Translating internal information needs into textual queries poses challenges for information retrieval, particularly for users with physical impairments or ill-defined search intentions. Brain Passage Retrieval (BPR) approaches map EEG signals directly to dense passage representations, bypassing text translation. Whilst recent work reports superior performance with auditory versus visual EEG, these findings emerge from unbalanced experimental conditions where sample sizes, vocabularies, and corpus characteristics differ substantially between modalities, making it unclear whether observed differences reflect genuine neural processing advantages or dataset artefacts. We investigate EEG-based retrieval performance comparing auditory and visual modalities under controlled dataset conditions. Using the Brennan (auditory, 49 subjects) and Nieuwland (visual, 51 subjects) datasets, balanced for vocabulary overlap and sample distributions, we train BPR models with transformer EEG encoders and BERT text encoders via contrastive learning. Under controlled conditions, we observe complementary performance characteristics: audio EEG demonstrates stronger recall (Hit@5: +74%, Hit@10: +140%) whilst visual EEG achieves better precision (Hit@1: +100%). These findings suggest that modality-specific strengths could inform the design of brain-computer interfaces for information retrieval.

Web searchers continuously form expectations about document content based on snippets and titles, yet when these expectations are violated, their attention and satisfaction are disrupted. Detecting such expectancy violations in real-time can enable adaptive, user-aware Web systems that respond to cognitive mismatches. This paper investigates whether eye-tracking features can predict expectancy violations during Web search and identifies which temporal aspects of attention carry predictive information. Using data from 34 participants performing controlled search tasks, we extracted four gaze metrics, i.e. time to first fixation, total fixation duration, number of fixations, and mean fixation duration, and trained machine learning models using a leave-one-participant-out cross-validation approach. Sustained attention features, particularly total fixation duration and number of fixations, predicted expectancy violations with 62.6% accuracy (p = .008), while initial attention metrics performed at chance. The results reveal that expectancy violations manifest through extended visual engagement rather than immediate orienting responses. The findings of this work contribute to the theoretical understanding of user–system interaction on the Web and provide a foundation for adaptive retrieval interfaces capable of detecting cognitive surprise and delivering timely support.

Query Performance Prediction (QPP) enables information retrieval systems to estimate search effectiveness without requiring explicit relevance judgements. While traditional QPP research has focused exclusively on textual features, we investigate on enhancing QPP through the multimodal integration of electroencephalography (EEG) and eye-tracking signals captured during listening and reading. We utilise a specialised dataset where queries are represented as text alongside corresponding neurophysiological recordings, with graded relevance judgements for multiple documents. Our methodology employs an ensemble architecture with dedicated models for each modality, followed by a meta-learner that produces final predictions. Experimental evaluation across reading and listening tasks demonstrates that generalised models trained across subjects achieve consistent improvements over text-only baselines, with the trimodal configuration (EEG+eye-tracking+text) reaching Pearson correlation of 0.458 for the ZuCo reading dataset and bimodal EEG+text models achieving 0.417 for the Narrative listening dataset, representing 50–80% higher performance than personalised single-subject models. Query-level analysis reveals that neurophysiological signals substantially improve predictions for fragmentary or semantically ambiguous query cases, while the majority show neutral effects. These findings establish the feasibility of neurophysiological QPP under specific conditions and provide design principles for integrating brain–computer interfaces in information retrieval systems. Our code can be found here .

The International Workshop on NeuroPhysiological Approaches for Interactive Information Retrieval (NeuroPhysIIR'25) aims to bringing together researchers from information science, humancomputer interaction, cognitive neuroscience, and related fields, to foster cross-disciplinary collaboration and accelerate progress in neurophysiologically-informed IIR research.As the third edition following successful workshops at SIGIR'15 [5] and CHIIR'17 [6], we anticipate that the interactive nature of this workshop will not only raise awareness but also lower the entry barriers for engaging with this exciting research area within the wider IIR community.Workshop website: https://neurophysiir.github.io/chiir2025/.

