Conversational information retrieval is challenging since it requires the consideration of the conversation history which potentially gives rise to topic shifts and coreference resolution across previous turns. To address these challenges, previous work mainly rely on traditional fine-tuning of ad-hoc retrievers on conversational datasets or extrapolates their generalizability through multi-tasking. However, this mainstream approach is costly—since it requires model re-training—and exhibits catastrophic forgetting, where the model loses its foundational ad-hoc retrieval performance. In this paper, we fill this gap by introducing model merging as a training-free strategy enabling the design of a single retrieval model that operates across both ad-hoc and conversational settings with no additional fine-tuning. We conduct experiments using linear and non-linear parameter-wise merging strategies—namely Model Soup and Slerp—on standard ad-hoc search and conversational retrieval datasets. Our results demonstrate that model merging significantly enhances the ad-hoc search capabilities of conversational retrievers while improving generalizability across task-specific datasets, achieving up to 15% higher NDCG@3 under zero-shot conditions.

Electronic Health Records (EHRs) provide crucial information for clinical decision-making. However, their high-dimensionality, heterogeneity, and sparsity make clinical prediction challenging. Large Language Models (LLMs) allowed progress towards addressing this challenge by leveraging parametric medical knowledge to enhance EHR data for clinical prediction tasks. Despite the significant achievements made so far, most of the existing approaches are fundamentally task-agnostic in the sense that they deploy LLMs as EHR encoders or EHR completion modules without fully integrating signals from the prediction tasks. This naturally hinders task performance accuracy. In this work, we propose Rewrite-To-Predict (ReToP), an LLM-based framework that addresses this limitation through an end-to-end training of an EHR rewriter and a clinical predictor. To cope with the lack of EHR rewrite training data, we generate synthetic pseudo-labels using clinical-driven feature selection strategies to create diverse patient rewrites for fine-tuning the EHR rewriter. ReToP aligns the rewriter with prediction objectives using a novel Classifier Supervised Contribution (CSC) score that enables the EHR rewriter to generate clinically relevant rewrites that directly enhance prediction. Our ReToP framework surpasses strong baseline models across three clinical tasks on MIMIC-IV. Moreover, the analysis of ReToP shows its generalizability to unseen datasets and tasks with minimal fine-tuning while preserving faithful rewrites and emphasizing task-relevant predictive features.

International audience

In this demo paper, we present a new open-source python module for building information retrieval pipelines with transformers namely CHERCHE. Our aim is to propose an easy to plug tool capable to execute, simple but strong, state-of-the-art information retrieval models. To do so, we have integrated classical models based on lexical matching but also recent models based on semantic matching. Indeed, a large number of models available on public hubs can be now tested on information retrieval tasks with only a few lines. CHERCHE is oriented to newcomers into the neural information retrieval field that want to use transformer-based models in small collections without struggling with heavy tools. The code and documentation of CHERCHE is public available at https://github.com/raphaelsty/cherche

While great progress is made in the area of information access, there are still open issues that involve designing intelligent systems supporting task-based search. Despite the importance of task-based search, the information retrieval and information science communities still feel the lack of open-ended and annotated datasets that enable the evaluation of a number of related facets of search tasks in downstream applications. Existing datasets are either sampled from large-scale logs but provide poor annotations, or sampled from lower-scale user studies but focus on ranked list evaluation. In this work, we present CoST: a novel richly annotated dataset for evaluating complex search tasks, collaboratively designed by researchers from the computer science and cognitive psychology domains, and intended to answer a wide range of research questions dealing with task-based search. CoST includes 5667 queries recorded in 630 task-based sessions that result from a user study involving 70 french native participants who are expert in one among 3 different domains of expertise (computer science, medicine, psychology). Each participant completed 15 tasks with 5 different types of cognitive complexity (fact-finding, exploratory learning, decision-making, problem-solving, multicriteria-inferential). In addition to search data (e.g., queries and clicks), CoST provides task and session-related data, task annotations and query annotations. We illustrate possible usages of CoST through the evaluation of query classification models and the understanding of the effect of task complexity and domain on user's search behavior.

