Expertise retrieval has been largely unexplored on data other than the W3C collection. At the same time, many intranets of universities and other knowledge-intensive organisations offer examples of relatively small but clean multilingual expertise data, covering broad ranges of expertise areas. We first present two main expertise retrieval tasks, along with a set of baseline approaches based on generative language modeling, aimed at finding expertise relations between topics and people. For our experimental evaluation, we introduce (and release) a new test set based on a crawl of a university site. Using this test set, we conduct two series of experiments. The first is aimed at determining the effectiveness of baseline expertise retrieval methods applied to the new test set. The second is aimed at assessing refined models that exploit characteristic features of the new test set, such as the organizational structure of the university, and the hierarchical structure of the topics in the test set. Expertise retrieval models are shown to be robust with respect to environments smaller than the W3C collection, and current techniques appear to be generalizable to other settings.

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Expertise-seeking research studies how people search for expertise and choose whom to contact in the context of a specific task. An important outcome are models that identify factors that influence expert finding. Expertise retrieval addresses the same problem, expert finding, but from a system-centered perspective. The main focus has been on developing content-based algorithms similar to document search. These algorithms identify matching experts primarily on the basis of the textual content of documents with which experts are associated. Other factors, such as the ones identified by expertise-seeking models, are rarely taken into account. In this article, we extend content-based expert-finding approaches with contextual factors that have been found to influence human expert finding. We focus on a task of science communicators in a knowledge-intensive environment, the task of finding similar experts, given an example expert. Our approach combines expertise-seeking and retrieval research. First, we conduct a user study to identify contextual factors that may play a role in the studied task and environment. Then, we design expert retrieval models to capture these factors. We combine these with content-based retrieval models and evaluate them in a retrieval experiment. Our main finding is that while content-based features are the most important, human participants also take contextual factors into account, such as media experience and organizational structure. We develop two principled ways of modeling the identified factors and integrate them with contentbased retrieval models. Our experiments show that models combining content-based and contextual factors can significantly outperform existing content-based models. Received March 16, 2009; revised November 19, 2009; accepted November 19, 2009 1 This is an expanded and revised version of Hofmann, Balog, Bogers, & de Rijke, 2008. IntroductionThe increasing amount of information available is making the need to criticially assess information more important. The burden of credibility assessment and quality control is partly shifting onto individual information seekers, but the need for information intermediaries (e.g., experts) has not disappeared and is actually increasing in cases where the credibility of information has to meet high standards (Metzger, 2007). Against this background, expert finding is a particularly relevant task: identifying and selecting individuals with specific expertise, for example, to help with a task or solve a problem. Expert finding has been addressed from different viewpoints, including expertise retrieval, which takes a mostly system-centered approach, and expertise seeking, which studies related human aspects.The goal of expertise retrieval is to support search for experts using information-retrieval technology. Following the experimental paradigm and evaluation framework established in the information-retrieval community, expertise retrieval has been addressed in world-wide evaluation efforts (Craswell, de Vries, & Soboroff, 2006...

Trends in Content-based Recommenders New Metadata Encodings Linked Open Data Usergenerated Content Visual and MulƟmedia Features Meta-Path based Approaches Algorithmic Data Related Deep Learning Heterogeneous InformaƟon Networks

This paper raises the issue of missing data sets for recommender systems in Technology Enhanced Learning that can be used as benchmarks to compare different recommendation approaches. It discusses how suitable data sets could be created according to some initial suggestions, and investigates a number of steps that may be followed in order to develop reference data sets that will be adopted and reused within a scientific community. In addition, policies are discussed that are needed to enhance sharing of data sets by taking into account legal protection rights. Finally, an initial elaboration of a representation and exchange format for sharable TEL data sets is carried out. The paper concludes with future research needs

One of the determining factors of the quality of Web search engines is the size of their index. In addition to its influence on search result quality, the size of the indexed Web can also tell us something about which parts of the WWW are directly accessible to the everyday user. We propose a novel method of estimating the size of a Web search engine’s index by extrapolating from document frequencies of words observed in a large static corpus of Web pages. In addition, we provide a unique longitudinal perspective on the size of Google and Bing’s indices over a nine-year period, from March 2006 until January 2015. We find that index size estimates of these two search engines tend to vary dramatically over time, with Google generally possessing a larger index than Bing. This result raises doubts about the reliability of previous one-off estimates of the size of the indexed Web. We find that much, if not all of this variability can be explained by changes in the indexing and ranking infrastructure of Google and Bing. This casts further doubt on whether Web search engines can be used reliably for cross-sectional webometric studies.

In this paper, we argue that the performance of content-based news recommender systems has been hampered by using relatively old and simple matching algorithms. Using more current probabilistic retrieval algorithms results in significant performance boosts. We test our ideas on a test collection that we have made publicly available. We perform both binary and graded evaluation of our algorithms and argue for the need for more graded evaluation of content-based recommender systems.

Abstract. Real-world information needs are generally complex, yet almost all research focuses on either relatively simple search based on queries or recommendation based on profiles. It is difficult to gain insight into complex information needs from observational studies with existing systems; potentially complex needs are obscured by the systems' limitations. In this paper we study explicit information requests in social media, focusing on the rich area of social book search. We analyse a large set of annotated book requests from the LibraryThing discussion forums. We investigate 1) the comprehensiveness of book requests on the forums, 2) what relevance aspects are expressed in real-world book search requests, and 3) how different types of search topics are related to types of users, human recommendations, and results returned by retrieval and recommender systems. We find that book search requests combine search and recommendation aspects in intricate ways that require more than only traditional search or (hybrid) recommendation approaches.