Users often turn to online forums when searching for known books, movies, or games that they cannot identify through conventional search engines. These “tip-of-the tongue” requests present a unique challenge, appearing highly variable in formulation, context, and specificity. So far, these could mostly only be solved by other humans answering in forums. Generative AI is believed to help solve these specific questions. In this work, we manually annotated 150 requests each for books, games, and movies in the casual leisure domain to study the differences between solved and unsolved requests and identify factors that influence their difficulty. We compare human responses in forum threads with the performance of a Large Language Model (LLM) under similar conditions. Specifically, we investigate how the formulation of requests affects human and LLM success; how item properties impact LLM retrieval; how interaction and feedback within a thread shape human and LLM performance; and whether increasing the information provided to an LLM improves its chances of solving the request. Our findings offer new insights into what makes these known-item search problems easier or harder to solve. This study contributes to a better understanding of complex search behavior and the role of LLMs in helping with difficult casual-leisure information needs.

Despite increasing interest in data storytelling, it remains unclear how the choice of communication medium shapes its effectiveness, particularly for audiences with varying levels of data literacy. This paper reports on a controlled, longitudinal experiment comparing verbal to written storytelling alongside a baseline data visualization condition. Each condition employed simple graphs and an author-driven narrative to examine their effects on recall and attitude change. Results showed mixed results of data storytelling: while storytelling did not improve recall, verbal storytelling and no storytelling facilitated long-term attitude change, whereas written storytelling did not. Higher data literacy supported long-term recall but was associated with smaller immediate attitude shifts, an effect that diminished over time. These findings challenge assumptions about the universal advantages of narrative-based communication, demonstrating that medium, topic familiarity, and audience characteristics jointly determine outcomes. The study contributes empirical evidence to the field and calls for further research into how narrative structures and visualization complexity affect the effectiveness of data storytelling.

The rapidly increasing popularity of LLM-powered chatbots has led to them being used for a increasing number of different tasks by the general public.One of these tasks is searching for information instead of using a search engine.Previous work has shown that complex search tasks can be problematic for traditional search engines to solve, but little is known about the capability of LLMs on the same task.We compared four LLMs on their capability to answer a specific type of complex search task: known-item requests from casual leisure domains.We constructed a test collection by gathering known-item requests for books, games and movies from online forums along with verified answers by the original requester.We prompted four LLMs multiple times with the same prompt and analyzed the results with respect to accuracy and the degree to which answers were fabricated by the LLM.Our results show that LLMs are not particularly effective in fulfilling these complex casual leisure needs, but there are are big differences between LLMs and across domains.

Recruitment is a professional search domain that has been largely overlooked in IR research, even though better support of recruiters could have a big impact on job seekers, companies and society as a whole.In this paper, we analyze the search formulation and source selection behavior of the recruiters at one of Scandinavia's largest job portals and recruitment agencies using search logs for close to 18,000 recruitment search tasks.We provide an analysis of the search sessions of recruiters in terms search tactics, query operators, query length, term re-use and filter usage, and break down their behavior both by task type and task complexity.We also relate their short-term tactics to different learning stages in the search process and investigate their influence on search success.We find that identifying and assessing relevant candidates for a job posting is a complex task: recruiters usually submit multiple queries during sessions that can last for hours and that increase in complexity.Recruiters all spend more time per query as their session progresses.We also observed query reformulation strategies that indicate distinct patterns of knowledge gaining during sessions.Relating these tactics to positive responses from candidates we aim at predicting successful strategies.

In settings such as e-recruitment and online dating, recommendation involves distributing limited opportunities, calling for novel approaches to quantify and enforce fairness.We introduce inferiority, a novel (un)fairness measure quantifying a user's competitive disadvantage for their recommended items.Inferiority complements envy, a fairness notion measuring preference for others' recommendations.We combine inferiority and envy with utility, an accuracy-related measure of aggregated relevancy scores.Since these measures are non-differentiable, we reformulate them using a probabilistic interpretation of recommender systems, yielding differentiable versions.We combine these loss functions in a multi-objective optimization problem called FEIR (Fairness through Envy and Inferiority Reduction), applied as post-processing for standard recommender systems.Experiments on synthetic and real-world data demonstrate that our approach improves trade-offs between inferiority, envy, and utility compared to naive recommendations and the baseline methods.