The impressive increasing availability of social media posts has given rise to considerable research challenges. This article is concerned with the problem of semantic location prediction of geotagged tweets. The underlying task is to associate to a social media post, the focal spatial object, if any (e.g., Place Of Interest POI), it topically focuses on. Although relevant for a number of applications such as POI recommendation, this problem has not so far received the attention it deserves. In previous work, the problem has mainly been tackled by means of language models that rely on costly probability estimation of word relevance across spatial regions. We propose the Spatially-aware Geotext Matching (SGM) model, which relies on a neural network learning framework. The model combines exact word-word-local interaction matching signals with semantic global tweet-POI interaction matching signals. The local interactions are built over kernel spatial word distributions that allow revealing spatially driven word pair similarity patterns. The global interactions consider the strength of the interaction between the tweet and the POI from both the spatial and semantic perspectives. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed SGM model compared to state-of-the-art baselines including language models and traditional neural interaction-based models.

An intensive recent research work investigated the combined use of hand-curated knowledge resources and corpus-driven resources to learn effective text representations. The overall learning process could be run by online revising the learning objective or by offline refining an original learned representation. The differentiated impact of each of the learning approaches on the quality of the learned representations has not been studied so far in the literature. This article focuses on the design of comparable offline vs. online knowledge-enhanced document representation learning models and the comparison of their effectiveness using a set of standard IR and NLP downstream tasks. The results of quantitative and qualitative analyses show that (1) offline vs. online learning approaches have dissimilar result trends regarding the task as well as the dataset distribution counts with regard to domain application; (2) while considering external knowledge resources is undoubtedly beneficial, the way used to express relational constraints could affect semantic inference effectiveness. The findings of this work present opportunities for the design of future representation learning models, but also for providing insights about the evaluation of such models.

The workshop on the evaluation of collaborative information retrieval and seeking (ECol) is held in conjunction with the ACM SIGIR Conference on Human Information Interaction & Retrieval (CHIIR) in Oslo, Norway. To make the workshop active and the participant pro-active, we released datasets and tools so as to help researchers contributing to the formalization of evaluation frameworks for challenging collaborative tasks. The workshop is split into two parts. First, a presentation session. Then, the afternoon is devoted to group discussion addressing challenges of evaluating and designing models for social and collaborative search.

In this paper, we specifically consider the challenging task of solving a question posted on Twitter. The latter generally remains unanswered and most of the replies, if any, are only from members of the questioner's neighborhood. As outlined in previous work related to community Q&A, we believe that question-answering is a collaborative process and that the relevant answer to a question post is an aggregation of answer nuggets posted by a group of relevant users. Thus, the problem of identifying the relevant answer turns into the problem of identifying the right group of users who would provide useful answers and would possibly be willing to collaborate together in the long-term. Accordingly, we present a novel method, called CRAQ, that is built on the collaboration paradigm and formulated as a group entropy optimization problem. To optimize the quality of the group, an information gain measure is used to select the most likely ``informative" users according to topical and collaboration likelihood predictive features. Crowd-based experiments performed on two crisis-related Twitter datasets demonstrate the effectiveness of our collaborative-based answering approach.

Collaborative Information Seeking/Retrieval (CIS/CIR) has given rise to several challenges in terms of search behavior analysis, retrieval model formalization as well as interface design. However, the major issue of evaluation in CIS/CIR is still underexplored. The goal of this workshop is to investigate the evaluation challenges in CIS/CIR with the hope of building standardized evaluation frameworks, methodologies, and task specifications that would foster and grow the research area (in a collaborative fashion).

Collaborative information retrieval systems often rely on division of labor policies. Such policies allow work to be divided among collaborators with the aim of preventing redundancy and optimizing the synergic effects of collaboration. Most of the underlying methods achieve these goals by the means of explicit vs. implicit role-based mediation. In this paper, we investigate whether and how different factors, such as users' behavior, search strategies, and effectiveness, are related to role assignment within a collaborative exploratory search. Our main findings suggest that: (1) spontaneous and cohesive implicit roles might emerge during the collaborative search session implying users with no prior roles, and that these implicit roles favor the search precision, (2) role drift might occur alongside the search session performed by users with prior-assigned roles.

