 𝑂 𝑖 𝑝 𝑂 𝑅, 𝑖 Full dependence on relevance Yahoo! R3 0 0.1 0.2 0.3 0.4 0 0.1 0.2 0.3 0.4 0.5 𝑝 𝑅 𝑖 𝑝 𝑂 𝑖 𝑖 𝑅 𝑂 3. Observation depends on both relevance and item
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Outline 1. Fairness and bias 2. Bias in recommendation 3. Majority bias in offline evaluation 4. Conclusion
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 1. Fairness and bias 2. Bias in recommendation 3. Majority bias in offline evaluation 4. Conclusion Outline
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Conclusion and final thoughts  Majority-biased recommendation is rational if… – user findings and choices are guided by relevance (regardless of positivity/negativity) – when relevance is ignored, majority rank doesn’t oppose relevance  A mix of those two trends seems common – The majority bias is useful for recommendation, (broadly) harmless to evaluation  But the majority bias can become misleading when popularity attracts negativity
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Beyond relevance  Bias is not just a relevance issue  Novelty  Fairness  Generality Equal opportunity: 𝑝 recommend 𝑅, 𝑖 = 𝑝 recommend 𝑅 Affirmative action: relevance builds on exposure Lost opportunities: new choices, new algorithms, better choice among relevants Leave the crowd, take risk and discover What do we mean by “relevant”, e.g. value/utility vs. rush of adrenaline Replace “relevance” by “well-being”, “truth”…? User-specific bias? Intrinsic value of fairness, diversity, novelty…
IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Thank you!

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Rational and irrational bias in recommendation

  • 1. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Rational and irrational bias in recommendation The Information Retrieval General Reading Group Pablo Castells Universidad Autónoma de Madrid http://ir.ii.uam.es/castells RMIT, June 3, 2021
  • 2. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Outline 1. Fairness and bias 2. Bias in recommendation 3. Majority bias in offline evaluation 4. Conclusion
  • 3. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 1. Fairness and bias 2. Bias in recommendation 3. Majority bias in offline evaluation 4. Conclusion Outline
  • 4. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Fairness angles  Fairness is complex  Has multiple definitions  Different angles  No universal formula Fair ≠ uniform Olteanu et al. Frontiers in Big Data 2019, Baeza Comm. ACM 2018, Ntoutsi et al. Rev. Data Min. Knowl. Discov. 2020
  • 5. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Fairness angles Equal representation 𝑝 group 𝐴 hire = 𝑝 group 𝐵 hire Statistical parity 𝑝 hire group 𝐴 = 𝑝 hire group 𝐵 Equal opportunity 𝑝 hire qualifications = 𝑥, group 𝐴 = 𝑝 hire qualifications = 𝑥, group 𝐵 Fair ≠ uniform Olteanu et al. Frontiers in Big Data 2019, Baeza Comm. ACM 2018, Ntoutsi et al. Rev. Data Min. Knowl. Discov. 2020, Pleiss et al NIPS 2017.
  • 6. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 > Bias for… • Rational thinking • Scientific method • Axioms of geometry • Trust on scientific authority Bias is… bad?
  • 7. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Shared principles • Rationalism, ethics, human rights, well being … “Truth” is a construct • Based on method • Subjective, consensus • Model-dependent • Useful Kant Kuhn Hawking > Bias for… • Rational thinking • Scientific method • Axioms of geometry • Trust on scientific authority Bias is… bad? Fair ≠ neutral Our “axioms”?
  • 8. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Truth vs. truth system Euclidean geometry Hyperbolic geometry Elliptic geometry Axioms involve a choice What’s a “rectangle”?
  • 9. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Truth vs. truth system Let 𝑅 = 𝐴 𝐴 ∉ 𝐴 } Then 𝑅 ∈ 𝑅 ⇔ 𝑅 ∉ 𝑅 !! Axioms are not perfect – they are just meant to be useful Even in math, multiple alternative axiom systems exist, and they are often revised
  • 10. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Bias and fairness My point • Fairness and bias are arbitrarily complex questions • The importance of bias awareness and understanding to better cope with bias
  • 11. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Outline 1. Fairness and bias 2. Bias in recommendation 3. Majority bias in offline evaluation 4. Conclusion
  • 12. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 1. Fairness and bias 2. Bias in recommendation 3. Majority bias in offline evaluation 4. Conclusion Outline
  • 13. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Recommender systems ? ? ? ? ? ? ? ? ?
