Search

Clarification is increasingly becoming a vital factor in various topics of information retrieval, such as conversational search and modern Web search engines. Prompting the user for clarification in a search session can be very beneficial to the system as the user’s explicit feedback helps the system improve retrieval massively. However, it comes with a very high risk of frustrating the user in case the system fails in asking decent clarifying questions. Therefore, it is of great importance to determine when and how to ask for clarification.To this aim, in this work, we model search clarification prediction as user engagement problem. We assume that the better a clarification is, the higher user engagement with it would be. We propose a Transformer-based model to tackle the task. The comparison with competitive baselines on large-scale real-life clarification engagement data proves the effectiveness of our model. Also, we analyse the effect of all result page elements on the performance and find that, among others, the ranked list of the search engine leads to considerable improvements. Our extensive analysis of task-specific features guides future research.

With the popularity of Location-based Social Networks, Point-of-Interest (POI) recommendation has become an important task, which learns the users’ preferences and mobility patterns to recommend POIs. Previous studies show that incorporating contextual information such as geographical and temporal influences is necessary to improve POI recommendation by addressing the data sparsity problem. However, existing methods model the geographical influence based on the physical distance between POIs and users, while ignoring the temporal characteristics of such geographical influences. In this paper, we perform a study on the user mobility patterns where we find out that users’ check-ins happen around several centers depending on their current temporal state. Next, we propose a spatio-temporal activity-centers algorithm to model users’ behavior more accurately. Finally, we demonstrate the effectiveness of our proposed contextual model by incorporating it into the matrix factorization model under two different settings: (i) static and (ii) temporal. To show the effectiveness of our proposed method, which we refer to as STACP, we conduct experiments on two well-known real-world datasets acquired from Gowalla and Foursquare LBSNs. Experimental results show that the STACP model achieves a statistically significant performance improvement, compared to the state-of-the-art techniques. Also, we demonstrate the effectiveness of capturing geographical and temporal information for modeling users’ activity centers and the importance of modeling them jointly.

Mental disorders are a major concern in societies all over the world, and in spite of the improved diagnosis rates of such disorders in recent years, many cases still go undetected. Nowadays, many people are increasingly utilising online social media platforms to share their feelings and moods. Despite the collective efforts in the community to develop models for identifying potential cases of mental disorder, not much work has been done to provide insights that could be used by a predictive system or a health practitioner in the elaboration of a diagnosis.In this paper, we present our research towards better visualising and understanding the factors that characterise and differentiate social media users who are affected by mental disorders from those who are not. Furthermore, we study to which extent various mental disorders, such as depression and anorexia, differ in terms of language use. We conduct different experiments considering various dimensions of language such as vocabulary, psychometric attributes and emotional indicators. Our findings reveal that positive instances of mental disorder show significant differences from control individuals in the way they write and express emotions in social media. However, there are not quantifiable differences that could be used to distinguish one mental disorder from each other.

With an increasing amount of information on globally important events, there is a growing demand for efficient analytics of multilingual event-centric information. Such analytics is particularly challenging due to the large amount of content, the event dynamics and the language barrier. Although memory institutions increasingly collect event-centric Web content in different languages, very little is known about the strategies of researchers who conduct analytics of such content. In this paper we present researchers’ strategies for the content, method and feature selection in the context of cross-lingual event-centric analytics observed in two case studies on multilingual Wikipedia. We discuss the influence factors for these strategies, the findings enabled by the adopted methods along with the current limitations and provide recommendations for services supporting researchers in cross-lingual event-centric analytics.

In this paper, we provide an overview of the seventh annual edition of the CLEF eHealth evaluation lab. CLEF eHealth 2019 continues our evaluation resource building efforts around the easing and support of patients, their next-of-kins, clinical staff, and health scientists in understanding, accessing, and authoring electronic health information in a multilingual setting. This year’s lab advertised three tasks: Task 1 on indexing non-technical summaries of German animal experiments with International Classification of Diseases, Version 10 codes; Task 2 on technology assisted reviews in empirical medicine building on 2017 and 2018 tasks in English; and Task 3 on consumer health search in mono- and multilingual settings that builds on the 2013–18 Information Retrieval tasks. In total nine teams took part in these tasks (six in Task 1 and three in Task 2). Herein, we describe the resources created for these tasks and evaluation methodology adopted. We also provide a brief summary of participants of this year’s challenges and results obtained. As in previous years, the organizers have made data and tools associated with the lab tasks available for future research and development.