A large body of research work examined, from both the query side and the user behavior side, the characteristics of medical‐ and health‐related searches. One of the core issues in medical information retrieval (IR) is diversity of tasks that lead to diversity of categories of information needs and queries. From the evaluation perspective, another related and challenging issue is the limited availability of appropriate test collections allowing the experimental validation of medically task oriented IR techniques and systems. In this paper, we explore the peculiarities of TREC and CLEF medically oriented tasks and queries through the analysis of the differences and the similarities between queries across tasks, with respect to length, specificity, and clarity features and then study their effect on retrieval performance. We show that, even for expert oriented queries, language specificity level varies significantly across tasks as well as search difficulty. Additional findings highlight that query clarity factors are task dependent and that query terms specificity based on domain‐specific terminology resources is not significantly linked to term rareness in the document collection. The lessons learned from our study could serve as starting points for the design of future task‐based medical information retrieval frameworks.

Most of the previous approaches surrounding collaborative information retrieval (CIR) provide either a user-based mediation, in which the system only supports users' collaborative activities, or a system-based mediation, in which the system plays an active part in balancing user roles, re-ranking results, and distributing them to optimize overall retrieval performance. In this paper, we propose to combine both of these approaches by a role mining methodology that learns from users' actions about the retrieval strategy they adapt. This hybrid method aims at showing how users are different and how to use these differences for suggesting roles. The core of the method is expressed as an algorithm that (1) monitors users' actions in a CIR setting; (2) discovers differences among the collaborators along certain dimensions; and (3) suggests appropriate roles to make the most out of individual skills and optimize IR performance. Our approach is empirically evaluated and relies on two different laboratory studies involving 70 pairs of users. Our experiments show promising results that highlight how role mining could optimize the collaboration within a search session. The contributions of this work include a new algorithm for mining user roles in collaborative IR, an evaluation methodology, and a new approach to improve IR performance with the operationalization of user-driven system-mediated collaboration.

Collaborative information retrieval involves retrieval settings in which a group of users collaborates to satisfy the same underlying need. One core issue of collaborative IR models involves either supporting collaboration with adapted tools or developing IR models for a multiple-user context and providing a ranked list of documents adapted for each collaborator. In this paper, we introduce the first document-ranking model supporting collaboration between two users characterized by roles relying on different domain expertise levels. Specifically, we propose a two-step ranking model: we first compute a document-relevance score, taking into consideration domain expertise-based roles. We introduce specificity and novelty factors into language-model smoothing, and then we assign, via an Expectation–Maximization algorithm, documents to the best-suited collaborator. Our experiments employ a simulation-based framework of collaborative information retrieval and show the significant effectiveness of our model at different search levels.

Recently, an increasing number of information retrieval studies have triggered a resurgence of interest in redefining the algorithmic estimation of relevance, which implies a shift from topical to multidimensional relevance assessment. A key underlying aspect that emerged when addressing this concept is the aggregation of the relevance assessments related to each of the considered dimensions. The most commonly adopted forms of aggregation are based on classical weighted means and linear combination schemes to address this issue. Although some initiatives were recently proposed, none was concerned with considering the inherent dependencies and interactions existing among the relevance criteria, as is the case in many real‐life applications. In this article, we present a new fuzzy‐based operator, called iAggregator, for multidimensional relevance aggregation. Its main originality, beyond its ability to model interactions between different relevance criteria, lies in its generalization of many classical aggregation functions. To validate our proposal, we apply our operator within a tweet search task. Experiments using a standard benchmark, namely, Text REtrieval Conference Microblog, emphasize the relevance of our contribution when compared with traditional aggregation schemes. In addition, it outperforms state‐of‐the‐art aggregation operators such as the Scoring and the And prioritized operators as well as some representative learning‐to‐rank algorithms.

