Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Advances in bias and fairness in information retrieval : third international workshop, BIAS 2022, Stavanger, Norway, April 10, 2022, revised selected papers / / edited by Ludovico Boratto [and three others]
| Advances in bias and fairness in information retrieval : third international workshop, BIAS 2022, Stavanger, Norway, April 10, 2022, revised selected papers / / edited by Ludovico Boratto [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (166 pages) |
| Disciplina | 025.524 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Algorithms
Information retrieval |
| ISBN | 3-031-09316-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Advances in Bias and Fairness in Information Retrieval: Preface -- Organization -- Contents -- Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Defining Popularity -- 3.2 Multimedia Datasets -- 3.3 Recommendation Algorithms and Evaluation Protocol -- 4 Results -- 4.1 RQ1: Relationship Between Item Popularity and Recommendation Frequency -- 4.2 RQ2: Relationship Between Users' Inclination to Popular Items and Recommendation Accuracy -- 5 Conclusion -- References -- The Impact of Recommender System and Users' Behaviour on Choices' Distribution and Quality -- 1 Introduction -- 2 Related Works -- 2.1 Impact of Recommender System on Users' Choice Distribution -- 2.2 Impact of Choice Model on Users' Choice Distribution -- 3 Simulation of Users' Choices -- 4 Experimental Analysis -- 5 Conclusion -- References -- Sequential Nature of Recommender Systems Disrupts the Evaluation Process -- 1 Introduction -- 1.1 Related Works -- 1.2 Notation and Definitions -- 2 Evaluation Systems and Adaptation -- 2.1 Ladder Mechanism -- 2.2 Adversarial Attacks -- 3 Recommender Systems as Sequential Decision Makers -- 3.1 k-NN Recommender System -- 4 Sequence-Aware Adversarial Attacks -- 4.1 Random Window Boosting Attack (WBoost) -- 4.2 k-NN Posterior Boosting Attack (PostBoost) -- 5 Experiments -- 5.1 Evaluation on Synthetic Data -- 5.2 Evaluation on ML-100k -- 6 Discussion -- References -- Towards an Approach for Analyzing Dynamic Aspects of Bias and Beyond-Accuracy Measures -- 1 Introduction -- 2 Understanding Long-Term Dynamics -- 3 Case Study -- 4 Conclusion -- References -- A Crowdsourcing Methodology to Measure Algorithmic Bias in Black-Box Systems: A Case Study with COVID-Related Searches -- 1 Introduction -- 2 Preliminaries -- 2.1 Auditing Algorithmic Bias.
2.2 Auditing Platforms and Search Engines -- 2.3 Crowdsourcing Platform: Amazon Mechanical Turk -- 2.4 Measuring Similarity Among SERPs -- 3 Methodology -- 3.1 Crowdsourcing Search Engine Result Pages -- 3.2 Queries -- 4 Results and Discussion -- 4.1 Collected Data -- 4.2 Demographics -- 4.3 Do Different Participants Get Different Search Results for the Same Queries? -- 5 Do Results Vary Between Positive and Negative Query Formulations? -- 6 Conclusion -- References -- The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation -- 1 Introduction -- 2 Related Work -- 3 Experimental Setup -- 3.1 Datasets -- 3.2 Evaluation Metrics -- 4 Popularity Bias in POI Data (RQ1) -- 4.1 Consumption Distribution of POIs -- 4.2 User Profiles and Popularity Bias -- 5 Results and Discussion -- 5.1 Trade-Off on Accuracy, User and Item Fairness (RQ2) -- 5.2 Popularity Bias in POI Recommendation (RQ3) -- 6 Conclusion and Future Work -- References -- The Unfairness of Popularity Bias in Book Recommendation -- 1 Introduction -- 2 Popularity Bias in Data -- 2.1 Reading Distribution of Books -- 2.2 User Profile Size and Popularity Bias in Book Data -- 3 Popularity Bias in Book Recommendation -- 3.1 Recommendation of Popular Books -- 3.2 Popularity Bias for Different User Groups -- 3.3 Unfairness of Popularity Bias vs. Personalization -- 4 Discussion -- 5 Conclusion and Future Work -- References -- Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches -- 1 Introduction and Background -- 2 Research Methodology -- 2.1 Baseline Algorithms and Re-ranking Algorithms -- 2.2 Metrics -- 2.3 Datasets -- 3 Results -- 4 Summary and Future Work -- References -- Analysis of Biases in Calibrated Recommendations -- 1 Introduction -- 2 Related Work -- 2.1 Biases in Recommender Systems -- 2.2 Calibration -- 3 Preliminaries. 