Laure Soulier

Social Media-Based Collaborative Information Access: Analysis of Online Crisis-Related Twitter Conversations

The notion of implicit (or explicit) collaborative information access refers to systems and practices allowing a group of users to unintentionally (respectively intentionally) seek, share and retrieve information to achieve similar (respectively shared) information-related goals. Despite an increasing adoption in social environments, collaboration behavior in information seeking and retrieval is mainly limited to small-sized groups, generally restricted to working spaces. Much remains to be learned about collaborative information seeking within open web social spaces. This paper is an attempt to better understand either implicit or explicit collaboration by studying Twitter, one of the most popular and widely used social networks. We study in particular the complex intertwinement of human interactions induced by both collaboration and social networking. We empirically explore explicit collaborative interactions based on focused conversation streams during two crisis. We identify structural patterns of temporally representative conversation subgraphs and represent their topics using Latent Dirichlet Allocation (LDA) modeling. Our main findings suggest that: 1) the critical mass of collaboration is generally limited to small-sized flat networks, with or without an influential user, 2) users are active as members of weakly overlapping groups and engage in numerous collaborative search and sharing tasks dealing with different topics, and 3) collaborative group ties evolve within the time-span of conversations.

A Collaborative Document Ranking Model for a Multi-faceted Search

This paper presents a novel collaborative document ranking model which aims at solving a complex information retrieval task involving a multi-faceted information need. For this purpose, we consider a group of users, viewed as experts, who collaborate by addressing the different query facets. We propose a two-step algorithm based on a relevance feedback process which first performs a document scoring towards each expert and then allocates documents to the most suitable experts using the Expectation-Maximisation learning-method. The performance improvement is demonstrated through experiments using TREC interactive benchmark.

On ranking relevant entities in heterogeneous networks using a language‐based model

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.

Report on the First International Workshop on the Evaluation on Collaborative Information Seeking and Retrieval (ECol'2015)

The workshop on the evaluation of collaborative information retrieval and seeking (ECol) was held in conjunction with the 24th Conference on Information and Knowledge Management (CIKM) in Melbourne, Australia. The workshop featured three main elements. First, a keynote on the main dimensions, challenges, and opportunities in collaborative information retrieval and seeking by Chirag Shah. Second, an oral presentation session in which four papers were presented. Third, a discussion based on three seed research questions: (1) In what ways is collaborative search evaluation more challenging than individual interactive information retrieval (IIIR) evaluation? (2) Would it be possible and/or useful to standardise experimental designs and data for collaborative search evaluation? and (3) For evaluating collaborative search, can we leverage ideas from other tasks such as diversified search, subtopic mining and/or e-discovery? The discussion was intense and raised many points and issues, leading to the proposition that a new evaluation track focused on collaborative information retrieval/seeking tasks, would be worthwhile.

On the Collaboration Support in Information Retrieval

Collaborative Information Retrieval (CIR) is a well-known setting in which explicit collaboration occurs among a group of users working together to solve a shared information need. This type of collaboration has been deemed as beneficial for complex or exploratory search tasks. With the multiplicity of factors impacting on the search effectiveness (e.g., collaborators’ interactions or the individual perception of the shared information need), CIR gives rise to several challenges in terms of collaboration support through algorithmic approaches. More particularly, CIR should allow us to satisfy the shared information need by optimizing the collaboration within the search session over all collaborators, while ensuring that both mutually beneficial goals are reached and that the cognitive cost of the collaboration does not impact the search effectiveness. In this survey, we propose an overview of CIR with a particular focus on the collaboration support through algorithmic approaches. The objective of this article is (a) to organize previous empirical studies analyzing collaborative search with the goal to provide useful design implications for CIR models, (b) to give a picture of the CIR area by distinguishing two main categories of models using the collaboration mediation axis, and (c) to point out potential perspectives in the domain.

Collaborative Information Retrieval: Concepts, Models and Evaluation

Recent work have shown the potential of collaboration for solving complex or exploratory search tasks allowing to achieve synergic effects with respect to individual search, which is the prevalent information retrieval (IR) setting this last decade. This interactive multiuser context gives rise to several challenges in IR. One main challenge relies on the adaptation of IR techniques or models [8] in order to build algo-rithmic supports of collaboration distributing documents among users. The second challenge is related to the design of Collaborative Information Retrieval (CIR) models and their effectiveness evaluation since individual IR frameworks and measures do not totally fit with the collaboration paradigms. In this tutorial, we address the second challenge and present first a general overview of collaborative search introducing the main underlying notions. Then, we focus on related work dealing with collaborative ranking models and their effectiveness evaluation. Our primary objective is to introduce these notions by highlighting how and why they should be different from individual IR in order to give participants the main clues for investigating new research directions in this domain with a deep understanding of current CIR work.

ECol 2015: First international workshop on the Evaluation on Collaborative Information Seeking and Retrieval

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).

Cross-Modal Retrieval in the Cooking Context: Learning Semantic Text-Image Embeddings

Designing powerful tools that support cooking activities has rapidly gained popularity due to the massive amounts of available data, as well as recent advances in machine learning that are capable of analyzing them. In this paper, we propose a cross-modal retrieval model aligning visual and textual data (like pictures of dishes and their recipes) in a shared representation space. We describe an effective learning scheme, capable of tackling large-scale problems, and validate it on the Recipe1M dataset containing nearly 1 million picture-recipe pairs. We show the effectiveness of our approach regarding previous state-of-the-art models and present qualitative results over computational cooking use cases.

DSRIM: A Deep Neural Information Retrieval Model Enhanced by a Knowledge Resource Driven Representation of Documents

The state-of-the-art solutions to the vocabulary mismatch in information retrieval (IR) mainly aim at leveraging either the relational semantics provided by external resources or the distributional semantics, recently investigated by deep neural approaches. Guided by the intuition that the relational semantics might improve the effectiveness of deep neural approaches, we propose the Deep Semantic Resource Inference Model (DSRIM) that relies on: 1) a representation of raw-data that models the relational semantics of text by jointly considering objects and relations expressed in a knowledge resource, and 2) an end-to-end neural architecture that learns the query-document relevance by leveraging the distributional and relational semantics of documents and queries. The experimental evaluation carried out on two TREC datasets from TREC Terabyte and TREC CDS tracks relying respectively on WordNet and MeSH resources, indicates that our model outperforms state-of-the-art semantic and deep neural IR models.

Second International Workshop On the Evaluation of Collaborative Information Seeking and Retrieval (Ecol'17)

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.