A Study on Agreement in PICO Span Annotations

In evidence-based medicine, relevance of medical literature is determined by predefined relevance conditions. The conditions are defined based on PICO elements, namely, Patient, Intervention, Comparator, and Outcome. Hence, PICO annotations in medical literature are essential for automatic relevant document filtering. However, defining boundaries of text spans for PICO elements is not straightforward. In this paper, we study the agreement of PICO annotations made by multiple human annotators, including both experts and non-experts. Agreements are estimated by a standard span agreement (i.e., matching both labels and boundaries of text spans), and two types of relaxed span agreement (i.e., matching labels without guaranteeing matching boundaries of spans). Based on the analysis, we report two observations: (i) Boundaries of PICO span annotations by individual human annotators are very diverse. (ii) Despite the disagreement in span boundaries, general areas of the span annotations are broadly agreed by annotators. Our results suggest that applying a standard agreement alone may undermine the agreement of PICO spans, and adopting both a standard and a relaxed agreements is more suitable for PICO span evaluation.Comment: Accepted in SIGIR 2019 (Short paper

A Survey of Location Prediction on Twitter

Locations, e.g., countries, states, cities, and point-of-interests, are central to news, emergency events, and people's daily lives. Automatic identification of locations associated with or mentioned in documents has been explored for decades. As one of the most popular online social network platforms, Twitter has attracted a large number of users who send millions of tweets on daily basis. Due to the world-wide coverage of its users and real-time freshness of tweets, location prediction on Twitter has gained significant attention in recent years. Research efforts are spent on dealing with new challenges and opportunities brought by the noisy, short, and context-rich nature of tweets. In this survey, we aim at offering an overall picture of location prediction on Twitter. Specifically, we concentrate on the prediction of user home locations, tweet locations, and mentioned locations. We first define the three tasks and review the evaluation metrics. By summarizing Twitter network, tweet content, and tweet context as potential inputs, we then structurally highlight how the problems depend on these inputs. Each dependency is illustrated by a comprehensive review of the corresponding strategies adopted in state-of-the-art approaches. In addition, we also briefly review two related problems, i.e., semantic location prediction and point-of-interest recommendation. Finally, we list future research directions.Comment: Accepted to TKDE. 30 pages, 1 figur

When is Eaton's Markov chain irreducible?

Consider a parametric statistical model $P(\mathrm{d}x|\theta)$ and an improper prior distribution $\nu(\mathrm{d}\theta)$ that together yield a (proper) formal posterior distribution $Q(\mathrm{d}\theta|x)$. The prior is called strongly admissible if the generalized Bayes estimator of every bounded function of $\theta$ is admissible under squared error loss. Eaton [Ann. Statist. 20 (1992) 1147--1179] has shown that a sufficient condition for strong admissibility of $\nu$ is the local recurrence of the Markov chain whose transition function is $R(\theta,\mathrm{d}\eta)=\int Q(\mathrm{d}\eta|x)P(\mathrm {d}x|\theta)$. Applications of this result and its extensions are often greatly simplified when the Markov chain associated with $R$ is irreducible. However, establishing irreducibility can be difficult. In this paper, we provide a characterization of irreducibility for general state space Markov chains and use this characterization to develop an easily checked, necessary and sufficient condition for irreducibility of Eaton's Markov chain. All that is required to check this condition is a simple examination of $P$ and $\nu$. Application of the main result is illustrated using two examples.Comment: Published at http://dx.doi.org/10.3150/07-BEJ6191 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm

Deep Learning based Recommender System: A Survey and New Perspectives

With the ever-growing volume of online information, recommender systems have been an effective strategy to overcome such information overload. The utility of recommender systems cannot be overstated, given its widespread adoption in many web applications, along with its potential impact to ameliorate many problems related to over-choice. In recent years, deep learning has garnered considerable interest in many research fields such as computer vision and natural language processing, owing not only to stellar performance but also the attractive property of learning feature representations from scratch. The influence of deep learning is also pervasive, recently demonstrating its effectiveness when applied to information retrieval and recommender systems research. Evidently, the field of deep learning in recommender system is flourishing. This article aims to provide a comprehensive review of recent research efforts on deep learning based recommender systems. More concretely, we provide and devise a taxonomy of deep learning based recommendation models, along with providing a comprehensive summary of the state-of-the-art. Finally, we expand on current trends and provide new perspectives pertaining to this new exciting development of the field.Comment: The paper has been accepted by ACM Computing Surveys. https://doi.acm.org/10.1145/328502

From Counter-intuitive Observations to a Fresh Look at Recommender System

Recently, a few papers report counter-intuitive observations made from experiments on recommender system (RecSys). One observation is that users who spend more time and users who have many interactions with a recommendation system receive poorer recommendations. Another observation is that models trained by using only the more recent parts of a dataset show significant performance improvement. In this opinion paper, we interpret these counter-intuitive observations from two perspectives. First, the observations are made with respect to the global timeline of user-item interactions. Second, the observations are considered counter-intuitive because they contradict our expectation on a recommender: the more interactions a user has, the higher chance that the recommender better learns the user preference. For the first perspective, we discuss the importance of the global timeline by using the simplest baseline Popularity as a starting point. We answer two questions: (i) why the simplest model popularity is often ill-defined in academic research? and (ii) why the popularity baseline is evaluated in this way? The questions lead to a detailed discussion on the data leakage issue in many offline evaluations. As the result, model accuracies reported in many academic papers are less meaningful and incomparable. For the second perspective, we try to answer two more questions: (i) why models trained by using only the more recent parts of data demonstrate better performance? and (ii) why more interactions from users lead to poorer recommendations? The key to both questions is user preference modeling. We then propose to have a fresh look at RecSys. We discuss how to conduct more practical offline evaluations and possible ways to effectively model user preferences. The discussion and opinions in this paper are on top-N recommendation only, not on rating prediction.Comment: 11 pages, 5 figure