A new challenge, accessing multiple relevant entities, arises from the availability of linked heterogeneous data. In this article, we address more specifically the problem of accessing relevant entities, such as publications and authors within a bibliographic network, given an information need. We propose a novel algorithm, called BibRank, that estimates a joint relevance of documents and authors within a bibliographic network. This model ranks each type of entity using a score propagation algorithm with respect to the query topic and the structure of the underlying bi‐type information entity network. Evidence sources, namely content‐based and network‐based scores, are both used to estimate the topical similarity between connected entities. For this purpose, authorship relationships are analyzed through a language model‐based score on the one hand and on the other hand, non topically related entities of the same type are detected through marginal citations. The article reports the results of experiments using the Bibrank algorithm for an information retrieval task. The CiteSeerX bibliographic data set forms the basis for the topical query automatic generation and evaluation. We show that a statistically significant improvement over closely related ranking models is achieved.

This paper interests in social search over social networking services, typically in microblogging networks. We propose a new approach that integrates, within a Bayesian network model, new relevance factors such as the social importance of microbloggers and the temporal magnitude of tweets. In particular, the social importance of a microblogger is assimilated to his influence on the social network. This property is evaluated by applying PageRank algorithm on the social network of retweets and mentions. The temporal magnitude of microblogs is estimated based on temporal neighbors that present similar query terms. To validate our approach, we conducted a series of experiments on the TREC 2011 Microblog dataset. Results show that the integration of social and temporal features increases the retrieval effectiveness.

General Web search engines characterized by "onesize fits all" provide the same results for the same keyword queries even though these latter are submitted by different users with different intentions. In mobile Web search, the expected results for some queries could vary depending upon the user'slocation. We believe that identifying user's geographic intent in Web search can help to personalize search results by ranking local search results higher in the search results lists. Therefore, the objective of this paper is twofold: first to identify whether a mobile user query is location sensitive and second to personalize Web search results for these queries. In order to achieve these objectives, we propose to build a location language model for queries as a location query profile. Based on this latter, we compute two features issued from the domains of probability theory and Information theory, namely the Kurtosis and Kullback-Leibler Divergence measures in order to automatically classify location sensitive queries. The classification scheme is then integrated into a personalization process according to two approaches: refinement and re-ranking. Experimental evaluation using a sample of queries from AOL log and top documents returned by Google search, shows that the proposed model achieves high accuracy in identifying local sensitive queries and shows significant improvement on search relevance when integrated to a search engine.

In the past, most personalized retrieval models have been solely based on the computational behavior of the user to model the user profile. Personalized mobile search should however take the changing environment of the mobile user into account in order to better improve the search results quality. In this paper we propose an approach to personalize search results for mobile users by exploiting both cognitive and spatiotemporal context of the user. We propose to model the user on three semantic dimensions: time, location and interests. A case based reasoning approach is adopted to select the appropriate user profile for re-ranking the search results. In the absence of a standard evaluation framework for mobile search, we propose an evaluation scenario based on diary study entries. Our experiments undertaken in front of Yahoo boss search service shows that our retrieval approach is effective.

In this poster, we describe a personalized search approach, which involves a graph based user profile issued from ontology and a session boundary recognition mechanism. The user profile refers to the short term user interest and is used for re-ranking the search results of queries in the same search session. The session boundary recognition is based on tracking changes in the dominant concepts held by the query and the user profile. Experimental evaluation was carried out using the HARD 2003 TREC collection and shows that our approach is effective.

Abstract This article presents a genetic relevance optimization process performed in an information retrieval system. The process uses genetic techniques for solving multimodal problems (niching) and query reformulation techniques commonly used in information retrieval. The niching technique allows the process to reach different relevance regions of the document space. Query reformulation techniques represent domain knowledge integrated in the genetic operators structure to improve the convergence conditions of the algorithm. Experimental analysis performed using a TREC subcollection validates our approach.

No abstract available.