2020/05/14 by Makbule Gülçin Özsoy, Ozsoy, Makbule Gulcin
Computer Science · #Data Management and Algorithms #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Recommender Systems and Techniques #Web Data Mining and Analysis
paper · pdf · doi:10.48550/arxiv.2005.12982
openalex publication_date 2020/05/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.