What Your Images Reveal: Exploiting Visual Contents - IT Assignment Help

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ABSTRACT

The rapid growth of Location-based Social Networks (LBSNs) provides a vast amount of check-in data, which facilitates the study of point-of-interest (POI) recommendation. The majority of the existing POI recommendation methods focus on four aspects, i.e., temporal patterns, geographical influence, social correlations and textual content indications. For example, user’s visits to locations have temporal patterns and users are likely to visit POIs near them. In real-world LBSNs such as Instagram, users can upload photos associating with locations. Photos not only reflect users’ interests but also provide informative descriptions about locations. For example, a user who posts many architecture photos is more likely to visit famous landmarks; while a user posts lots of images about food has more incentive to visit restaurants. Thus, images have potentials to improve the performance of POI recommendation. However, little work exists for POI recommendation by exploiting images. In this paper, we study the problem of enhancing POI recommendation with visual contents. In particular, we propose a new framework Visual Content Enhanced POI recommendation (VPOI), which incorporates visual contents for POI recommendations. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework.

Keywords POI recommendation; Visual contents; Location-based Social Networks

1. INTRODUCTION

As an increasingly popular application of location-based services, location-based social networks (LBSNs), such as Yelp, Instagram and Foursquare, have attracted millions of users. Users in LBSNs can check in their preferred points-ofinterest (POIs), e.g., museums, restaurants and stores, and share their experiences of visiting these POIs with friends, resulting in huge amount of user check-in data. The avail- c 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License. WWW 2017, April 3–7, 2017, Perth, Australia. ACM 978-1-4503-4913-0/17/04. http://dx.doi.org/10.1145/3038912.3052638 . ability of user check-in data in large volume brings in new opportunities to design appealing services to facilitate user’s travels and social interactions. Personalized POI recommendation, which aims at recommending personalized POIs to a user who has not visited them before, is one of such services. Various POI recommendation methods have been proposed, which mainly study four aspects, i.e., geographical influence, social correlations, temporal patterns and textual content indications [8, 37, 35, 4, 34, 10]. These aspects have been proven to be effective for improving POI recommendations

2. RELATED WORK

In this section, we will briefly review related works on POI recommendation and visual contents for data mining.

2.1 POI Recommendation

POI recommendation, also called location recommendation, has been recognized as an essential task on recommender systems. Existing work on POI recommendation generally focuses on four aspects, i.e., geographical influence, social correlations, temporal patterns and textual content indications [8]. Ye et al. [36] introduced POI recommendation on LBSNs and investigated the geographical influence [37] and social influence [35] for POI recommendation. Cheng et al. [4] investigated the geographical and social influence through a multi-center Gaussian model. Zhang et al. [39] further exploits categorical correlations together with geographical and social correlations. Temporal information has also attracted much attention from researchers. Gao et al. [9] investigated the temporal cyclic patterns of check-ins in terms of temporal non-uniformness and temporal consecutiveness.

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