Already before the enactment of the EU AI Act, candidate or job recommendation for algorithmic hiring—semi-automatically matching CVs to job postings—was used as an example of a high-risk application where unfair treatment could result in serious harms to job seekers. Recommending candidates to jobs or jobs to candidates, however, is also a fitting example of a multi-stakeholder recommendation problem. In such multi-stakeholder systems, the end user is not the only party whose interests should be considered when generating recommendations. In addition to job seekers, other stakeholders—such as recruiters, organizations behind the job postings, and the recruitment agency itself—are also stakeholders in this and deserve to have their perspectives included in the design of relevant fairness metrics. Nevertheless, past analyses of fairness in algorithmic hiring have been restricted to single-side fairness, ignoring the perspectives of the other stakeholders. In this paper, we address this gap and present a multi-stakeholder approach to fairness in a candidate recommender system that recommends relevant candidate CVs to human recruiters in a human-in-the-loop algorithmic hiring scenario. We conducted semi-structured interviews with 40 different stakeholders (job seekers, companies, recruiters, and other job portal employees). We used these interviews to explore their lived experiences of unfairness in hiring, co-design definitions of fairness as well as metrics that might capture these experiences. Finally, we attempt to reconcile and map these different (and sometimes conflicting) perspectives and definitions to existing (categories of) fairness metrics that are relevant for our candidate recommendation scenario.

Across all scientific fields, there is an increased focus on the impact of scientific research: what academic and societal benefits does it provide? This question has spurred the development of a variety of different approaches to impact assessment, each appropriate in different circumstances. In this paper, we study the academic impact of the CHIIR community through a comprehensive analysis of the work published in the 2016-2023 CHIIR conference series. We collect citation counts, citing documents, and altmetrics scores for all CHIIR publications to determine their academic impact across a variety of different attributes of the CHIIR publications. In addition, we analyze a subset of citation contexts in the papers that have cited CHIIR publications to analyze how they are being used and what that means for their potential impact. Finally, we attempt to predict which properties of CHIIR publications are most predictive of future impact.

In this paper, we present the results of an initial study of the research, sharing, and re-use practices at the CHIIR conference through a systematic analysis of all CHIIR papers published from 2016 to 2022. We find that CHIIR is a conference predominantly focused on empirical, multi-methods research that over the years has undergone a focusing in terms of the type of research methods that are being used. A modest number of papers re-use existing data and design resources, but infrastructure component re-use is much more rare. Only a fraction of CHIIR papers actually share their own resources, which suggests that there is much to gain in terms of reproducibility of research presented at CHIIR and could potentially be used to support changes in reviewing practices.

We studied the collaboration patterns of CHIIR authors, and found that most papers are collaborative. A core of 33% of the CHIIR researchers are directly connected and frequently co-author, and several disconnected clusters also make frequent CHIIR contributions. We also studied citation impact of the CHIIR papers and show that in relation to research design type, theoretical and empirical papers tend to receive more citations than resource papers. With regards to sharing and re-use, papers that share at least one resource tend to have significantly higher citation impact—in particular when sharing data resources and design resources. Re-using resources does not significantly increase citation impact in itself.

While the rise of online job portals and corporate websites have allowed for easier collection of digital candidate CVs, much of the candidate identification and assessment process—also known as talent search—still requires manual work from recruiters. Recruitment is a professional search domain that has been largely overlooked in IR research, even though better support of recruiters in finding more high-quality candidates could have a big impact on job seekers, companies and society as a whole. Such recruiter support can only be built on top of a more thorough understanding of the information seeking behavior of recruiters when trying to identify the most relevant candidates for open job postings.

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Recent years have seen a rapid increase in the application of computational approaches to different HR tasks, such as algorithmic hiring, skill extraction, and monitoring of employee satisfaction. Much of the recent work on estimating the fit between a person and a job has used representation learning to represent both resumes and job vacancies computationally and determine the degree to which they match. A common approach to this task is Sentence-BERT, which uses a Siamese network to encode resumes and job descriptions into fixed-length vectors and estimates how well they match based on the similarity between those vectors. In our paper, we adapt BERT’s next-sentence prediction task—predicting whether one sentence is likely to follow another in a given context—to the task of matching resumes with job descriptions. Using historical data on past (mis)matches between job-resume pairs, we fine-tune BERT for this downstream task. Through a combination of offline and online experiments on data from a large Scandinavian job portal, we show that this approach performs significantly better than Sentence-BERT and other state-of-the-art approaches for determining person-job fit.