In this study we investigate the recommendation problem with multifaceted contextual information to overcome the scarcity of users' check-in data in Location-based Social Networks. To generate accurate personalized Point-of-Interest (POI) recommendations in the presence of data scarcity, we account for both users' and POIs' contextual information such as the social influence of friends, as well as the geographical and sequential transition influence of POIs on user's check-in behavior. We first propose a multi-view learning strategy to capture the multifaceted contextual information of users and POIs along with users' check-in data. Then, we feed the learned user and POI latent vectors to a deep neural framework, to capture their non-linear correlations. Finally, we formulate the objective function of our geo-based deep collaborative filtering model (GeoDCF) as a Bayesian personalized ranking problem to focus on the top-k recommendation task and we learn the parameters of our model via backpropagation. Our experiments on real-world datasets confirm that GeoDCF achieves high recommendation accuracy, significantly outperforming other state-of-the-art methods. Furthermore, we confirm the influence of both users' and POIs' contextual information on our GeoDCF model. The evaluation datasets are publicly available at: http://snap.stanford.edu/data/loc-gowalla.html, https://sites.google.com/site/yangdingqi/home/foursquare-dataset.

Recent developments allow for sentiment analysis on multimodal social media content. In this paper we analyse content posted on microblogging and content-sharing platforms to estimate sentiment of the city’s neighbourhoods. The results of sentiment analysis are evaluated through investigation into the existence of relationships with the indicators of city liveability, collected by the local government. Additionally, we create a set of sentiment maps that may help discover existence of possible sentiment patterns within the city. This study shows several important findings. First, utilizing multimedia data, i.e., both visual and text content leads to more reliable sentiment scores. The microblogging platform Twitter further appears more suitable for sentiment analysis than the content-sharing website Flickr. However, in case of both platforms, the computed multimodal sentiment scores show significant relationships with the indicators of city liveability.

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This volume contains the papers presented at IIR'17: 8th Italian Information Retrieval Workshop held on June 05-07, 2017 in Lugano, Switzerland. The purpose of the Italian Information Retrieval (IIR) workshop series is to provide a forum for stimulating and disseminating research in information retrieval, where Italian researchers (especially young ones) and researchers a liated with Italian institutions can network and discuss their research results in an informal way. Previously IIR workshops took place in Venice (2016), Cagliari (2015), Rome (2014), Pisa (2013), Bari (2012), Milan (2011) and Padua (2010).

Online learning to rank methods aim to optimize ranking models based on user interactions. The dueling bandit gradient descent (DBGD) algorithm is able to effectively optimize linear ranking models solely from user interactions. We propose an extension of DBGD, called probabilistic multileave gradient descent (P-MGD) that builds on probabilistic multileave, a recently proposed highly sensitive and unbiased online evaluation method. We demonstrate that P-MGD significantly outperforms state-of-the-art online learning to rank methods in terms of online performance, without sacrificing offline performance and at greater learning speed.

The potential of mining tourist information from social multimedia data gives rise to new applications offering much richer impressions of the city. In this paper we propose Scenemash, a system that generates multimodal summaries of multiple alternative routes between locations in a city. To get insight into the geographic areas on the route, we collect a dataset of community-contributed images and their associated annotations from Foursquare and Flickr. We identify images and terms representative of a geographic area by jointly analysing distributions of a large number of semantic concepts detected in the visual content and latent topics extracted from associated text. Scenemash prototype is implemented as an Android app for smartphones and smartwatches.

Which queries stand to gain or loose from diversifying their results?Some queries are more difficult than others for diversification. Across a numberof conceptually different diversification methods, performance on such queriestends to deteriorate after applying these diversification methods, even though theirinitial performance in terms of relevance or diversity tends to be good.