Information Retrieval (IR) systems primarily rely on users' ability to translate their internal information needs into (text) queries.However, this translation process is often uncertain and cognitively demanding, leading to queries that incompletely or inaccurately represent users' true needs.This challenge is particularly acute for users with ill-defined information needs or physical impairments that limit traditional text input, where the gap between cognitive intent and query expression becomes even more pronounced.Recent neuroscientific studies have explored Brain-Machine Interfaces (BMIs) as a potential solution, aiming to bridge the gap between users' cognitive semantics and their search intentions.However, current approaches attempting to decode explicit text queries from brain signals have shown limited effectiveness in learning robust brain-to-text representations, often failing to capture the nuanced semantic information present in brain patterns.To address these limitations, we propose BPR (Brain Passage Retrieval), a novel framework that eliminates the need for intermediate query translation by enabling direct retrieval of relevant passages from users' brain signals.Our approach leverages dense retrieval architectures to map EEG signals and text passages into a shared semantic space.Through comprehensive experiments on the ZuCo dataset, we demonstrate that BPR achieves up to 8.81% improvement in precision@5 over existing EEG-to-text baselines, while maintaining effectiveness across 30 participants.Our ablation studies reveal the critical role of hard negative sampling and specialised brain encoders in achieving robust cross-modal alignment.These results establish the viability of direct brain-to-passage retrieval and provide a foundation for developing more natural interfaces between users' cognitive states and IR systems.

The fundamental goal of Information Retrieval (IR) systems lies in their capacity to effectively satisfy human information needs -a challenge that encompasses not just the technical delivery of information, but the nuanced understanding of human cognition during information seeking.Contemporary IR platforms rely primarily on observable interaction signals, creating a fundamental gap between system capabilities and users' cognitive processes.Brain-Machine Interface (BMI) technologies now offer unprecedented potential to bridge this gap through direct measurement of previously inaccessible aspects of information-seeking behaviour.This perspective paper offers a broad examination of the IR landscape, providing a comprehensive analysis of how BMI technology could transform IR systems, drawing from advances at the intersection of both neuroscience and IR research.We present our analysis through three identified fundamental vertices: (1) understanding the neural correlates of core IR concepts to advance theoretical models of search behaviour, (2) enhancing existing IR systems through contextual integration of neurophysiological signals, and (3) developing proactive IR capabilities through direct neurophysiological measurement.

One of the foundational goals of Information Retrieval (IR) is to satisfy searchers ' Information Needs (IN).Understanding how INs physically manifest has long been a complex and elusive process.However, recent studies utilising Electroencephalography (EEG) data have provided real-time insights into the neural processes associated with INs.Unfortunately, they have yet to demonstrate how this insight can practically benefit the search experience.As such, within this study, we explore the ability to predict the realisation of IN within EEG data across 14 subjects whilst partaking in a Question-Answering (Q/A) task.Furthermore, we investigate the combinations of EEG features that yield optimal predictive performance, as well as identify regions within the Q/A queries where a subject's realisation of IN is more pronounced.The findings from this work demonstrate that EEG data is sufficient for the real-time prediction of the realisation of an IN across all subjects with an accuracy of 73.5% (SD 2.6%) and on a per-subject basis with an accuracy of 90.1% (SD 22.1%).This work helps to close the gap by bridging theoretical neuroscientific advancements with tangible improvements in information retrieval practices, paving the way for real-time prediction of the realisation of IN.

The realisation and the variability of information needs (IN) with respect to a searcher’s gap in knowledge is driven by the perceived Anomalous State of Knowledge (ASK). The concept of Feeling-of-Knowing (FOK), as the introspective feeling of knowledge awareness, shares the characteristics of an ASK state. From an IR perspective, FOK as a premise to trigger IN is unexplored. Motivated by the neuroimaging studies in IR, we investigate the neurophysiological drivers associated with FOK, to provide evidence validating FOK as a distinctive state in IN realisation. We employ Electroencephalography to capture the brain activity of 24 healthy participants performing a textual Question Answering IR scenario. We analyse the evoked neural patterns corresponding to three states of knowledge: i.e., (1)“I know”, (2)“FOK”, (3)“I do not know”. Our findings show the distinct neurophysiological signatures (N1, P2, N400, P6) in response to information segments processed in the context of our three levels. They further reveal that the brain manifestation associated with “FOK” does not significantly differ from the ones associated with “I do not know”, indicating their association with recognition of a gap in knowledge and as such could further inform the IN formation on different levels of knowing.

Music recommender systems are an integral part of our daily life. Recent research has seen a significant effort around black-box recommender based approaches such as Deep Reinforcement Learning (DRL). These advances have led, together with the increasing concerns around users’ data collection and privacy, to a strong interest in building responsible recommender systems. A key element of a successful music recommender system is modelling how users interact with streamed content. By first understanding these interactions, insights can be drawn to enable the construction of more transparent and responsible systems. An example of these interactions is skipping behaviour, a signal that can measure users’ satisfaction, dissatisfaction, or lack of interest. In this paper, we study the utility of users’ historical data for the task of sequentially predicting users’ skipping behaviour. To this end, we adapt DRL for this classification task, followed by a post-hoc explainability (SHAP) and ablation analysis of the input state representation. Experimental results from a real-world music streaming dataset (Spotify) demonstrate the effectiveness of our approach in this task by outperforming state-of-the-art models. A comprehensive analysis of our approach and of users’ historical data reveals a temporal data leakage problem in the dataset. Our findings indicate that, overall, users’ behaviour features are the most discriminative in how our proposed DRL model predicts music skips. Content and contextual features have a lesser effect. This suggests that a limited amount of user data should be collected and leveraged to predict skipping behaviour.

