A Convolutional Neural Network and Matrix Factorization-Based Travel Location Recommendation Method Using Community-Contributed Geotagged Photos

Thaair Ameen,Dandan Lyu,Ling Chen,Zhenxing Xu,Hongyu Shi

doi:10.3390/ijgi9080464

Abstract

Travel location recommendation methods using community-contributed geotagged photos are based on past check-ins. Therefore, these methods cannot effectively work for new travel locations, i.e., they suffer from the travel location cold start problem. In this study, we propose a convolutional neural network and matrix factorization-based travel location recommendation method to address the problem. Specifically, a weighted matrix factorization method is used to obtain the latent factor representations of travel locations. The latent factor representation for a new travel location is estimated from its photos by using a convolutional neural network. Experimental results on a Flickr dataset demonstrate that the proposed method can provide better recommendations than existing methods.

Highlights

The continued growth of photo-sharing sites (e.g., Flickr and Panoramio) has increased the volume of community-contributed geotagged photos (CCGPs) that are available on the Web
We use similarity weight between users to exploit auxiliary attributes, which are integrated into the weighted matrix factorization (WMF) process to recommend travel locations
The proposed CNNMF framework uses similarity weight between users to exploit auxiliary attributes, which are integrated into the WMF process to recommend travel locations

Summary

Introduction

The continued growth of photo-sharing sites (e.g., Flickr and Panoramio) has increased the volume of community-contributed geotagged photos (CCGPs) that are available on the Web. These large amounts of CCGPs include rich information (e.g., user-provided tags, time, and visual contents, as shown in Figure 1 and Table 1) This information is tremendously useful for travel location recommendation systems [1], taking into consideration users’ travel preferences depending on their past check-ins, and developing travel location recommendation systems. Existing integrated methods only work on implicit/explicit rating prediction problems, and latent factor representation is not learned effectively with highly sparse content information.

Objectives

Methods

Conclusion