Recommending Points-of-Interest via Weighted kNN, Rated Rocchio, and Borda Count Fusion

We present the work of the Democritus University of Thrace (DUTH) team in TREC’s 2016 Contextual Suggestion Track. The goal of the Contextual Suggestion Track is to build a system capable of proposing venues which a user might be interested to visit, using any available contextual and personal information. First, we enrich the TREC-provided dataset by collecting more information on venues from web-services like Foursquare and Yelp. Then, we address the task with two different content-based methods, namely, a Weighted kNN classifier and a Rated Rocchio personalized query. Last, we also explore the use of a voting system, namely Borda Count, as a means of fusing the results of several suggestion systems. Our runs provided very good results, achieving top or near-top TREC performance.