Podcasts are spoken documents that, in recent years, have gained widespread popularity. Despite the growing research interest in this domain, conducting user studies remains challenging due to the lack of datasets that include user behaviour. In particular, there is a need for a podcast streaming platform that reduces the overhead of conducting user studies. To address these issues, in this work, we present Podify. It is the first web-based platform for podcast streaming and consumption specifically designed for research. The platform highly resembles existing streaming systems to provide users with a high level of familiarity on both desktop and mobile. A catalogue of podcast episodes can be easily created via RSS feeds. The platform also offers Elasticsearch-based indexing and search that is highly customisable, allowing research and experimentation in podcast search. Users can manually curate playlists of podcast episodes for consumption. With mechanisms to collect explicit feedback from users (i.e., liking and disliking behaviour), Podify also automatically collects implicit feedback (i.e., all user interactions). Users' behaviour can be easily exported to a readable format for subsequent experimental analysis. A demonstration of the platform is available at https://youtu.be/k9Z5w_KKHr8, with the code and documentation available at https://github.com/NeuraSearch/Podify.

A fundamental goal of Information Retrieval (IR) is to satisfy search­ers' information need (IN). Advances in neuroimaging technologies have allowed for interdisciplinary research to investigate the brain activity associated with the realisation of IN. While these studies have been informative, they were not able to capture the cognitive processes underlying the realisation of IN and the interplay between them with a high temporal resolution. This paper aims to investigate this research question by inferring the variability of brain activity based on the contrast of a state of IN with the two other (no-IN) scenarios. To do so, we employed Electroencephalography (EEG) and constructed an Event-Related Potential (ERP) analysis of the brain signals captured while the participants were experiencing the realisation of IN. In particular, the brain signals of 24 healthy participants were captured while performing a Question-Answering (Q/A) Task. Our results show a link between the early stages of processing, corresponding to awareness and the late activity, meaning memory control mechanisms. Our findings also show that participants exhibited early N1-P2 complex indexing awareness processes and indicate, thus, that the realisation of IN is manifested in the brain before it reaches the user's consciousness. This research contributes novel insights into a better understanding of IN and informs the design of IR systems to better satisfy it.

In this article, we propose an approach to improve quality in crowdsourcing (CS) tasks using Task Completion Time (TCT) as a source of information about the reliability of workers in a game-theoretical competitive scenario. Our approach is based on the hypothesis that some workers are more risk-inclined and tend to gamble with their use of time when put to compete with other workers. This hypothesis is supported by our previous simulation study. We test our approach with 35 topics from experiments on the TREC-8 collection being assessed as relevant or non-relevant by crowdsourced workers both in a competitive (referred to as “Game”) and non-competitive (referred to as “Base”) scenario. We find that competition changes the distributions of TCT, making them sensitive to the quality (i.e., wrong or right) and outcome (i.e., relevant or non-relevant) of the assessments. We also test an optimal function of TCT as weights in a weighted majority voting scheme. From probabilistic considerations, we derive a theoretical upper bound for the weighted majority performance of cohorts of 2, 3, 4, and 5 workers, which we use as a criterion to evaluate the performance of our weighting scheme. We find our approach achieves a remarkable performance, significantly closing the gap between the accuracy of the obtained relevance judgements and the upper bound. Since our approach takes advantage of TCT, which is an available quantity in any CS tasks, we believe it is cost-effective and, therefore, can be applied for quality assurance in crowdsourcing for micro-tasks.

The ability to skip songs is a core feature in modern online streaming services. Its introduction has led to a new music listening paradigm and has changed the way users interact with the underlying services. Thus, understanding their skipping activity during listening sessions has acquired considerable importance. This is because such implicit feedback signal can be considered a measure of users' satisfaction (dissatisfaction or lack of interest), affecting their engagement with the platforms. Prior work has mainly focused on analysing the skipping activity at an individual song level. In this work, we investigate different behaviours during entire listening sessions with regards to the users' session-based skipping activity. To this end, we propose a data transformation and clustering-based approach to identify and categorise skipping types. Experimental results on the real-world music streaming dataset (Spotify) indicate four main types of session skipping behaviour. A subsequent analysis of short, medium, and long listening sessions demonstrate that these session skipping types are consistent across sessions of varying length. Furthermore, we discuss their distributional differences under various listening context information, i.e. day types (i.e. weekday and weekend), times of the day, and playlist types.

