"Data fusion and search result diversification are two critical research topics in information retrieval. Data fusion approaches combine search result lists in order to produce a new and hopefully better ranking. We propose two data fusion models for microblog search that exploit temporal information and infer rank scores of missing documents in the lists to be fused. We also propose a fusion method based on manifolds. The method constructs manifolds, let low ranked documents be rewarded to be relevant by high ranked documents in the same manifolds, and utilize the top-k documents as anchors to enhance the efficiency of data fusion. Search result diversification is widely being studied as a way of tackling query ambiguity. Instead of trying to identify the "correct" interpretation behind a query, the idea is to make the search results diversified so that users with different backgrounds will find at least one of these results to be relevant. We examine the hypothesis that data fusion can improve performance in terms of diversity metrics, and proposes a new data fusion method, called diversified data fusion for search result diversification. We also study the problem of personalized diversification via supervised learning, with the goal of enhancing both diversification and personalization performance. The results in this thesis show how both our proposed data fusion and search result diversification methods improve retrieval performance and how they relate to each other. The insights in this thesis may be used to improve retrieval performance for a range of tasks in information retrieval."--Samenvatting auteur.

The technique of data fusion has been used extensively in information retrieval due to the complexity and diversity of tasks involved such as web and social networks, legal, enterprise, and many others. This book presents both a theoretical and empirical approach to data fusion. Several typical data fusion algorithms are discussed, analyzed and evaluated. A reader will find answers to the following questions, among others: What are the key factors that affect the performance of data fusion algorithms significantly? What conditions are favorable to data fusion algorithms? CombSum and CombMNZ, which one is better? and why? What is the rationale of using the linear combination method? How can the best fusion option be found under any given circumstances?

This book constitutes the refereed proceedings of the 27th European Conference on Information Retrieval Research, ECIR 2005, held in Santiago de Compostela, Spain in March 2005. The 34 revised full papers presented together with 2 invited keynote papers and 17 selected poster papers were carefully reviewed and selected from 124 papers submitted. The papers are organized in topical sections on peer-to-peer, information retrieval models, text summarization, information retrieval methods, text classification and fusion, user studies and evaluation, multimedia retrieval, and Web information retrieval.

This text introduces the basic concepts and techniques in VIR. In doing so, it develops a foundation for further research and study. Divided into two parts, the first part describes the fundamental principles. A chapter is devoted to each of the main features of VIR, such as colour, texture and shape-based search. There is coverage of search techniques for time-based image sequences or videos, and an overview of how to combine all the basic features described and integrate them into the search process. The second part looks at advanced topics such as multimedia query. This book is essential reading for researchers in VIR, and final-year undergraduate and postgraduate students on courses such as Multimedia Information Retrieval, Multimedia Databases, and others.

This two volume set LNCS 10041 and LNCS 10042 constitutes the proceedings of the 17th International Conference on Web Information Systems Engineering, WISE 2016, held in Shanghai, China, in November 2016. The 39 full papers and 31 short papers presented in these proceedings were carefully reviewed and selected from 233 submissions. The papers cover a wide range of topics such as Social Network Data Analysis; Recommender Systems; Topic Modeling; Data Diversity; Data Similarity; Context-Aware Recommendation; Prediction; Big Data Processing; Cloud Computing; Event Detection; Data Mining; Sentiment Analysis; Ranking in Social Networks; Microblog Data Analysis; Query Processing; Spatial and Temporal Data; Graph Theory; Non-Traditional Environments; and Special Session on Data Quality and Trust in Big Data.

This two-volume set, LNCS 9658 and 9659, constitutes the thoroughly refereed proceedings of the 17th International Conference on Web-Age Information Management, WAIM 2016, held in Nanchang, China, in June 2016. The 80 full research papers presented together with 8 demonstrations were carefully reviewed and selected from 266 submissions. The focus of the conference is on following topics: data mining, spatial and temporal databases, recommender systems, graph data management, information retrieval, privacy and trust, query processing and optimization, social media, big data analytics, and distributed and cloud computing.

This two-volume set LNCS 12035 and 12036 constitutes the refereed proceedings of the 42nd European Conference on IR Research, ECIR 2020, held in Lisbon, Portugal, in April 2020.* The 55 full papers presented together with 8 reproducibility papers, 46 short papers, 10 demonstration papers, 12 invited CLEF papers, 7 doctoral consortium papers, 4 workshop papers, and 3 tutorials were carefully reviewed and selected from 457 submissions. They were organized in topical sections named: Part I: deep learning I; entities; evaluation; recommendation; information extraction; deep learning II; retrieval; multimedia; deep learning III; queries; IR – general; question answering, prediction, and bias; and deep learning IV. Part II: reproducibility papers; short papers; demonstration papers; CLEF organizers lab track; doctoral consortium papers; workshops; and tutorials. *Due to the COVID-19 pandemic, this conference was held virtually.