Related Event Discovery

Abstract

We consider the problem of discovering local events on the web, where events are entities extracted from webpages. Examples of such local events include small venue concerts, farmers markets, sports activities, etc. Given an event entity, we propose a graph-based framework for retrieving a ranked list of related events that a user is likely to be interested in attending. Due to the difficulty of obtaining ground-truth labels for event entities, which are temporal and are constrained by location, our retrieval framework is unsupervised, and its graph-based formulation addresses (a) the challenge of feature sparseness and noisiness, and (b) the semantic mismatch problem in a self-contained and principled manner. To validate our methods, we collect human annotations and conduct a comprehensive empirical study, analyzing the performance of our methods with regard to relevance, recall, and diversity. This study shows that our graph-based framework is significantly better than any individual feature source, and can be further improved with minimal supervision.

DOI: 10.1145/3018661.3018713

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Cite this paper

@inproceedings{Li2017RelatedED, title={Related Event Discovery}, author={Cheng Li and Michael Bendersky and Vijay Garg and Sujith Ravi}, booktitle={WSDM}, year={2017} }