The graph data structure offers a highly expressive way of representing many real-world constructs such as social networks, chemical compounds, the world wide web, street maps, etc. In essence, any collection of entities and the relationships between them can be modelled using a graph, thus preserving more information about the real-world objects than a simple vector space model. An issue that arises when operating on collections of graphs, however, is that most statistical analysis and machine learning methods expect their input data to be in the form of multidimensional vectors, where all items can be compared with each other using well-understood metrics such as Euclidean or Manhattan distance. This paper presents a variety of approaches for computing distances between graphs with known node correspondence, with the aim of applying those measures alongside clustering algorithms to discover patterns in a given dataset. The performance of each distance measure is then evaluated through its ability to identify communities of graphs with similar features. We show that because the considered distance metrics highlight different structural properties, the method that produces the highest quality result will depend on the characteristics of the processed graph population.

Relevance is an essential concept in Information Retrieval (IR). Recent studies using brain imaging have significantly contributed towards the understanding of this concept, but only as a binary notion, i.e. a document being judged as relevant or non-relevant. While such a binary division is prevalent in IR, seminal theories have proposed relevance as a graded variable; i.e. having different degrees. In this paper, we aim to investigate the brain activity associated with relevance when it is treated as a graded concept. Twenty-five participants provided graded relevance judgements in the context of a Question Answering (Q/A) Task, during assessment with an electroencephalogram (EEG). Our findings show that significant differences in event-related potentials (ERPs) were observed in response to information segments processed in the context of high-relevance, low-relevance and no-relevance, supporting the concept of graded relevance. We speculate that differences in attentional engagement, semantic mismatch (between the question and answer) and memory processing underpin the electrophysiological responses to the graded relevance judgements. We believe our conclusions constitute an important step in unravelling the nature of graded relevance and knowledge of the electrophysiological modulation to each grade of relevance will help to improve the design and evaluation of IR systems.

Procurement legislation stipulates that information about the goods, services, or works, that tax-funded authorities wish to purchase are made publicly available in a procurement contract notice. However, for businesses wishing to tender for such competitive opportunities, finding relevant procurement contract notices presents a challenging professional search task. In this talk, we will provide an overview of procurement search and then describe the challenges in addressing the related search and recommendation tasks.

The goal of Information Retrieval (IR) systems is to satisfy searchers' Information Need (IN). Our research focuses on next-generation IR engines, which can proactively detect, identify, and serve INs without receiving explicit queries. It is essential, therefore, to be able to detect when INs occur. Previous research has established that a realisation of INs physically manifests itself with specific brain activity. With this work we take the next step, showing that monitoring brain activity can lead to accurate predictions of a realisation of IN occurrence. We have conducted experiments whereby twenty-four participants performed a Q/A Task, while their brain activity was being monitored using functional Magnetic Resonance Imaging (fMRI) technology. The questions were selected and developed from the TREC-8 and TREC 2001 Q/A Tracks. We present two methods for predicting the realisation of an IN, i.e. Generalised method (GM) and Personalised method (PM). GM is based on the collective brain activity of all twenty-four participants in a predetermined set of brain regions known to be involved in representing a realisation of INs. PM is unique to each individual and employs a 'Searchlight' analysis to locate brain regions informative for distinguishing when a “specific” user realises an information need. The results of our study show that both methods were able to predict a realisation of an IN (statistically) significantly better than chance. Our results also show that PM (statistically) significantly outperformed GM in terms of prediction accuracy. These encouraging findings make the first fundamental step towards proactive IR engines based on brain signals.

