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Efficient Retrieval of the Top-k Most Relevant Spatial Web Objects

A new indexing framework for location-aware top-k text retrieval that encompasses algorithms that utilize the proposed indexes for computing the top- k query, thus taking into account both text relevancy and location proximity to prune the search space.
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Time-aware point-of-interest recommendation

This paper defines a new problem, namely, the time-aware POI recommendation, to recommend POIs for a given user at a specified time in a day, and develops a collaborative recommendation model that is able to incorporate temporal information.
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Personalized Ranking Metric Embedding for Next New POI Recommendation

This paper proposes a personalized ranking metric embedding method (PRME) to model personalized check-in sequences and develops a PRME-G model, which integrates sequential information, individual preference, and geographical influence, to improve the recommendation performance.
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Global Context Enhanced Graph Neural Networks for Session-based Recommendation

A novel approach to exploit item transitions over all sessions in a more subtle manner for better inferring the user preference of the current session, called GCE-GNN, which outperforms the state-of-the-art methods consistently.
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Improving Data Quality: Consistency and Accuracy

This paper proposes two algorithms: one for automatically computing a repair D' that satisfies a given set of CFDs, and the other for incrementally finding a repair in response to updates to a clean database.
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Rank-GeoFM: A Ranking based Geographical Factorization Method for Point of Interest Recommendation

A ranking based geographical factorization method, called Rank-GeoFM, for POI recommendation, which addresses the two challenges of scarcity of check-in data and context information, and outperforms the state-of-the-art methods significantly in terms of recommendation accuracy.
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Collective spatial keyword querying

This paper defines the problem of retrieving a group of spatial web objects such that the group's keywords cover the query's keywords and such that objects are nearest to the query location and have the lowest inter-object distances and designs exact and approximate solutions with provable approximation bounds to the problems.
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Finding question-answer pairs from online forums

This paper proposes a sequential patterns based classification method to detect questions in a forum thread, and a graph based propagation method to detects answers for questions in the same thread.
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COM: a generative model for group recommendation

A probabilistic model named COM (COnsensus Model) is proposed to model the generative process of group activities, and make group recommendations, and is able to incorporate both users' selection history and personal considerations of content factors.
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An Experimental Evaluation of Point-of-interest Recommendation in Location-based Social Networks

An all-around evaluation of 12 state-of-the-art POI recommendation models to provide readers with an overall picture of the cutting-edge research onPOI recommendation and obtain several important findings.
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