Third International Workshop on Gamification for Information Retrieval (GamifIR'16)

Stronger engagement and greater participation is often crucial to reach a goal or to solve an issue. Issues like the emerging employee engagement crisis, insufficient knowledge sharing, and chronic procrastination. In many cases we need and search for tools to beat procrastination or to change people’s habits. Gamification is the approach to learn from often fun, creative and engaging games. In principle, it is about understanding games and applying game design elements in a non-gaming environments. This offers possibilities for wide area improvements. For example more accurate work, better retention rates and more cost effective solutions by relating motivations for participating as more intrinsic than conventional methods. In the context of Information Retrieval (IR) it is not hard to imagine that many tasks could benefit from gamification techniques. Besides several manual annotation tasks of data sets for IR research, user participation is important in order to gather implicit or even explicit feedback to feed the algorithms. Gamification, however, comes with its own challenges and its adoption in IR is still in its infancy. Given the enormous response to the first and second GamifIR workshops that were both co-located with ECIR, and the broad range of topics discussed, we now organized the third workshop at SIGIR 2016 to address a range of emerging challenges and opportunities

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

Benchmarking news recommendations: the CLEF NewsREEL use case

The CLEF NewsREEL challenge is a campaign-style evaluation lab allowing participants to evaluate and optimize news recommender algorithms. The goal is to create an algorithm that is able to generate news items that users would click, respecting a strict time constraint. The lab challenges participants to compete in either a "living lab" (Task 1) or perform an evaluation that replays recorded streams (Task 2). In this report, we discuss the objectives and challenges of the NewsREEL lab, summarize last year's campaign and outline the main research challenges that can be addressed by participating in NewsREEL 2016

How can heat maps of indexing vocabularies be utilized for information seeking purposes?

The ability to browse an information space in a structured way by exploiting similarities and dissimilarities between information objects is crucial for knowledge discovery. Knowledge maps use visualizations to gain insights into the structure of large-scale information spaces, but are still far away from being applicable for searching. The paper proposes a use case for enhancing search term recommendations by heat map visualizations of co-word relation-ships taken from indexing vocabulary. By contrasting areas of different "heat" the user is enabled to indicate mainstream areas of the field in question more easily.Comment: URL workshop proceedings: http://ceur-ws.org/Vol-1311

Third International Workshop on Gamification

Stronger engagement and greater participation is often crucial to reach a goal or to solve an issue. Issues like the emerging employee engagement crisis, insufficient knowledge sharing, and chronic procrastination. In many cases we need and search for tools to beat procrastination or to change people’s habits. Gamification is the approach to learn from often fun, creative and engaging games. In principle, it is about understanding games and applying game design elements in a non-gaming environments. This offers possibilities for wide area improvements. For example more accurate work, better retention rates and more cost effective solutions by relating motivations for participating as more intrinsic than conventional methods. In the context of Information Retrieval (IR) it is not hard to imagine that many tasks could benefit from gamification techniques. Besides several manual annotation tasks of data sets for IR research, user participation is important in order to gather implicit or even explicit feedback to feed the algorithms. Gamification, however, comes with its own challenges and its adoption in IR is still in its infancy. Given the enormous response to the first and second GamifIR workshops that were both co-located with ECIR, and the broad range of topics discussed, we now organized the third workshop at SIGIR 2016 to address a range of emerging challenges and opportunities

Evaluation Measures for Relevance and Credibility in Ranked Lists

Recent discussions on alternative facts, fake news, and post truth politics have motivated research on creating technologies that allow people not only to access information, but also to assess the credibility of the information presented to them by information retrieval systems. Whereas technology is in place for filtering information according to relevance and/or credibility, no single measure currently exists for evaluating the accuracy or precision (and more generally effectiveness) of both the relevance and the credibility of retrieved results. One obvious way of doing so is to measure relevance and credibility effectiveness separately, and then consolidate the two measures into one. There at least two problems with such an approach: (I) it is not certain that the same criteria are applied to the evaluation of both relevance and credibility (and applying different criteria introduces bias to the evaluation); (II) many more and richer measures exist for assessing relevance effectiveness than for assessing credibility effectiveness (hence risking further bias). Motivated by the above, we present two novel types of evaluation measures that are designed to measure the effectiveness of both relevance and credibility in ranked lists of retrieval results. Experimental evaluation on a small human-annotated dataset (that we make freely available to the research community) shows that our measures are expressive and intuitive in their interpretation

Improving Contextual Suggestions using Open Web Domain Knowledge

Also published online by CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073)Contextual suggestion aims at recommending items to users given their current context, such as location-based tourist recommendations. Our contextual suggestion ranking model consists of two main components: selecting candidate suggestions and providing a ranked list of personalized suggestions. We focus on selecting appropriate suggestions from the ClueWeb12 collection using tourist domain knowledge inferred from social sites and resources available on the public Web (Open Web). Specifically, we generate two candidate subsets retrieved from the ClueWeb12 collection, one by filtering the content on mentions of the location context, and one by integrating domain knowledge derived from the OpenWeb. The impact of these candidate selection methods on contextual suggestion effectiveness is analyzed using the test collection constructed for the TREC Contextual Suggestion Track in 2014. Our main findings are that contextual suggestion performance on the subset created using OpenWeb domain knowledge is significantly better than using only geographical information. Second, using a prior probability estimated from domain knowledge leads to better suggestions and improves the performance

Event Detection in Wikipedia Edit History Improved by Documents Web Based Automatic Assessment

A majority of current work in events extraction assumes the static nature of relationships in constant expertise knowledge bases. However, in collaborative environments, such as Wikipedia, information and systems are extraordinarily dynamic over time. In this work, we introduce a new approach for extracting complex structures of events from Wikipedia. We advocate a new model to represent events by engaging more than one entities that are generalizable to an arbitrary language. The evolution of an event is captured successfully primarily based on analyzing the user edits records in Wikipedia. Our work presents a basis for a singular class of evolution-aware entity-primarily based enrichment algorithms and will extensively increase the quality of entity accessibility and temporal retrieval for Wikipedia. We formalize this problem case and conduct comprehensive experiments on a real dataset of 1.8 million Wikipedia articles in order to show the effectiveness of our proposed answer. Furthermore, we suggest a new event validation automatic method relying on a supervised model to predict the presence of events in a non-annotated corpus. As the extra document source for event validation, we chose the Web due to its ease of accessibility and wide event coverage. Our outcomes display that we are capable of acquiring 70% precision evaluated on a manually annotated corpus. Ultimately, we conduct a comparison of our strategy versus the Current Event Portal of Wikipedia and discover that our proposed WikipEvent along with the usage of Co-References technique may be utilized to provide new and more data on events