论文标题
利用FastText进行场地推荐
Utilizing FastText for Venue Recommendation
论文作者
论文摘要
场地推荐系统对用户的过去互动（即签到）进行建模，并推荐场地。传统推荐系统采用协作过滤，基于内容的过滤或矩阵分解。最近，媒介空间嵌入和深度学习算法也用于推荐。在这项工作中，我提出了一种通过使用校验和最近的矢量空间嵌入方法（即FastText）来推荐TOP-K场地的方法。我们提出的方法；形成校验组的组，学习场地的矢量空间表示形式，并利用学习的嵌入来提出场地建议。我使用Foursquare签入数据集测量了所提出的方法的性能。结果表明，所提出的方法的性能优于最先进的方法。
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.
