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Asking Clarifying Questions in Open-Domain Information-Seeking Conversations

This paper formulate the task of asking clarifying questions in open-domain information-seeking conversational systems, propose an offline evaluation methodology for the task, and collect a dataset, called Qulac, through crowdsourcing, which significantly outperforms competitive baselines.
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ANTIQUE: A Non-factoid Question Answering Benchmark

This paper develops and releases a collection of 2,626 open-domain non-factoid questions from a diverse set of categories, and includes a brief analysis of the data as well as baseline results on both classical and neural IR models.
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Personalized Context-Aware Point of Interest Recommendation

This article proposes a probabilistic model to find the mapping between user-annotated tags and locations’ taste keywords and investigates four approaches to use the proposed mapping for addressing the data sparsity problem.
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Improved churn prediction in telecommunication industry using data mining techniques

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ConvAI3: Generating Clarifying Questions for Open-Domain Dialogue Systems (ClariQ)

This document presents a detailed description of the challenge on clarifying questions for dialogue systems (ClariQ) and provides a common evaluation framework to evaluate mixed-initiative conversations.
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Venue Appropriateness Prediction for Personalized Context-Aware Venue Suggestion

A set of novel scores to measure the similarity between a user and a candidate venue in a new city using contextually appropriate places based on user's history of preferences in other cities as well as user's context are presented.
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LGLMF: Local Geographical based Logistic Matrix Factorization Model for POI Recommendation

An effective geographical model is proposed by considering the user's main region of activity and the relevance of each location within that region and is fused into the Logistic Matrix Factorization to improve the accuracy of POI recommendation.
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A Cross-Platform Collection for Contextual Suggestion

Both collections that were used by the TREC 2016 Contextual Suggestion track are released to give other researchers the opportunity to compare their approaches with the top systems in the track, and it provides the opportunities to explore different methods to predicting contextually appropriate venues.
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User Model Enrichment for Venue Recommendation

This paper introduces a user model based on venues’ categories and their descriptive keywords extracted from Foursquare tips, and proposes an enriched user model which leverages the users’ reviews from Yelp.
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Target Apps Selection: Towards a Unified Search Framework for Mobile Devices

This paper introduces and study the task of target apps selection, which has various potential real-world applications and proposes two simple yet effective neural models that significantly outperform the baselines.
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