  • 14. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Majority bias in recommendation Best sellers Long-tail items Items Users Popular items Rest of items (long tail) Items # Interactions Bias in the data
  • 15. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Majority bias in recommendation Bias in offline evaluation Ranking by # positive ratings 0.3 0.2 0.1 0 nDCG@10 0 0.1 0.2 0.3 Random Avg. rating Nr. ratings User-based Matrix fact. nDCG@10 Netflix Cañamares & Castells SIGIR 2018, Cremonesi et al. RecSys 2010, Jannach et al. UMUAI 2015 Popular items (short head) Rest of items (long tail) Items Users User-item interaction Unobserved preference
  • 16. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Majority bias in recommendation Cañamares & Castells SIGIR 2018, Cremonesi et al. RecSys 2010, Jannach et al. UMUAI 2015 Popular items (short head) Rest of items (long tail) Items Users Test data (relevant) Unobserved preference User-item interaction P@𝑘 ∼ 𝑘 Users 𝑘 Bias in offline evaluation Ranking by # positive ratings 0.3 0.2 0.1 0 nDCG@10 0 0.1 0.2 0.3 Random Avg. rating Nr. ratings User-based Matrix fact. nDCG@10 Netflix
  • 17. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Majority bias in recommendation Bias in algorithms Cañamares & Castells SIGIR 2017, Jannach et al. UMUAI 2015, Zhu et al. WSDM 2021 MovieLens 1M dataset 0 400 800 0 1000 2000 Matrix factorization # positive ratings Nº times top 10 800 400 0 0 1000 2000 0 1000 2000 0 1000 2000 User-based kNN # positive ratings 2000 1000 0 0 1000 2000 0 1000 2000 3000 0 1000 2000 Item-based kNN # positive ratings 3000 1000 0 0 1000 2000 2000
  • 18. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Majority bias in recommendation • “Bias = distortion” • Majority → herd behavior Are we getting it all wrong?
  • 19. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Is the majority bias useful? Irrational
  • 20. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Is the majority bias useful? Should I follow the crowd? Rational
  • 21. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Popular recommendations Things to do in Paris 1. Musée d’Orsay 2. Notre-Dame de Paris 3. Sainte Chapelle 4. Palais Garnier 4. Eiffel Tower 6. Musée l’Orangerie 7. Arc de Triomphe 8. Louvre Museum 9. Montmartre
  • 22. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Popular recommendations Things to do in Paris 1. Musée d’Orsay 2. Notre-Dame de Paris 3. Sainte Chapelle 4. Palais Garnier 4. Eiffel Tower 6. Musée l’Orangerie 7. Arc de Triomphe 8. Louvre Museum 9. Montmartre
  • 23. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 1 2 3 4 5 6 7 8 9 10 0 20 30 Songs 0% 5% 10% How reliable is the majority bias? Salganik, Dodd & Watt’s experiment Salganik et al. Science 2006 ➢ Degree of randomness in success of song ➢ Social influence… • reinforces majority skew • increases irrationality 48 songs Listen & freely download See download count Don’t see download count 14k people in 8+1 parallel “worlds” Downloads 10% 5% 0% Worlds # downloads rank One example song
  • 24. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Is the majority bias useful? Menczer’s simulation: popularity raking vs. quality “Follow the crowd” behavior Avg quality of choice “Laziness” (top-heaviness in popularity ranking) Ciampaglia et al. Sci. Rep. 2018 Simulation: • People choose items and interact • People see interaction count • Parameterized choice behavior: herd vs. quality, browsing effort Rational herd area: optimum = mild herd behavior + moderate effort budget
