Advances in Information Retrieval

The popularity of search engines on the World Wide Web is a testament to the broad impact of the work done by the information retrieval community over the last decades. The advances achieved by this community have not only made the World Wide Web more accessible, they have also made it appealing to consider the application of ranking algorithms to other domains, beyond the ranking of documents. One of the most interesting examples is the domain of ranking people. In this talk, I will first highlight some of the many challenges that come with deploying ranking algorithms to individuals. I will then show how mechanisms that are perfectly fine to utilize when ranking documents can have undesired or even detrimental effects when ranking people. This talk intends to stimulate a discussion on the manifold, interdisciplinary challenges around the increasing adoption of ranking algorithms in computational social systems.

[1]  Krisztian Balog,et al.  Exploiting Entity Linking in Queries for Entity Retrieval , 2016, ICTIR.

[2]  Krisztian Balog,et al.  Entity-Oriented Search , 2018, The Information Retrieval Series.

[3]  Gjorgji Madjarov,et al.  Web Genre Classification via Hierarchical Multi-label Classification , 2015, IDEAL.

[4]  Vasudeva Varma,et al.  Don't Use a Lot When Little Will Do: Genre Identification Using URLs , 2013, Res. Comput. Sci..

[5]  Azadeh Shakery,et al.  An Axiomatic Study of Query Terms Order in Ad-hoc Retrieval , 2019, ECIR.

[6]  Bonnie L. Webber,et al.  Squibs: Stable Classification of Text Genres , 2011, CL.

[7]  Xiaofang Zhou,et al.  Enhance Web Pages Genre Identification Using Neighboring Pages , 2011, WISE.

[8]  Aytug Onan,et al.  An ensemble scheme based on language function analysis and feature engineering for text genre classification , 2018, J. Inf. Sci..

[9]  Adam Jatowt,et al.  PaperHunter: A System for Exploring Papers and Citation Contexts , 2019, ECIR.

[10]  Efstathios Stamatatos,et al.  Open-Set Classification for Automated Genre Identification , 2013, ECIR.

[11]  Ricardo da Silva Torres,et al.  Nearest neighbors distance ratio open-set classifier , 2016, Machine Learning.

[12]  Mark A. Rosso User-based identification of Web genres , 2008, J. Assoc. Inf. Sci. Technol..

[13]  Grigori Sidorov,et al.  Application of the distributed document representation in the authorship attribution task for small corpora , 2017, Soft Comput..

[14]  Alistair Kennedy,et al.  Automatic Identification of Home Pages on the Web , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.

[15]  Katja Markert,et al.  Semi-supervised Graph-based Genre Classification for Web Pages , 2014, TextGraphs@EMNLP.

[16]  Maarten de Rijke,et al.  OpenSearch: Lessons Learned from an Online Evaluation Campaign , 2018, ACM J. Data Inf. Qual..

[17]  Efstathios Stamatatos,et al.  Open set evaluation of web genre identification , 2018, Lang. Resour. Evaluation.

[18]  Oliver Kopp,et al.  Mr. DLib's Living Lab for Scholarly Recommendations , 2018, ECIR.

[19]  Klaus U. Schulz,et al.  Genre as noise: noise in genre , 2007, International Journal of Document Analysis and Recognition (IJDAR).

[20]  Ruchika Malhotra,et al.  Quantitative evaluation of web metrics for automatic genre classification of web pages , 2017, Int. J. Syst. Assur. Eng. Manag..

[21]  Osman Mutlu,et al.  A Task Set Proposal for Automatic Protest Information Collection Across Multiple Countries , 2019, ECIR.

[22]  Efstathios Stamatatos,et al.  The Impact of Noise in Web Genre Identification , 2015, CLEF.

[23]  Fabio Crestani,et al.  Early Detection of Risks on the Internet: An Exploratory Campaign , 2019, ECIR.

[24]  Jebari Chaker A Combination based on OWA Operators for Multi-label Genre Classification of web pages , 2015, Proces. del Leng. Natural.

[25]  Kevin Crowston,et al.  Problems in the Use-Centered Development of a Taxonomy of Web Genres , 2011, Genres on the Web.

[26]  A. Venugopal Reddy,et al.  Web Page Genre Classification: Impact of n-Gram Lengths , 2014 .

[27]  Udo Kruschwitz,et al.  Rethinking 'Advanced Search': A New Approach to Complex Query Formulation , 2019, ECIR.

[28]  Efstathios Stamatatos,et al.  Learning to recognize webpage genres , 2009, Inf. Process. Manag..

[29]  Pavel Pecina,et al.  An Extended CLEF eHealth Test Collection for Cross-Lingual Information Retrieval in the Medical Domain , 2019, ECIR.

[30]  Gil-Chang Kim,et al.  Multiple sets of features for automatic genre classification of web documents , 2005, Inf. Process. Manag..

[31]  Mark Sanderson,et al.  The SPIRIT collection: an overview of a large web collection , 2004, SIGF.

[32]  Azadeh Shakery,et al.  Deep Neural Networks for Query Expansion using Word Embeddings , 2018, ECIR.