University of Waterloo at TREC 2014 Contextual Suggestion: Experiments with Suggestion Clustering.

摘要

In this work we present our group's first attempt at developing a system to solve the problem presented in the contextual suggestion task. As part of TREC 2014 the contextual suggestion track is running for the third time. The goal of this task is to tailor point-of-interest suggestions to users according to this preferences. Here we present how we gathered candidate points-of-interest, grouped them according to similarity using clustering, and picked points-of-interest that each user would nd especially appealing. The organizers of this track distributed users' personal pro les in three les: examples2014.csv, pro les2014-70.csv and pro les2014-100.csv. A list of 100 example points-of-interest, which each consist of an ID, a title, a description and a URL were included in examples2014.csv. 299 users indicated their preferences by giving a rating on a 5-point score (0, 1, 2, 3, 4) to the description and website of each example point-of-interest. 116 users, indicated preferences to all the 100 example points of interests, these pro les are distributed in pro les2014-100.csv. The other 183 users, only indicated 70% of all the example points of interest, these pro les are distributed in pro les2014- 70.csv. There are 50 contexts which each represent a city in the United States which are listed in contexts2014.txt.