Result diversification aims to provide searchers with a broader view of a given topic while attempting to maximise the chances of retrieving relevant material. Diversifying results also aims to reduce search bias by increasing the coverage over different aspects of the topic. As such, searchers should learn more about the given topic in general. Despite diversification algorithms being introduced over two decades ago, little research has explicitly examined their impact on search behaviour and performance in the context of Interactive Information Retrieval (IIR) . In this paper, we explore the impact of diversification when searchers undertake complex search tasks that require learning about different aspects of a topic (aspectual retrieval) . We hypothesise that by diversifying search results, searchers will be exposed to a greater number of aspects. In turn, this will maximise their coverage of the topic (and thus reduce possible search bias). As a consequence, diversification should lead to performance benefits, regardless of the task, but how does diversification affect search behaviours and search satisfaction? Based on Information Foraging Theory (IFT) , we infer two hypotheses regarding search behaviours due to diversification, namely that (i) it will lead to searchers examining fewer documents per query, and (ii) it will also mean searchers will issue more queries overall. To this end, we performed a within-subjects user study using the TREC AQUAINT collection with 51 participants, examining the differences in search performance and behaviour when using (i) a non-diversified system ( BM25 ) versus (ii) a diversified system (BM25 + xQuAD ) when the search task is either (a) ad-hoc or (b) aspectual. Our results show a number of notable findings in terms of search behaviour: participants on the diversified system issued more queries and examined fewer documents per query when performing the aspectual search task. Furthermore, we showed that when using the diversified system, participants were: more successful in marking relevant documents, and obtained a greater awareness of the topics (i.e. identified relevant documents containing more novel aspects). These findings show that search behaviour is influenced by diversification and task complexity. They also motivate further research into complex search tasks such as aspectual retrieval—and how diversity can play an important role in improving the search experience, by providing greater coverage of a topic and mitigating potential bias in search results.

The field of neuroscience has fruitfully contributed to a wide variety of other fields, for example, economics, marketing and information systems, where the broad adoption and influence of neurophysiological (NP) research tools led to the creation of several new subfields, including neuroeconomics.

The main goal of information retrieval (IR) is to satisfy information need (IN). IN refers to a complex concept: at the very initial state of the phenomenon (that is, at a visceral level), even the searcher may not be aware of its existence. Thus, despite advances in the past few decades in both the IR and relevant scientific communities, we do not fully understand how an IN emerges and how it is physically manifested. In this article we aim to inform a holistic view of the realization of IN using functional magnetic resonance imaging. We collected new data of brain activity of 24 participants while they formulated and stated a realization of IN in a Question Answering task, focusing on a distributed set of brain regions associated with activities related to IN, found in our previous study. Results of a functional connectivity analysis led us to propose a neuropsychological model of the realization of IN. Our model consists of three components: (a) a successful memory retrieval component, (b) an information flow regulation component, and (c) a high‐level perception component. We believe this study constitutes an important step in unraveling the nature of IN and how to better satisfy IN.

Fine-grained geolocation of tweets has become an important feature for reliably performing a wide range of tasks such as real-time event detection, topic detection or disaster and emergency analysis. Recent work adopted a ranking approach to return a predicted location based on content-based similarity to already available individual geotagged tweets. However, this work made use of the IDF weighting model to compute the ranking, which can diminish the quality of the Top-N retrieved tweets. In this work, we adopt a learning to rank approach towards improving the effectiveness of the ranking and increasing the accuracy of fine-grained geolocalisation. To this end we propose a set of features extracted from pairs of geotagged tweets generated within the same fine-grained geographical area (squared areas of size 1 km). Using geotagged tweets from two cities (Chicago and New York, USA), our experimental results show that our learning to rank approach significantly outperforms previous work based on IDF ranking, and improves accuracy of tweet geolocalisation at a fine-grained level.

Search is one of the most performed activities on the World Wide Web. Various conceptual models postulate that the search process can be broken down into distinct emotional and cognitive states of searchers while they engage in a search process. These models significantly contribute to our understanding of the search process. However, they are typically based on self-report measures, such as surveys, questionnaire, etc. and therefore, only indirectly monitor the brain activity that supports such a process. With this work, we take one step further and directly measure the brain activity involved in a search process. To do so, we break down a search process into five time periods: a realisation of Information Need, Query Formulation, Query Submission, Relevance Judgment and Satisfaction Judgment. We then investigate the brain activity between these time periods. Using functional Magnetic Resonance Imaging (fMRI), we monitored the brain activity of twenty-four participants during a search process that involved answering questions carefully selected from the TREC-8 and TREC 2001 Q/A Tracks. This novel analysis that focuses on transitions rather than states reveals the contrasting brain activity between time periods - which enables the identification of the distinct parts of the search process as the user moves through them. This work, therefore, provides an important first step in representing the search process based on the transitions between neural states. Discovering more precisely how brain activity relates to different parts of the search process will enable the development of brain-computer interactions that better support search and search interactions, which we believe our study and conclusions advance.