Work in Progress Share on Third Workshop on Building towards Information Interaction and Retrieval Resources Re-use (BIIRRR 2022) Authors: Toine Bogers Aalborg University, Denmark Aalborg University, DenmarkView Profile , Maria Gäde Humboldt-Universität zu Berlin, Germany Humboldt-Universität zu Berlin, GermanyView Profile , Mark Michael Hall The Open University, United Kingdom The Open University, United KingdomView Profile , Marijn Koolen Huygens Institute for the History of the Netherlands, Royal Netherlands Academy of Arts and Sciences, Netherlands Huygens Institute for the History of the Netherlands, Royal Netherlands Academy of Arts and Sciences, NetherlandsView Profile , Vivien Petras Humboldt-Universität zu Berlin, Germany Humboldt-Universität zu Berlin, GermanyView Profile , Paul Thomas Microsoft, Australia Microsoft, AustraliaView Profile Authors Info & Claims CHIIR '22: ACM SIGIR Conference on Human Information Interaction and RetrievalMarch 2022 Pages 374–376https://doi.org/10.1145/3498366.3505838Published:14 March 2022Publication History 0citation24DownloadsMetricsTotal Citations0Total Downloads24Last 12 Months24Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Work in Progress Share on Information Quality in Information Interaction and Retrieval: Workshop proposal for CHIIR 2022 Authors: Frans van der Sluis Department of Communication, University of Copehagen, Denmark Department of Communication, University of Copehagen, DenmarkView Profile , Catherine Smith Kent State University, United States Kent State University, United StatesView Profile , Toine Bogers Aalborg University Copenhagen, Denmark Aalborg University Copenhagen, DenmarkView Profile , Florian Meier Aalborg University Copenhagen, Denmark Aalborg University Copenhagen, DenmarkView Profile Authors Info & Claims CHIIR '22: ACM SIGIR Conference on Human Information Interaction and RetrievalMarch 2022 Pages 371–373https://doi.org/10.1145/3498366.3505798Published:14 March 2022Publication History 0citation56DownloadsMetricsTotal Citations0Total Downloads56Last 12 Months56Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

With ever-increasing amounts of complex data, we need compelling ways to distill this information into meaningful, memorable and engaging insights. Data storytelling is an emerging visualization paradigm that aims to “tell a story” with data in order to elicit deeper reflections in an effective manner. However, the effects of adding a narrative to a visualization on the memorability of the information remain speculative. Based on a review of related work, we synthesize a framework of data storytelling principles with concrete actions for every principle. We use this framework to design an online, controlled experiment to test compare traditional data visualizations with data storytelling visualizations in terms of their effects on short-term and long-term recall of information displayed in the visualizations. In general, despite long-held assumptions in the visualization community, we find no significant differences in recall between traditional visualizations and data storytelling visualization. However, we find indications that the cognitive load induced by different chart types and self-assessed prior knowledge on the chart topics could possibly have a moderating effect on information recall.

introduction Share on Second Workshop on Recommender Systems for Human Resources (RecSys in HR 2022) Authors: Toine Bogers Aalborg University Copenhagen, Denmark Aalborg University Copenhagen, DenmarkView Profile , David Graus Randstad Groep Nederland, Netherlands Randstad Groep Nederland, NetherlandsView Profile , Mesut Kaya Aalborg University Copenhagen, Denmark Aalborg University Copenhagen, DenmarkView Profile , Francisco Gutiérrez Computer Science, KU Leuven, Belgium Computer Science, KU Leuven, BelgiumView Profile , Sepideh Mesbah Randstad Groep Nederland, Netherlands Randstad Groep Nederland, NetherlandsView Profile , Chris Johnson Indeed, United States Indeed, United StatesView Profile Authors Info & Claims RecSys '22: Proceedings of the 16th ACM Conference on Recommender SystemsSeptember 2022Pages 671–674https://doi.org/10.1145/3523227.3547414Published:13 September 2022Publication History 0citation112DownloadsMetricsTotal Citations0Total Downloads112Last 12 Months42Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

The FinRec workshop series offers a central forum for the study and discussion of the domain-specific aspects, challenges, and opportunities of RecSys and other related technologies in the financial services domain. Six years after the second edition of the workshop, the recent advances in the area of personalization and recommendation in financial services fostered the need for a new workshop aiming at bringing together researchers and practitioners working in financial services-related areas. Accordingly, the third edition of the event aims to: (1) understand and discuss open research challenges, (2) provide an overview of existing technologies using recommender systems in the financial services domain, and (3) provide an interactive platform for information exchange between industry and academia.

