A Deep Recurrent Collaborative Filtering Framework for Venue Recommendation
A Deep Recurrent Collaborative Filtering Framework for Venue Recommendation
复制标题
DOI：
10.1145/3132847.3133036
复制
发表时间：
2017-11
期刊：
影响因子：
--
通讯作者：
Jarana Manotumruksa;Craig Macdonald;I. Ounis
中科院分区：
文献类型：
--
作者：
Jarana Manotumruksa;Craig Macdonald;I. Ounis
地点推荐是基于位置的社交网络（LBSNs）（例如Yelp）的重要应用，并且近年来得到了广泛的研究。矩阵分解（MF）是一种流行的协同过滤（CF）技术，可以基于类似用户可能访问类似场所的假设向用户建议相关场所。近年来，深度神经网络已成功应用于语音识别、计算机视觉和自然语言处理等任务。基于这一势头，文献中提出了各种推荐方法，通过利用神经网络模型来增强基于MF的方法的有效性，例如：词嵌入以纳入辅助信息（例如评论的文本内容）;以及递归神经网络（RNN）以捕获观察到的用户-场地交互的顺序属性。然而，这样的方法依赖于传统的内积的潜在因素的用户和场所捕捉的概念，协同过滤，这可能不足以捕捉复杂的结构的用户-场所的互动。在本文中，我们提出了一个深度递归协同过滤框架（DRCF）与成对排名功能，旨在通过利用多层感知和递归神经网络架构，从观察到的反馈序列中以CF方式捕获用户-场地交互。我们提出的框架包括两个组成部分：即广义递归矩阵分解（GRMF）和多级递归感知器（MLRP）模型。特别是，GRMF和MLRP学习使用元素和点积以及潜在因素的串联来建模用户-场地交互的复杂结构。此外，我们提出了一种新的基于序列的负采样方法，占观察到的反馈和场地的地理位置的顺序属性，以提高场地建议的质量，以及缓解冷启动用户的问题。在三个大型签到和评级数据集上的实验表明，我们提出的框架优于各种最先进的方法的有效性。
Venue recommendation is an important application for Location-Based Social Networks (LBSNs), such as Yelp, and has been extensively studied in recent years. Matrix Factorisation (MF) is a popular Collaborative Filtering (CF) technique that can suggest relevant venues to users based on an assumption that similar users are likely to visit similar venues. In recent years, deep neural networks have been successfully applied to tasks such as speech recognition, computer vision and natural language processing. Building upon this momentum, various approaches for recommendation have been proposed in the literature to enhance the effectiveness of MF-based approaches by exploiting neural network models such as: word embeddings to incorporate auxiliary information (e.g. textual content of comments); and Recurrent Neural Networks (RNN) to capture sequential properties of observed user-venue interactions. However, such approaches rely on the traditional inner product of the latent factors of users and venues to capture the concept of collaborative filtering, which may not be sufficient to capture the complex structure of user-venue interactions. In this paper, we propose a Deep Recurrent Collaborative Filtering framework (DRCF) with a pairwise ranking function that aims to capture user-venue interactions in a CF manner from sequences of observed feedback by leveraging Multi-Layer Perception and Recurrent Neural Network architectures. Our proposed framework consists of two components: namely Generalised Recurrent Matrix Factorisation (GRMF) and Multi-Level Recurrent Perceptron (MLRP) models. In particular, GRMF and MLRP learn to model complex structures of user-venue interactions using element-wise and dot products as well as the concatenation of latent factors. In addition, we propose a novel sequence-based negative sampling approach that accounts for the sequential properties of observed feedback and geographical location of venues to enhance the quality of venue suggestions, as well as alleviate the cold-start users problem. Experiments on three large checkin and rating datasets show the effectiveness of our proposed framework by outperforming various state-of-the-art approaches.