3.1 Data -- 3.2 Recommendation Models -- 3.3 Calibration -- 4 Results -- 4.1 Analysis of Bias in the Calibration Algorithm -- 4.2 Analysis of Recommendation Accuracy in Calibration Process -- 5 Conclusions and Future Work -- References -- Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements? -- 1 Introduction -- 2 Related Work -- 3 Experiment Setup -- 4 Results and Discussion -- 4.1 Gender-Agnostic Experiments -- 4.2 Gender-Specific Experiments -- 4.3 Discussion -- 4.4 Limitations of the Experiments -- 5 Conclusion and Future Work -- References -- Enhancing Fairness in Classification Tasks with Multiple Variables: A Data- and Model-Agnostic Approach -- 1 Introduction -- 2 Background Knowledge and Related Work -- 2.1 Fairness Definition -- 2.2 Related Works -- 3 Debiaser for Multiple Variables (DEMV) -- 4 Experimental Analysis -- 4.1 Employed Datasets -- 4.2 Experimental Results -- 5 Conclusion and Future Work -- References -- Keyword Recommendation for Fair Search -- 1 Introduction -- 2 Related Work -- 3 Objective -- 4 Proposed Method -- 4.1 Problem Setup -- 4.2 The FairKR Framework and Implementation -- 4.3 Limitations -- 5 Experimental Setup -- 5.1 Data -- 5.2 Search Engine -- 6 Analysis and Discussion -- 7 Conclusion and Future Work -- References -- FARGO: A Fair, Context-AwaRe, Group RecOmmender System -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 CtxInfl -- 3.2 FARGO -- 4 Experimental Results -- 4.1 TV Dataset -- 4.2 Music Dataset -- 5 Conclusions -- References -- Author Index. |
| Record Nr. | UNISA-996478864203316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Advances in bias and fairness in information retrieval : third international workshop, BIAS 2022, Stavanger, Norway, April 10, 2022, revised selected papers / / edited by Ludovico Boratto [and three others]
| Advances in bias and fairness in information retrieval : third international workshop, BIAS 2022, Stavanger, Norway, April 10, 2022, revised selected papers / / edited by Ludovico Boratto [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (166 pages) |
| Disciplina | 025.524 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Algorithms
Information retrieval |
| ISBN | 3-031-09316-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Advances in Bias and Fairness in Information Retrieval: Preface -- Organization -- Contents -- Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Defining Popularity -- 3.2 Multimedia Datasets -- 3.3 Recommendation Algorithms and Evaluation Protocol -- 4 Results -- 4.1 RQ1: Relationship Between Item Popularity and Recommendation Frequency -- 4.2 RQ2: Relationship Between Users' Inclination to Popular Items and Recommendation Accuracy -- 5 Conclusion -- References -- The Impact of Recommender System and Users' Behaviour on Choices' Distribution and Quality -- 1 Introduction -- 2 Related Works -- 2.1 Impact of Recommender System on Users' Choice Distribution -- 2.2 Impact of Choice Model on Users' Choice Distribution -- 3 Simulation of Users' Choices -- 4 Experimental Analysis -- 5 Conclusion -- References -- Sequential Nature of Recommender Systems Disrupts the Evaluation Process -- 1 Introduction -- 1.1 Related Works -- 1.2 Notation and Definitions -- 2 Evaluation Systems and Adaptation -- 2.1 Ladder Mechanism -- 2.2 Adversarial Attacks -- 3 Recommender Systems as Sequential Decision Makers -- 3.1 k-NN Recommender System -- 4 Sequence-Aware Adversarial Attacks -- 4.1 Random Window Boosting Attack (WBoost) -- 4.2 k-NN Posterior Boosting Attack (PostBoost) -- 5 Experiments -- 5.1 Evaluation on Synthetic Data -- 5.2 Evaluation on ML-100k -- 6 Discussion -- References -- Towards an Approach for Analyzing Dynamic Aspects of Bias and Beyond-Accuracy Measures -- 1 Introduction -- 2 Understanding Long-Term Dynamics -- 3 Case Study -- 4 Conclusion -- References -- A Crowdsourcing Methodology to Measure Algorithmic Bias in Black-Box Systems: A Case Study with COVID-Related Searches -- 1 Introduction -- 2 Preliminaries -- 2.1 Auditing Algorithmic Bias.