This perspective paper on resource re-use intends to draw the attention of the interactive information retrieval (IIR) community to the challenges of research documentation and archiving for future use. Resources are understood as encompassing research designs, research data and research infrastructures. It proposes eight principles for improving the re-use of resources in the IIR community and presents concrete steps on how to achieve them. A five-level system for data archiving and documentation envisions increasingly open and stable documentation and access infrastructures.

During the past decade, recommender systems have rapidly become an indispensable element of websites, apps, and other platforms that seek to provide personalized interactions to their users. As recommendation technologies are applied to an ever-growing array of non-standard problems and scenarios, researchers and practitioners are also increasingly faced with challenges of dealing with greater variety and complexity in the inputs to those recommender systems. For example, there has been more reliance on fine-grained user signals as inputs rather than simple ratings or likes. Applications require more complex domain-specific constraints on inputs to the recommender systems. Likewise, the outputs of recommender systems are moving towards more complex composite items, such as package or sequence recommendations. This increasing complexity requires smarter recommender algorithms that can deal with this diversity in inputs and outputs. For the past four years, the ComplexRec workshop series has offered an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution.

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Increasing re-use in Interactive Information Retrieval (IIR) has been an ongoing aim in IIR for a significant amount of time, however progress has been limited and patchy. While re-use of some study aspects can be difficult due to the varied nature of IIR studies, the use of pre- and post-task self-reported measures is widespread and relatively standardised. Nevertheless, re-use of elements in this area is also limited, in part because systems used to implement them are not able to exchange question, instruments, or complete study setups. To address this, this paper presents a standardised, but extendable, format for IIR survey instrument exchange.

Distractions while driving are a major cause of traffic accidents and chief among these is the use of mobile phones. Driver distractions typically fall into four categories-visual, cognitive, bio-mechanical, and auditory-and different technological solutions have been proposed to address these. Intelligent Personal Assistants (IPAs), such as Siri, is a recent example of such a technological solution that offers the potential for hands-free phone interaction through a voice-controlled interface. IPAs could potentially reduce visual and bio-mechanical distractions if they are usable enough to not increase a driver's cognitive load. We present the results of a controlled experiment with the aim of understanding how the use of Siri while driving compares to manual interaction in terms of usability and distractions. We also tested these two interaction types in the lab in order to understand how the main driving task influences Siri's (perceived) usability. Our study shows that Siri is not ready for every-day use in the car: interacting with Siri while driving is likely to be unsafe for most participants, especially less experienced drivers. Participants were distracted by Siri due to its over-reliance on visual feedback as well as frequent time-outs by Siri when waiting for a response from a driver occupied with the road environment. Speech recognition quality in a noisy car as well as problematic multi-lingual speech recognition in general are other issues that resulted in low usability and more cognitive distractions. While interacting with Siri may be hands-free, it does not provide an eyes-free and distraction-free experience yet.

During the past decade, recommender systems have rapidly become an indispensable element of websites, apps, and other platforms that are looking to provide personalized interaction to their users. As recommendation technologies are applied to an ever-growing array of non-standard problems and scenarios, researchers and practitioners are also increasingly faced with challenges of dealing with greater variety and complexity in the inputs to those recommender systems. For example, there has been more reliance on fine-grained user signals as inputs rather than simple ratings or likes. Many applications also require more complex domain-specific constraints on inputs to the recommender systems. The outputs of recommender systems are also moving towards more complex composite items, such as package or sequence recommendations. This increasing complexity requires smarter recommender algorithms that can deal with this diversity in inputs and outputs. The ComplexRec workshop series offers an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution.

