Collaborative Information Retrieval: Concepts, Models and Evaluation

International audienceRecent 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

Report on the Information Retrieval Festival (IRFest2017)

The Information Retrieval Festival took place in April 2017 in Glasgow. The focus of the workshop was to bring together IR researchers from the various Scottish universities and beyond in order to facilitate more awareness, increased interaction and reflection on the status of the field and its future. The program included an industry session, research talks, demos and posters as well as two keynotes. The first keynote was delivered by Prof. Jaana Kekalenien, who provided a historical, critical reflection of realism in Interactive Information Retrieval Experimentation, while the second keynote was delivered by Prof. Maarten de Rijke, who argued for more Artificial Intelligence usage in IR solutions and deployments. The workshop was followed by a "Tour de Scotland" where delegates were taken from Glasgow to Aberdeen for the European Conference in Information Retrieval (ECIR 2017

Towards quantifying the impact of non-uniform information access in collaborative information retrieval

The majority of research into Collaborative Information Retrieval (CIR) has assumed a uniformity of information access and visibility between collaborators. However in a number of real world scenarios, information access is not uniform between all collaborators in a team e.g. security, health etc. This can be referred to as Multi-Level Collaborative Information Retrieval (MLCIR). To the best of our knowledge, there has not yet been any systematic investigation of the effect of MLCIR on search outcomes. To address this shortcoming, in this paper, we present the results of a simulated evaluation conducted over 4 different non-uniform information access scenarios and 3 different collaborative search strategies. Results indicate that there is some tolerance to removing access to the collection and that there may not always be a negative impact on performance. We also highlight how different access scenarios and search strategies impact on search outcomes

CHESTNUT: Improve serendipity in movie recommendation by an Information Theory-based collaborative filtering approach

The term serendipity has been understood narrowly in the Recommender System. Applying a user-centered approach, user-friendly serendipitous recommender systems are expected to be developed based on a good understanding of serendipity. In this paper, we introduce CHESTNUT , a memory-based movie collaborative filtering system to improve serendipity performance. Relying on a proposed Information Theory-based algorithm and previous study, we demonstrate a method of successfully injecting insight, unexpectedness and usefulness, which are key metrics for a more comprehensive understanding of serendipity, into a practical serendipitous runtime system. With lightweight experiments, we have revealed a few runtime issues and further optimized the same. We have evaluated CHESTNUT in both practicability and effectiveness , and the results show that it is fast, scalable and improves serendip-ity performance significantly, compared with mainstream memory-based collaborative filtering. The source codes of CHESTNUT are online at https://github.com/unnc-idl-ucc/CHESTNUT/