2.2 Auditing Platforms and Search Engines -- 2.3 Crowdsourcing Platform: Amazon Mechanical Turk -- 2.4 Measuring Similarity Among SERPs -- 3 Methodology -- 3.1 Crowdsourcing Search Engine Result Pages -- 3.2 Queries -- 4 Results and Discussion -- 4.1 Collected Data -- 4.2 Demographics -- 4.3 Do Different Participants Get Different Search Results for the Same Queries? -- 5 Do Results Vary Between Positive and Negative Query Formulations? -- 6 Conclusion -- References -- The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation -- 1 Introduction -- 2 Related Work -- 3 Experimental Setup -- 3.1 Datasets -- 3.2 Evaluation Metrics -- 4 Popularity Bias in POI Data (RQ1) -- 4.1 Consumption Distribution of POIs -- 4.2 User Profiles and Popularity Bias -- 5 Results and Discussion -- 5.1 Trade-Off on Accuracy, User and Item Fairness (RQ2) -- 5.2 Popularity Bias in POI Recommendation (RQ3) -- 6 Conclusion and Future Work -- References -- The Unfairness of Popularity Bias in Book Recommendation -- 1 Introduction -- 2 Popularity Bias in Data -- 2.1 Reading Distribution of Books -- 2.2 User Profile Size and Popularity Bias in Book Data -- 3 Popularity Bias in Book Recommendation -- 3.1 Recommendation of Popular Books -- 3.2 Popularity Bias for Different User Groups -- 3.3 Unfairness of Popularity Bias vs. Personalization -- 4 Discussion -- 5 Conclusion and Future Work -- References -- Mitigating Popularity Bias in Recommendation: Potential and Limits of Calibration Approaches -- 1 Introduction and Background -- 2 Research Methodology -- 2.1 Baseline Algorithms and Re-ranking Algorithms -- 2.2 Metrics -- 2.3 Datasets -- 3 Results -- 4 Summary and Future Work -- References -- Analysis of Biases in Calibrated Recommendations -- 1 Introduction -- 2 Related Work -- 2.1 Biases in Recommender Systems -- 2.2 Calibration -- 3 Preliminaries. 3.1 Data -- 3.2 Recommendation Models -- 3.3 Calibration -- 4 Results -- 4.1 Analysis of Bias in the Calibration Algorithm -- 4.2 Analysis of Recommendation Accuracy in Calibration Process -- 5 Conclusions and Future Work -- References -- Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements? -- 1 Introduction -- 2 Related Work -- 3 Experiment Setup -- 4 Results and Discussion -- 4.1 Gender-Agnostic Experiments -- 4.2 Gender-Specific Experiments -- 4.3 Discussion -- 4.4 Limitations of the Experiments -- 5 Conclusion and Future Work -- References -- Enhancing Fairness in Classification Tasks with Multiple Variables: A Data- and Model-Agnostic Approach -- 1 Introduction -- 2 Background Knowledge and Related Work -- 2.1 Fairness Definition -- 2.2 Related Works -- 3 Debiaser for Multiple Variables (DEMV) -- 4 Experimental Analysis -- 4.1 Employed Datasets -- 4.2 Experimental Results -- 5 Conclusion and Future Work -- References -- Keyword Recommendation for Fair Search -- 1 Introduction -- 2 Related Work -- 3 Objective -- 4 Proposed Method -- 4.1 Problem Setup -- 4.2 The FairKR Framework and Implementation -- 4.3 Limitations -- 5 Experimental Setup -- 5.1 Data -- 5.2 Search Engine -- 6 Analysis and Discussion -- 7 Conclusion and Future Work -- References -- FARGO: A Fair, Context-AwaRe, Group RecOmmender System -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 CtxInfl -- 3.2 FARGO -- 4 Experimental Results -- 4.1 TV Dataset -- 4.2 Music Dataset -- 5 Conclusions -- References -- Author Index. |
| Record Nr. | UNINA-9910578689203321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in bias and fairness in information retrieval : second International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, Lucca, Italy, April 1, 2021 : proceedings / / Ludovico Boratto [and three others] editors
| Advances in bias and fairness in information retrieval : second International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, Lucca, Italy, April 1, 2021 : proceedings / / Ludovico Boratto [and three others] editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (181 pages) |