Share on Workshop on Barriers to Interactive IR Resources Re-use (BIIRRR 2019) Authors: Toine Bogers Aalborg University Copenhagen, Copenhagen, Denmark Aalborg University Copenhagen, Copenhagen, DenmarkView Profile , Samuel Dodson University of British Columbia, Vancouver, Canada University of British Columbia, Vancouver, CanadaView Profile , Luanne Freund University of British Columbia, Vancouver, Canada University of British Columbia, Vancouver, CanadaView Profile , Maria Gäde Humboldt-Universität zu Berlin, Berlin, Germany Humboldt-Universität zu Berlin, Berlin, GermanyView Profile , Mark Hall Martin-Luther-Universität Halle-Wittenberg, Halle, Germany Martin-Luther-Universität Halle-Wittenberg, Halle, GermanyView Profile , Marijn Koolen Royal Netherlands Academy of Arts and Sciences, Amsterdam, Netherlands Royal Netherlands Academy of Arts and Sciences, Amsterdam, NetherlandsView Profile , Vivien Petras Humboldt-Universität zu Berlin, Berlin, Germany Humboldt-Universität zu Berlin, Berlin, GermanyView Profile , Nils Pharo Oslo Metropolitan University, Oslo, Norway Oslo Metropolitan University, Oslo, NorwayView Profile , Mette Skov Aalborg University, Aalborg, Denmark Aalborg University, Aalborg, DenmarkView Profile Authors Info & Claims CHIIR '19: Proceedings of the 2019 Conference on Human Information Interaction and RetrievalMarch 2019 Pages 389–392https://doi.org/10.1145/3295750.3298965Published:08 March 2019Publication History 1citation75DownloadsMetricsTotal Citations1Total Downloads75Last 12 Months8Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Over the past decade, recommendation algorithms for ratings prediction and item ranking have steadily matured. However, these state-of-the-art algorithms are typically applied in relatively straightforward and static scenarios: given information about a user's past item preferences in isolation, can we predict whether they will like a new item or rank all unseen items based on predicted interest? In reality, recommendation is often a more complex problem: the evaluation of a list of recommended items never takes place in a vacuum, and it is often a single step in the user's more complex background task or need. The goal of the ComplexRec 2019 workshop is to offer an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution.

The goal of this workshop is to serve as a starting point for a community-driven effort to design and implement a platform for the collection, organization, maintenance, and sharing of resources for IIR experimentation. As in all scientific endeavors, progress in IIR research is contingent on the ability to build on previous ideas, approaches, and resources. However, we believe there to be a number of barriers to reproducibility and re-use of resources in IIR research: the fragmentary nature of how the community»s resources are organized, the lack of awareness of their existence, documentation and organization of the resources, the nature of the typical research publication cycle, and the effort required to make such resources available. We believe that an online platform dedicated to the collection and organization of IIR resources could be a promising way of overcoming these barriers. The workshop therefore aims to serve both as a brainstorming opportunity about the shape this iRepository should take, as well as a way of building support in the community for its implementation.

While ownership and usage of handheld devices such as smartphones and tablets continues to grow at a rapid pace, we do not have complete picture of how people manage personal information on these devices. The few existing studies have typically used interview or survey methods to focus on personal information management (PIM) practices on smartphones. We present the results of an exploratory contextual inquiry study of PIM practices aimed at providing a structured, naturalistic overview of PIM on both smartphones and tablets. We find that people use multiple complementary strategies to acquire different types of information on their devices, and that people rely strongly on automatic chronological ordering instead of organization by subject, although this pays off most for smaller information collections. Deletion of information is strongly influenced by usefulness and personal attachment. Finally, we find that people strongly prefer browsing over search when retrieving information from their devices.

Over the past decade, recommendation algorithms for ratings prediction and item ranking have steadily matured. However, these state-of-the-art algorithms are typically applied in relatively straightforward scenarios. In reality, recommendation is often a more complex problem: it is usually just a single step in the user's more complex background need. These background needs can often place a variety of constraints on which recommendations are interesting to the user and when they are appropriate. However, relatively little research has been done on these complex recommendation scenarios. The ComplexRec 2018 workshop addresses this by providing an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all solution.

