Utilizing FastText for Venue Recommendation
Makbule Gulcin Ozsoy

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.
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Taxonomy
TopicsRecommender Systems and Techniques · Data Management and Algorithms · Web Data Mining and Analysis