| Disciplina | 025.04 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Computer algorithms
Information retrieval Information filtering systems |
| ISBN | 3-030-78818-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910488695103321 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in bias and fairness in information retrieval : second International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, Lucca, Italy, April 1, 2021 : proceedings / / Ludovico Boratto [and three others] editors
| Advances in bias and fairness in information retrieval : second International Workshop on Algorithmic Bias in Search and Recommendation, BIAS 2021, Lucca, Italy, April 1, 2021 : proceedings / / Ludovico Boratto [and three others] editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (181 pages) |
| Disciplina | 025.04 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Computer algorithms
Information retrieval Information filtering systems |
| ISBN | 3-030-78818-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996464518503316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Bias and social aspects in search and recommendation : first International Workshop, BIAS 2020, Lisbon, Portugal, April 14, Proceedings / / Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo (eds.)
| Bias and social aspects in search and recommendation : first International Workshop, BIAS 2020, Lisbon, Portugal, April 14, Proceedings / / Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo (eds.) |
| Pubbl/distr/stampa | Cham : , : Springer, , [2020] |
| Descrizione fisica | 1 online resource : illustrations (chiefly color) |
| Disciplina | 005.56 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Recommender systems (Information filtering)
Information retrieval Discrimination |
| ISBN | 3-030-52485-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Facets of Fairness in Search and Recommendation -- Mitigating Gender Bias in Machine Learning Data Sets -- Why Do We Need To Be Bots? What Prevents Society From Detecting Biases in Recommendation Systems -- Effect of Debiasing on Information Retrieval -- Matchmaking Under Fairness Constraints: a Speed Dating Case Study -- Recommendation Filtering à la Carte for Intelligent Tutoring Systems -- Bias Goggles - Exploring the bias of Web Domains through the Eyes of the Users -- Data Pipelines for Personalized Exploration of Rated Datasets -- Beyond Accuracy in Link Prediction -- A Novel Similarity Measure for Group Recommender Systems with Optimal Time Complexity -- What Kind of Content are you Prone to Tweet? Multi-topic Preference Model for Tweeters -- Venue Suggestion Using Social-Centric Scores -- The Impact of Foursquare Checkins on Users’ Emotions on Twitter -- Improving News Personalization through Search Logs -- Analyzing the Interaction of Users with News Articles to Create Personalization Services -- Using String-Comparison measures to Improve and Evaluate Collaborative Filtering Recommender Systems -- Enriching Product Catalogs with User Opinions. |
| Record Nr. | UNISA-996465363503316 |
| Cham : , : Springer, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Bias and Social Aspects in Search and Recommendation : First International Workshop, BIAS 2020, Lisbon, Portugal, April 14, Proceedings / / edited by Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo
| Bias and Social Aspects in Search and Recommendation : First International Workshop, BIAS 2020, Lisbon, Portugal, April 14, Proceedings / / edited by Ludovico Boratto, Stefano Faralli, Mirko Marras, Giovanni Stilo |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
| Descrizione fisica | 1 online resource : illustrations (chiefly color) |
| Disciplina | 005.56 |
| Collana | Communications in Computer and Information Science |
| Soggetto topico |