Given the proliferation of social media and the abundance of news feeds, a substantial amount of real-time content is distributed through disparate sources, which makes it increasingly difficult to glean and distill useful information. Although combining heterogeneous sources for topic detection has gained attention from several research communities, most of them fail to consider the interaction among different sources and their intertwined temporal dynamics. To address this concern, we studied the dynamics of topics from heterogeneous sources by exploiting both their individual properties (including temporal features) and their inter-relationships. We first implemented a heterogeneous topic model that enables topic–topic correspondence between the sources by iteratively updating its topic–word distribution. To capture temporal dynamics, the topics are then correlated with a time-dependent function that can characterise its social response and popularity over time. We extensively evaluate the proposed approach and compare to the state-of-the-art techniques on heterogeneous collection. Experimental results demonstrate that our approach can significantly outperform the existing ones.

As an empirical discipline, information access and retrieval research requires substantial software infrastructure to index and search large collections. This workshop is motivated by the desire to better align information retrieval research with the practice of building search applications from the perspective of open-source information retrieval systems. Our goal is to promote the use of Lucene for information access and retrieval research.

The design and presentation of a Search Engine Results Page (SERP) has been subject to much research. With many contemporary aspects of the SERP now under scrutiny, work still remains in investigating more traditional SERP components, such as the result summary. Prior studies have examined a variety of different aspects of result summaries, but in this paper we investigate the influence of result summary length on search behaviour, performance and user experience. To this end, we designed and conducted a within-subjects experiment using the TREC AQUAINT news collection with 53 participants. Using Kullback-Leibler distance as a measure of information gain, we examined result summaries of different lengths and selected four conditions where the change in information gain was the greatest: (i) title only; (ii) title plus one snippet; (iii) title plus two snippets; and (iv) title plus four snippets. Findings show that participants broadly preferred longer result summaries, as they were perceived to be more informative. However, their performance in terms of correctly identifying relevant documents was similar across all four conditions. Furthermore, while the participants felt that longer summaries were more informative, empirical observations suggest otherwise; while participants were more likely to click on relevant items given longer summaries, they also were more likely to click on non-relevant items. This shows that longer is not necessarily better, though participants perceived that to be the case - and second, they reveal a positive relationship between the length and informativeness of summaries and their attractiveness (i.e. clickthrough rates). These findings show that there are tensions between perception and performance when designing result summaries that need to be taken into account.

This paper describes a novel approach to re-ranking search engine result pages (SERP): Its fundamental principle is to re-rank results to a given query, based on exploiting evidence gathered from past similar search queries. Our approach is inspired by collaborative filtering, with the main challenge being to find the set of similar queries, while also taking efficiency into account. In particular, our approach aims to address this challenge by proposing a combination of a similarity graph and a locality sensitive hashing scheme. We construct a set of features from our similarity graph and build a prediction model using the Hoeffding decision tree algorithm. We have evaluated the effectiveness of our model in terms of P@1, MAP@10, and nDCG@10, using the Yandex Data Challenge data set. We have compared the performance of our model against two baselines, namely, the Yandex initial ranking and the decision tree model learnt on the same set of features when extracted based on query repetition (i.e. excluding the evidence of similar queries in our approach). Our results reveal that the proposed approach consistently and (statistically) significantly outperforms both baselines.

In this paper we introduce a game scenario for crowdsourcing (CS) using incentives as a bait for careless (gambler) workers, who respond to them in a characteristic way. We hypothesise that careless workers are risk-inclined and can be detected in the game scenario by their use of time, and test this hypothesis in two steps: first, we formulate and prove a theorem stating that a risk-inclined worker will react to competition with shorter Task Completion Time (TCT) than a risk-neutral or risk-averse worker. Second, we check if the game scenario introduces a link between TCT and performance, by performing a crowdsourced evaluation using 35 topics from the TREC-8 collection. Experimental evidence confirms our hypothesis, showing that TCT can be used as a powerful discrimination factor to detect careless workers. This is a valuable result in the quest for quality assurance in CS-based micro tasks such as relevance assessment.

The raison d'etre of IR is to satisfy human information need. But, do we really understand information need? Despite advances in the past few decades in both the IR and relevant scientific communities, this question is largely unanswered. We do not really understand how an information need emerges and how it is physically manifested. Information need refers to a complex concept: at the very initial state of the phenomenon (i.e. at a visceral level), even the searcher may not be aware of its existence. This renders the measuring of this concept (using traditional behaviour studies) nearly impossible. In this paper, we investigate the connection between an information need and brain activity. Using functional Magnetic Resonance Imaging (fMRI), we measured the brain activity of twenty four participants while they performed a Question Answering (Q/A) Task, where the questions were carefully selected and developed from TREC-8 and TREC 2001 Q/A Track. The results of this experiment revealed a distributed network of brain regions commonly associated with activities related to information need and retrieval and differing brain activity in processing scenarios when participants knew the answer to a given question and when they did not and needed to search. We believe our study and conclusions constitute an important step in unravelling the nature of information need and therefore better satisfying it.

