Utilizing FastText for Venue Recommendation

TL;DR

This paper introduces a venue recommendation approach that leverages FastText embeddings and check-in sequences to improve recommendation accuracy over existing methods.

Contribution

It presents a novel venue recommendation method using FastText embeddings and check-in sequence modeling, outperforming state-of-the-art techniques.

Findings

Proposed method outperforms existing recommendation algorithms.

Utilizes sequential check-in data for improved accuracy.

FastText embeddings effectively capture venue relationships.

Abstract

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.