Altingovde I.S.

Can F.

Castells P.

Santos, R.L.T.

Bilkent University Institutional Repository

Diversity and Novelty in Web Search,Recommender Systems and Data StreamsRodrygo L. T. SantosUniv. Federal de Minas GeraisBelo Horizonte, MG, Brazilrodrygo@dcc.ufmg.brPablo CastellsUniv. Autónoma de MadridMadrid, Spainpablo.castells@uam.esIsmail Sengor AltingovdeMiddle East Technical UniversityAnkara, Turkeyaltingovde@ceng.metu.edu.trFazli CanBilkent UniversityAnkara, Turkeycanf@cs.bilkent.edu.trABSTRACTThis tutorial aims to provide a unifying account of currentresearch on diversity and novelty in the domains of websearch, recommender systems, and data stream processing.Categories and Subject DescriptorsH.3.3 [Information Storage and Retrieval]: InformationSearch and Retrieval—search processKeywordsRelevance, Diversity, Novelty, Ambiguity, Redundancy1. OVERVIEWInformation retrieval (IR) is traditionally approached as apursuit of relevant information, under the assumption thatthe users’ information needs are unambiguously conveyedby their information requests. Arguably, such an assump-tion does not hold for the multitude of users’ needs in mod-ern IR systems, such as web search engines, recommendersystems, and stream processing engines. In order to iden-tify relevant information under the uncertainty posed by theusers’ requests, an effective approach is to diversify the re-trieved results. By doing so, an IR system can minimizethe chance of wrongly guessing the users’ needs, which maycause the users to abandon their retrieval task.Through a stream of active research and experiences, di-versity and novelty can be said to have by now consolidatedinto a significant body of techniques, methodologies, the-ories, and knowledge in the field of information retrieval.This tutorial aims to provide a unifying account of currentresearch on diversity and novelty in different IR domains. Inparticular, we will cover the motivations, as well as the mostestablished approaches for producing and evaluating diversePermission to make digital or hard copies of part or all of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage, and that copies bear this notice and the full ci-tation on the first page. Copyrights for third-party components of this work must behonored. For all other uses, contact the owner/author(s). Copyright is held by theauthor/owner(s).WSDM’14, February 24–28, 2014, New York, New York, USA.ACM 978-1-4503-2351-2/14/02.http://dx.doi.org/10.1145/2556195.2556199.results in the context of search engines, recommender sys-tems, and data streams. By contrasting the state-of the-artin these multiple domains, this tutorial aims to derive acommon understanding of the diversification problem andthe existing solutions, their commonalities and differences,as a means to foster new research directions.In particular, the tutorial attendees will:• understand the importance and complexities of achiev-ing diversity/novelty for various IR domains;• learn the state-of-the-art approaches for promoting di-versity and novelty in web search, recommender sys-tems, and data streams;• learn the fundamental evaluation metrics and have anoverview of past and current evaluation campaigns;• get an overview of other related application areas thatinclude query suggestions, query ambiguity detection,and aggregated search;• obtain a unifying view of the topic by exploring thesimilarities and differences between the methods em-ployed in different domains.2. TUTORIAL OUTLINE1. Practical and Theoretical Background2. Diversity in Web Search• Implicit and Explicit Diversification• Advanced Topics in Web Search Diversification• Diversity Evaluation3. Diversity in Recommender Systems• Motivation and Notions• Novelty and Diversity Enhancement• Novelty and Diversity Evaluation4. Diversity in Data Streams• Document-level Novelty• Novelty and Diversification of Document Streams• Evaluation5. Other Application Areas679

Diversity and novelty in web search, recommender systems and data streams

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Diversity and novelty in web search, recommender systems and data streams

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