  • 25. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Is the majority bias reliable Recommender systems: rational or irrational herd? Rec. system Rec. system
  • 26. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Outline 1. Fairness and bias 2. Bias in recommendation 3. Majority bias in offline evaluation 4. Conclusion
  • 27. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 1. Fairness and bias 2. Bias in recommendation 3. Majority bias in offline evaluation 4. Conclusion Outline
  • 28. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 𝑢 Where do biases come from? Cañamares & Castells SIGIR 2018 Discover Engage 𝑖 Popularity distribution 𝑝 𝑂 𝑖 Recommender system Recommend Your ad here Advertisement Friends Random chance Search Observe External factors The feedback loop Internal factors Relevant
  • 29. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Internal factors Relevant Where do biases come from? Cañamares & Castells SIGIR 2018 Popularity distribution 𝑝 𝑂 𝑖 Recommender system Your ad here Advertisement Friends Random chance Search External factors 𝑢 Engage 𝑖 Recommend Observe Discover Retrain (LTR) The feedback loop
  • 30. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 The feedback loop Impressions High rank User engagement Model training More impressions Higher rank More user engagement Model training   · · ·  Model training Offline evaluation improvement Algorithm selection re-confirmed  Recommend Recommend
  • 31. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Remove the bias  In the metrics Stratified recall Off-policy evaluation  In the algorithms Steck RecSys 2011, Schnabel et al. ICML 2016, Swaminathan et. al NIPS 2017, Yang et al. RecSys 2018, etc. Test data subsampling procedures Unbiased datasets, e.g. Yahoo! R3, Coat, CM100K  In the data Counterfactual learning Multi-armed bandits
  • 32. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Remove the bias  In the metrics Stratified recall Off-policy evaluation  In the algorithms Steck RecSys 2011, Schnabel et al. ICML 2016, Swaminathan et. al NIPS 2017, Yang et al. RecSys 2018, etc. Test data subsampling procedures Unbiased datasets, e.g. Yahoo! R3, Coat, CM100K  In the data Counterfactual learning Multi-armed bandits • Model/learn the observation bias 𝑝 𝑂𝑢.𝑖 • Factor it out : explore beyond the feedback loop
  • 33. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Biased vs. unbiased evaluation 10 random tracks per user 1,000 music tracks Missing data 5,400 users Yahoo! R3 dataset Free user interaction 130K ratings Marlin & Zemel RecSys 2009 Non-random data Random data
  • 34. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Biased vs. unbiased evaluation Cañamares & Castells SIGIR 2018, Mena et al. ACM TOIS in press Unbiased evaluation Biased evaluation Users Non-random data Random data Items Training Missing data Test Items Training Missing data Users
  • 35. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Biased vs. unbiased evaluation Mena et al. ACM TOIS in press, Cañamares & Castells SIGIR 2018 Yahoo! R3 – 16 algorithms Biased P@10 Unbiased P@10 Pop Item KNN LRMF Rnd SLIM SVD++ RSGD User KNN Avg PNMF EALS GBPR GPLSA BPMF WRMF WBPR 0 0.002 0.004 0.006 0.008 0.01 0 0.02 0.04 0.06 Real P @10 Where is the bias? • Why such agreement? • Can we always expect this? • When and when not?