There is broad consensus in the field of IR that search is complex in many use cases and applications, both on the Web and in domain specific collections, and both professionally and in our daily life. Yet our understanding of complex search tasks, in comparison to simple look up tasks, is fragmented at best. The workshop addresses many open research questions: What are the obvious use cases and applications of complex search? What are essential features of work tasks and search tasks to take into account? And how do these evolve over time--With a multitude of information, varying from introductory to specialized, and from authoritative to speculative or opinionated, when to show what sources of information? How does the information seeking process evolve and what are relevant differences between different stages? With complex task and search process management, blending searching, browsing, and recommendations, and supporting exploratory search to sensemaking and analytics, UI and UX design pose an overconstrained challenge. How do we evaluate and compare approaches? Which measures should be taken into account? Supporting complex search tasks requires new collaborations across the fields of CHI and IR, and the proposed workshop will bring together a diverse group of researchers to work together on one of the greatest challenges of our field.

Research into recommendation algorithms has made great strides in recent years. However, these algorithms are typically applied in relatively straightforward scenarios: given information about a user's past preferences, what will they like in the future? Recommendation is often more complex: evaluating recommended items never takes place in a vacuum, and it is often a single step in the user's more complex background task. In this paper, we define a specific type of recommendation scenario called narrative-driven recommendation, where the recommendation process is driven by both a log of the user's past transactions as well as a narrative description of their current interest(s). Through an analysis of a set of real-world recommendation narratives from the LibraryThing forums, we demonstrate the uniqueness and richness of this scenario and highlight common patterns and properties of such narratives.

Recommendation algorithms for ratings prediction and item ranking have steadily matured during the past decade. However, these state-of-the-art algorithms are typically applied in relatively straightforward scenarios. In reality, recommendation is often a more complex problem: it is usually just a single step in the user's more complex background need. These background needs can often place a variety of constraints on which recommendations are interesting to the user and when they are appropriate. However, relatively little research has been done on these complex recommendation scenarios. The ComplexRec 2017 workshop addressed this by providing an interactive venue for discussing approaches to recommendation in complex scenarios that have no simple one-size-fits-all-solution.

While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how these data sources should be combined to provided the best recommendation performance. The CBRecSys 2016 workshop provides a dedicated venue for papers dedicated to all aspects of content-based recommendation.

While content-based recommendation has been applied successfully in many different domains, it has not seen the same level of attention as collaborative filtering techniques have. However, there are many recommendation domains and applications where content and metadata play a key role, either in addition to or instead of ratings and implicit usage data. For some domains, such as movies, the relationship between content and usage data has seen thorough investigation already, but for many other domains, such as books, news, scientific articles, and Web pages we still do not know if and how these data sources should be combined to provided the best recommendation performance. The CBRecSys 2014 workshop aims to address this by providing a dedicated venue for papers dedicated to all aspects of content-based recommendation.

Expertise retrieval has attracted significant interest in the field of information retrieval. Expert finding has been studied extensively, with less attention going to the complementary task of expert profiling, that is, automatically identifying topics about which a person is knowledgeable. We describe a test collection for expert profiling in which expert users have self‐selected their knowledge areas. Motivated by the sparseness of this set of knowledge areas, we report on an assessment experiment in which academic experts judge a profile that has been automatically generated by state‐of‐the‐art expert‐profiling algorithms; optionally, experts can indicate a level of expertise for relevant areas. Experts may also give feedback on the quality of the system‐generated knowledge areas. We report on a content analysis of these comments and gain insights into what aspects of profiles matter to experts. We provide an error analysis of the system‐generated profiles, identifying factors that help explain why certain experts may be harder to profile than others. We also analyze the impact on evaluating expert‐profiling systems of using self‐selected versus judged system‐generated knowledge areas as ground truth; they rank systems somewhat differently but detect about the same amount of pairwise significant differences despite the fact that the judged system‐generated assessments are more sparse.

Abstract 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 offinding 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 content‐based retrieval models. Our experiments show that models combining content‐based and contextual factors can significantly outperform existing content‐based models.

We describe the use of the social reference management website CiteULike for recommending scientific articles to users, based on their reference library. We test three different collaborative filtering algorithms, and find that user-based filtering performs best. A temporal analysis of the data indexed by CiteULike shows that it takes about two years for the cold-start problem to disappear and recommendation performance to improve.

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.

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.