Despite the ever-increasing popularity of crowdsourcing (CS) in both industry and academia, procedures that ensure quality in its results are still elusive. We hypothesise that a CS design based on game theory can persuade workers to perform their tasks as quickly as possible with the highest quality. In order to do so, in this article we propose a CS framework inspired by then-person Chicken game. Our aim is to address the problem of CS quality without compromising on CS benefits such as low monetary cost and high task completion speed. With that goal in mind, we study the effects of knowledge updates as well as incentives for good workers to continue playing. We define a general task with the characteristics of relevance assessment as a case study, because it has been widely explored in the past with CS due to its potential cost and complexity. In order to investigate our hypotheses, we conduct a simulation where we study the effect of the proposed framework on data accuracy, task completion time, and total monetary rewards. Based on a game-theoretical analysis, we study how different types of individuals would behave under a particular game scenario. In particular, we simulate a population comprised of different types of workers with varying ability to formulate optimal strategies and learn from their experiences. A simulation of the proposed framework produced results that support our hypothesis.

Urban Informatics aims to exploit the large quantities of information produced by modern cities in order to gain insights into how they function. These insights lay the foundation for improving the lives of citizens, by improving the efficacy and efficiency of public services, and satisfying complex information needs arising within this context. The goal of the workshop is to provide a multidisciplinary forum which brings together researchers in Big Data (BD), Information Retrieval (IR), Data Mining, and Urban Studies, to explore novel solutions to the numerous theoretical, practical and ethical challenges arising in this context. These include difficulties in collecting city data, creating data management infrastructures, and providing new effective and efficient information access techniques to as many users as possible in the context of a smart city. To foster the development of new BD and IR approaches in Urban Informatics, the workshop makes available a representative dataset of city data, including Internet-based visual (Flickr) and textual (Tweets and News) media collections. The workshop provides enormous opportunities for data scientists who wish to understand the complexities of working with city data, conduct innovative research within Urban Informatics, and build a long-term community in this emerging research area.

Relevance is a central notion in Information Retrieval, but it is considered to be a difficult concept to define. We analyse brain signals for the first 800 milliseconds (ms) of a relevance assessment process to answer the question "when relevance is happening in the brain?" with the belief that it will lead to better operational definitions of relevance. For this purpose, we devised a user study in which we captured the brain response of 20 participants. Using a 64-channel EEG device, we measured the electrophysiological activity of the brain while the subjects were in the phase of giving an explicit judgement about the relevance of presented images according to a given topic. Analyses were then performed over different time windows of the recorded EEG signals using repeated measures ANOVA. Data reveal significant variation between relevance and non-relevance within the EEG signals from the presentation of the image to 800 milliseconds afterwards. At an early stage these differences were located at frontal and posterior electrode sites. However, at later stages these differences were located in central, centro-parietal and centro-frontal areas.Our findings are an important step towards (i) a better understanding of the concept of relevance and (ii) a more effective implicit feedback systems.

Predicting popular content is a challenging problem for social media websites in order to encourage user interactions and activity. Existing works in this area, including the recommendation approach used by Flickr (called "interestingness"), consider only click through data, tags, comments and explicit user feedback in this computation. On image sharing websites, however, many images are annotated with no tags and initially, an image has no interaction data. In this case, these existing approaches fail due to lack of evidence. In this paper, we therefore focus on image popularity prediction in a cold start scenario (i.e. where there exist no, or limited, textual/interaction data), by considering an image's context, visual appearance and user context. Specifically, we predict the number of comments and views an image has based on a number of new features for this propose. Experimenting on the MIR-Flickr 1M collection, we are able to overcome the problems associated with popularity prediction in a cold start, achieving accuracy of up to 76%.

Despite the popularity of Twitter for research, there are very few publicly available corpora, and those which are available are either too small or unsuitable for tasks such as event detection. This is partially due to a number of issues associated with the creation of Twitter corpora, including restrictions on the distribution of the tweets and the difficultly of creating relevance judgements at such a large scale. The difficulty of creating relevance judgements for the task of event detection is further hampered by ambiguity in the definition of event. In this paper, we propose a methodology for the creation of an event detection corpus. Specifically, we first create a new corpus that covers a period of 4 weeks and contains over 120 million tweets, which we make available for research. We then propose a definition of event which fits the characteristics of Twitter, and using this definition, we generate a set of relevance judgements aimed specifically at the task of event detection. To do so, we make use of existing state-of-the-art event detection approaches and Wikipedia to generate a set of candidate events with associated tweets. We then use crowdsourcing to gather relevance judgements, and discuss the quality of results, including how we ensured integrity and prevented spam. As a result of this process, along with our Twitter corpus, we release relevance judgements containing over 150,000 tweets, covering more than 500 events, which can be used for the evaluation of event detection approaches.