Database management
Artificial intelligence Computer engineering Computer networks Social sciences - Data processing Electronic commerce Database Management System Artificial Intelligence Computer Engineering and Networks Computer Application in Social and Behavioral Sciences e-Commerce and e-Business |
| ISBN | 3-030-52485-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Facets of Fairness in Search and Recommendation -- Mitigating Gender Bias in Machine Learning Data Sets -- Why Do We Need To Be Bots? What Prevents Society From Detecting Biases in Recommendation Systems -- Effect of Debiasing on Information Retrieval -- Matchmaking Under Fairness Constraints: a Speed Dating Case Study -- Recommendation Filtering à la Carte for Intelligent Tutoring Systems -- Bias Goggles - Exploring the bias of Web Domains through the Eyes of the Users -- Data Pipelines for Personalized Exploration of Rated Datasets -- Beyond Accuracy in Link Prediction -- A Novel Similarity Measure for Group Recommender Systems with Optimal Time Complexity -- What Kind of Content are you Prone to Tweet? Multi-topic Preference Model for Tweeters -- Venue Suggestion Using Social-Centric Scores -- The Impact of Foursquare Checkins on Users’ Emotions on Twitter -- Improving News Personalization through Search Logs -- Analyzing the Interaction of Users with News Articles to Create Personalization Services -- Using String-Comparison measures to Improve and Evaluate Collaborative Filtering Recommender Systems -- Enriching Product Catalogs with User Opinions. |
| Record Nr. | UNINA-9910413447003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Group Recommender Systems : An Introduction / / by Alexander Felfernig, Ludovico Boratto, Martin Stettinger, Marko Tkalčič
| Group Recommender Systems : An Introduction / / by Alexander Felfernig, Ludovico Boratto, Martin Stettinger, Marko Tkalčič |
| Autore | Felfernig Alexander |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (xiii, 173 pages) : illustrations |
| Disciplina | 001.64 |
| Collana | SpringerBriefs in Electrical and Computer Engineering |
| Soggetto topico |
Telecommunication
Computational intelligence Artificial intelligence Image processing - Digital techniques Computer vision Communications Engineering, Networks Computational Intelligence Artificial Intelligence Computer Imaging, Vision, Pattern Recognition and Graphics |
| ISBN | 3-319-75067-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part 1. Group Recommendation Techniques -- Decision Tasks and Basic Algorithms -- Algorithms for Group Recommendation -- Evaluating Group Recommender Systems -- Part 2. Group Recommender User Interfaces -- Group Recommender Applications -- Handling Preferences -- Explanations for Groups -- Part 3. Group Decision Processes -- Further Choice Scenarios -- Biases in Group Decisions -- Personality, Emotions, and Group Dynamics -- Conclusions. |
| Record Nr. | UNINA-9910299955903321 |
Felfernig Alexander
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
HT '22 : 33rd ACM Conference on Hypertext and Social Media : June 28-July 1, 2022, Barcelona (Spain) / / Alejandro Bellogín, Ludovico Boratto, Federica Cena, editors
| HT '22 : 33rd ACM Conference on Hypertext and Social Media : June 28-July 1, 2022, Barcelona (Spain) / / Alejandro Bellogín, Ludovico Boratto, Federica Cena, editors |
| Pubbl/distr/stampa | New York : , : Association for Computing Machinery, , 2022 |
| Descrizione fisica | 1 online resource (272 pages) : illustrations |
| Disciplina | 005.754 |
| Collana | ACM Conferences |
| Soggetto topico |
Hypertext systems
Multimodal user interfaces (Computer systems) Semantic Web Natural language processing Social media |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910580196003321 |
| New York : , : Association for Computing Machinery, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||