Recommendation
By Mohammad Aliannejadi, I. Mele, Fabio A. Crestani. 2020
. An important task in recommender systems is suggesting relevant venues in a city to a user. These suggestions are usually created by exploiting the user’s history of preferences, which are, for example, collected in previously visited cities. In this paper, we first introduce a user model based on venues’ categories and their descriptive keywords extracted from Foursquare tips. Then, we propose an enriched user model which leverages the users’ reviews from Yelp. Our participation in the TREC 2015 Contextual Suggestion track, confirmed that our model out-performs other approaches by a significant margin.
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Jan Skoglund
- Alejandro Luebs
- Michael Chinen
- Felicia S. C. Lim
- W. Kleijn
- Andrew Storus
- Meng Liu
- Tom Denton
Citations in the corpus
- Generative Speech Coding with Predictive Variance Regularization (2021, methodology): “Except for data collection, we followed the ITU-T P.800 [29] (ACR) recommendation.”
- KGAT: Knowledge Graph Attention Network for Recommendation (2019, background): “Despite its effectiveness and universality, CF methods suffer from the inability of modeling side information [30, 31], such as item attributes, user profiles, and contexts, thus perform poorly in sparse situations where users and items have few interactions.”