Image tagging is a growing application on social media websites, however, the performance of many auto-tagging methods are often poor. Recent work has exploited an image's context (e.g. time and location) in the tag recommendation process, where tags which co-occur highly within a given time interval or geographical area are promoted. These models, however, fail to address how and when different image contexts can be combined. In this paper, we propose a weighted tag recommendation model, building on an existing state-of-the-art, which varies the importance of time and location in the recommendation process, based on a given set of input tags. By retrieving more temporally and geographically relevant tags, we achieve statistically significant improvements to recommendation accuracy when testing on 519k images collected from Flickr. The result of this paper is an important step towards more effective image annotation and retrieval systems.

The effectiveness of various behavioural signals for implicit relevance feedback models has been exhaustively studied. Despite the advantages of such techniques for a real time information retrieval system, most of the behavioural signals are noisy and therefore not reliable enough to be employed. Among many, a combination of dwell time and task information has been shown to be effective for relevance judgement prediction. However, the task information might not be available to the system at all times. Thus, there is a need for other sources of information which can be used as a substitute for task information. Recently, affective and physiological signals have shown promise as a potential source of information for relevance judgement prediction. However, their accuracy is not high enough to be applicable on their own.

The complex and dynamic nature of search processes surrounding information seeking have been exhaustively studied. Recent studies have highlighted search processes with different intentions, such as those for entertainment purposes or re-finding a visited information object, are fundamentally different in nature to typical information seeking intentions. Despite the popularity of such search processes on the Web, they have not yet been thoroughly explored. Using a video retrieval system as a use case, we study the characteristics of four different search task types: seeking information, re-finding a particular information object, and two different entertainment intentions (i.e. entertainment by adjusting arousal level, and entertainment by adjusting mood). In particular, we looked at the cognition, emotion and action aspects of these search tasks at different phases of a search process. This follows the common assumption in the information seeking and retrieval community that a complex search process can be broken down into a relatively small number of activity phases. Our experimental results show significant differences in the characteristics of studied search tasks. Furthermore, we investigate whether we can predict these search tasks given user's interaction with the system. Results show that we can learn a model that predicts the search task types with reasonable accuracy. Overall, these findings may help to steer search engines to better satisfy searchers' needs beyond typically assumed information seeking processes.

Collaborative filtering (CF) aims to recommend items based on prior user interaction. Despite their success, CF techniques do not handle data sparsity well, especially in the case of the cold start problem where there is no past rating for an item. In this paper, we provide a framework, which is able to tackle such issues by considering item-related emotions and semantic data. In order to predict the rating of an item for a given user, this framework relies on an extension of Latent Dirichlet Allocation, and on gradient boosted trees for the final prediction. We apply this framework to movie recommendation and consider two emotion spaces extracted from the movie plot summary and the reviews, and three semantic spaces: actor, director, and genre. Experiments with the 100K and 1M MovieLens datasets show that including emotion and semantic information significantly improves the accuracy of prediction and improves upon the state-of-the-art CF techniques. We also analyse the importance of each feature space and describe some uncovered latent groups.

As digital collections expand, the importance of the temporal aspect of information has become increasingly apparent. The aim of this paper is to investigate the effect of using long-term temporal profiles of terms in information retrieval by enhancing the term selection process of pseudo-relevance feedback (PRF). For this purpose, two temporal PRF approaches were introduced considering only temporal aspect and temporal along with textual aspect. Experiments used the AP88-89 and WSJ87-92 test collections with TREC Ad-Hoc Topics 51-100. Term temporal profiles are extracted from the Google Books n-grams dataset. The results show that the long-term temporal aspects of terms are capable of enhancing retrieval effectiveness.

Blogs facilitate online debates and discussions for millions of people around the world. Identifying the most popular and prevailing topics discussed in the Blogosphere is a crucial task. This poster describes our novel approach to the quantification of the level of topic propagation in the Blogosphere. Our model uses graph-theoretic representations of the Blogosphere's link structures that allows it to deduce the `Percolation Threshold', which is then used in the quantification and definition of a global topic. The result of our experiments on a blog collection shows that our model is able to quantify the propagation of topics. Moreover, our model is successful in identifying specific topics that propagate throughout the Blogosphere and classifies them as `Global'.

No abstract available.