Utilizing FastText for Venue Recommendation
This work addresses venue recommendation for users, but it is incremental as it applies an existing embedding method to a known problem.
The authors tackled venue recommendation by leveraging the sequentiality of check-ins and FastText embeddings, achieving better performance than state-of-the-art methods on a Foursquare dataset.
Venue recommendation systems model the past interactions (i.e., check-ins) of the users and recommend venues. Traditional recommendation systems employ collaborative filtering, content-based filtering or matrix factorization. Recently, vector space embedding and deep learning algorithms are also used for recommendation. In this work, I propose a method for recommending top-k venues by utilizing the sequentiality feature of check-ins and a recent vector space embedding method, namely the FastText. Our proposed method; forms groups of check-ins, learns the vector space representations of the venues and utilizes the learned embeddings to make venue recommendations. I measure the performance of the proposed method using a Foursquare check-in dataset.The results show that the proposed method performs better than the state-of-the-art methods.