{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":1,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":20,"n_cols":5,"rows_kept":20,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[10,2,10,3],[11,2,11,3],[15,2,15,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNISA996478864203316"]
[null,null,"Titolo",null,"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]"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-031-09316-X"]
[null,null,"Descrizione fisica",null,"1 online resource (166 pages)"]
[null,null,"Collana",null,"Communications in Computer and Information Science ; ; v.1610"]
[null,null,"Disciplina",null,"025.524"]
[null,null,"Soggetti",null,"Algorithms"]
[{"$nulls":4},"Information retrieval"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"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 "]
[{"$nulls":4},"ML-100k -- 6 Discussion -- References -- Towards an Approach for Analyzing Dynamic Aspects of Bias and Beyond-Accuracy Measures -- 1 Introduction -- 2 Understanding Long-Term Dynamics -- 3 Case Study -- 4 Conclusion -- References -- A Crowdsourcing Methodology to Measure Algorithmic Bias in Black-Box Systems: A Case Study with COVID-Related Searches -- 1 Introduction -- 2 Preliminaries -- 2.1 Auditing Algorithmic Bias."]
[{"$nulls":4},"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."]
[{"$nulls":4},"3.1 Data -- 3.2 Recommendation Models -- 3.3 Calibration -- 4 Results -- 4.1 Analysis of Bias in the Calibration Algorithm -- 4.2 Analysis of Recommendation Accuracy in Calibration Process -- 5 Conclusions and Future Work -- References -- Do Perceived Gender Biases in Retrieval Results Affect Relevance Judgements? -- 1 Introduction -- 2 Related Work -- 3 Experiment Setup -- 4 Results and Discussion -- 4.1 Gender-Agnostic Experiments -- 4.2 Gender-Specific Experiments -- 4.3 Discussion -- 4.4 Limitations of the Experiments -- 5 Conclusion and Future Work -- References -- Enhancing Fairness in Classification Tasks with Multiple Variables: A Data- and Model-Agnostic Approach -- 1 Introduction -- 2 Background Knowledge and Related Work -- 2.1 Fairness Definition -- 2.2 Related Works -- 3 Debiaser for Multiple Variables (DEMV) -- 4 Experimental Analysis -- 4.1 Employed Datasets -- 4.2 Experimental Results -- 5 Conclusion and Future Work -- References -- Keyword Recommendation for Fair Search -- 1 Introduction -- 2 Related Work -- 3 Objective -- 4 Proposed Method -- 4.1 Problem Setup -- 4.2 The FairKR Framework and Implementation -- 4.3 Limitations -- 5 Experimental Setup -- 5.1 Data -- 5.2 Search Engine -- 6 Analysis and Discussion -- 7 Conclusion and Future Work -- References -- FARGO: A Fair, Context-AwaRe, Group RecOmmender System -- 1 Introduction -- 2 Related Work -- 3 The Proposed Method -- 3.1 CtxInfl -- 3.2 "]
[{"$nulls":4},"FARGO -- 4 Experimental Results -- 4.1 TV Dataset -- 4.2 Music Dataset -- 5 Conclusions -- References -- Author Index."]