@inproceedings{trec:drosatos:2013,
abstract = {In this report we give an overview of our participation in the TREC 2013 Contextual Suggestion Track. We present an approach for context processing that comprises a carefully designed and fine-tuned POI (Point Of Interest) data collection technique, a crowdsourcing approach to enrich our data collection and two radically different approaches for suggestion processing (a k-NN classification-based and a Rocchio-like). In the context processing, we collect POIs from three popular place search engines, Google Places, Foursquare and Yelp. The collected POIs are enriched by adding snippets from the Google and Bing search engines using crowdsourcing techniques. In the suggestion processing, we propose two methods. The first submits each candidate place as a query to an index of rated examples and scores it based on the top-$k$ user's ratings. The second method is based on Rocchio's algorithm and uses the rated examples per profile to generate a personal query which is then submitted to an index of places. The track evaluation shows that both approaches are working well; especially the Rocchio-like approach looks very promising since it scores almost firmly above the median and achieves the best results in almost half of the judged context-profile pairs.},
author = {George Drosatos and Giorgos Stamatelatos and Avi Arampatzis and Pavlos S. Efraimidis},
booktitle = {22nd Text REtrieval Conference (TREC 2013)},
title = {DUTH at TREC 2013 Contextual Suggestion Track},
year = {2013},
month = {November},
location = {Gaithersburg, Maryland USA},
numpages = {10}
}