  • 36. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Conditional (in)dependences between variables Cañamares & Castells SIGIR 2018 𝑢 Engage Observe Relevant Popularity distribution 𝑝 𝑂 𝑖 0.3 0.2 0.1 0 nDCG@10 0 0.1 0.2 0.3 Random Avg. rating Nr. ratings User-based Matrix fact. nDCG@10 Netflix Evaluation Recommender system Discover 𝑖 𝑅 𝑂 𝑖 𝑅 𝑂
  • 37. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 𝑢 Conditional (in)dependences between variables 1. Observation depends just on relevance 2. Observation independent from relevance 3. Observation depends both on the item and relevance Discover Popularity distribution 𝑝 𝑂 𝑖 0.3 0.2 0.1 0 nDCG@10 0 0.1 0.2 0.3 Random Avg. rating Nr. ratings User-based Matrix fact. nDCG@10 Netflix Evaluation Engage Recommender system 𝑖 𝑅 𝑂 𝑖 𝑅 𝑂 𝑖 𝑅 𝑂 Relevant Observe 𝑝 𝑂 𝑅, 𝑖 = 𝑝 𝑂 𝑅 𝑝 𝑂 𝑅, 𝑖 = 𝑝 𝑂 𝑖 𝑖 𝑅 𝑂
  • 38. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Harmless bias Cañamares & Castells SIGIR 2018 Unbiased P@10 Biased P@10 Comparisons are preserved Rational majority emerges 1. Observation depends strongly on relevance (simulation) • You find items through search engines, good recommender systems, good friends • You engage based on whether or not you like the choices 𝑖 𝑅 𝑂
  • 39. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 • Heavy advertisement • Conformity, manipulation • Reinforcement loops Harmless vs. misleading bias 0 0.1 0.2 0.3 0.4 0 0.1 0.2 0.3 0.4 0.5 𝑝 𝑂 𝑖 Uncorrelated observation and relevance 𝑝 𝑅 𝑖 Unbiased P@10 Biased P@10 Just noise Cañamares & Castells SIGIR 2018, Mena et al. ACM TOIS in press 0 0.1 0.2 0.3 0.4 0 0.1 0.2 0.3 0.4 0.5 𝑝 𝑂 𝑖 𝑝 𝑅 𝑖 Observation opposes relevance Unbiased P@10 Biased P@10 2. Observation independent from relevance given item (simulations) 𝑖 𝑅 𝑂 Misleading bias
  • 40. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Misleading bias • Negativity + virality • Item quality increase over time Cañamares & Castells SIGIR 2018, Mena et al. ACM TOIS in press Misleading crowd effects emerge Misleading bias Unbiased P@10 Biased P@10 0 0.1 0.2 0.3 0.4 0 0.1 0.2 0.3 0.4 0.5 𝑝 𝑂 𝑖 𝑝 𝑅 𝑖 Observation opposes relevance • Heavy advertisement • Conformity, manipulation • Reinforcement loops 2. Observation independent from relevance given item 𝑖 𝑅 𝑂
  • 41. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 General case: real data Mena et al. ACM TOIS in press Biased P@10 Unbiased P@10 Pop Item KNN LRMF Rnd SLIM SVD++ RSGD User KNN Avg PNMF EALS GBPR GPLSA BPMF WRMF WBPR 0 0.002 0.004 0.006 0.008 0.01 0 0.02 0.04 0.06 Real P @10 Results would suggest that the typical case is a mix of 1. noticeable relevance dependence 2. item dependence with positive relevance correlation Rational trend 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 𝑝 𝑂 𝑖 𝑝 𝑂 𝑅, 𝑖 Full dependence on relevance Yahoo! R3 0 0.1 0.2 0.3 0.4 0 0.1 0.2 0.3 0.4 0.5 𝑝 𝑅 𝑖 𝑝 𝑂 𝑖 𝑖 𝑅 𝑂 3. Observation depends on both relevance and item
  • 42. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Outline 1. Fairness and bias 2. Bias in recommendation 3. Majority bias in offline evaluation 4. Conclusion
  • 43. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 1. Fairness and bias 2. Bias in recommendation 3. Majority bias in offline evaluation 4. Conclusion Outline
  • 44. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Conclusion and final thoughts  Majority-biased recommendation is rational if… – user findings and choices are guided by relevance (regardless of positivity/negativity) – when relevance is ignored, majority rank doesn’t oppose relevance  A mix of those two trends seems common – The majority bias is useful for recommendation, (broadly) harmless to evaluation  But the majority bias can become misleading when popularity attracts negativity
  • 45. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Beyond relevance  Bias is not just a relevance issue  Novelty  Fairness  Generality Equal opportunity: 𝑝 recommend 𝑅, 𝑖 = 𝑝 recommend 𝑅 Affirmative action: relevance builds on exposure Lost opportunities: new choices, new algorithms, better choice among relevants Leave the crowd, take risk and discover What do we mean by “relevant”, e.g. value/utility vs. rush of adrenaline Replace “relevance” by “well-being”, “truth”…? User-specific bias? Intrinsic value of fairness, diversity, novelty…
  • 46. IRG IRGroup @UAM Rational and irrational bias in recommendation The Information Retrieval General Reading Group RMIT, June 3, 2021 Thank you!