Recommending venues or points-of-interest (POIs) is a hot topic in recent years, especially for tourism applications
and mobile users. We propose and evaluate several suggestion methods, taking an effectiveness, feasibility,
efficiency, and privacy perspective. The task is addressed by two content-based methods (a Weighted
kNN classifier and a Rated Rocchio personalized query), Collaborative Filtering methods, as well as several
(rank-based or rating-based) methods of merging results of different systems. Effectiveness is evaluated
on two standard benchmark datasets, provided and used by TREC’s Contextual Suggestion Tracks in
2015 and 2016. First, we enrich these datasets with more information on venues, collected from web services
like Foursquare and Yelp; we make this extra data available for future experimentation. Then, we
find that the content-based methods provide state-of-the-art effectiveness, the collaborative filtering variants
mostly suffer from data sparsity problems in the current datasets, and the merging methods further
improve results by mainly promoting the first relevant suggestion. Concerning mobile feasibility, efficiency,
and user privacy, the content-based methods, especially Rated Rocchio, are the best. Collaborative filtering
has the worst efficiency and privacy leaks. Our findings can be very useful for developing effective
and efficient operational systems, respecting user privacy. Last, our experiments indicate that better benchmark
datasets would be welcome, and the use of additional evaluation measures—more sensitive in recall—is
recommended
AVI ARAMPATZIS and GEORGIOS KALAMATIANOS, Democritus University of Thrace
ACM Transactions on Information Systems, Vol. 36, No. 3, Article 23. Publication date: September 2017.
https://doi.org/10.1145/3125620
Most New paper(published by Sep. 2017 TOIS)
Abstract(direct sentence from ACM)
Recommending venues or points-of-interest (POIs) is a hot topic in recent years, especially for tourism applications and mobile users. We propose and evaluate several suggestion methods, taking an effectiveness, feasibility, efficiency, and privacy perspective. The task is addressed by two content-based methods (a Weighted kNN classifier and a Rated Rocchio personalized query), Collaborative Filtering methods, as well as several (rank-based or rating-based) methods of merging results of different systems. Effectiveness is evaluated on two standard benchmark datasets, provided and used by TREC’s Contextual Suggestion Tracks in 2015 and 2016. First, we enrich these datasets with more information on venues, collected from web services like Foursquare and Yelp; we make this extra data available for future experimentation. Then, we find that the content-based methods provide state-of-the-art effectiveness, the collaborative filtering variants mostly suffer from data sparsity problems in the current datasets, and the merging methods further improve results by mainly promoting the first relevant suggestion. Concerning mobile feasibility, efficiency, and user privacy, the content-based methods, especially Rated Rocchio, are the best. Collaborative filtering has the worst efficiency and privacy leaks. Our findings can be very useful for developing effective and efficient operational systems, respecting user privacy. Last, our experiments indicate that better benchmark datasets would be welcome, and the use of additional evaluation measures—more sensitive in recall—is recommended
AVI ARAMPATZIS and GEORGIOS KALAMATIANOS, Democritus University of Thrace ACM Transactions on Information Systems, Vol. 36, No. 3, Article 23. Publication date: September 2017. https://doi.org/10.1145/3125620