Location-based and preference-aware recommendation using sparse geo-social networking data

Abstract

The popularity of location-based social networks provide us with a new platform to understand users' preferences based on their location histories. In this paper, we present a location-based and preference-aware recommender system that offers a particular user a set of venues (such as restaurants) within a geospatial range with the consideration of both: 1) User preferences, which are automatically learned from her location history and 2) Social opinions, which are mined from the location histories of the local experts. This recommender system can facilitate people's travel not only near their living areas but also to a city that is new to them. As a user can only visit a limited number of locations, the userlocations matrix is very sparse, leading to a big challenge to traditional collaborative filtering-based location recommender systems. The problem becomes even more challenging when people travel to a new city. To this end, we propose a novel location recommender system, which consists of two main parts: offline modeling and online recommendation. The offline modeling part models each individual's personal preferences with a weighted category hierarchy (WCH) and infers the expertise of each user in a city with respect to different category of locations according to their location histories using an iterative learning model. The online recommendation part selects candidate local experts in a geospatial range that matches the user's preferences using a preference-aware candidate selection algorithm and then infers a score of the candidate locations based on the opinions of the selected local experts. Finally, the top-k ranked locations are returned as the recommendations for the user. We evaluated our system with a large-scale real dataset collected from Foursquare. The results confirm that our method offers more effective recommendations than baselines, while having a good efficiency of providing location recommendations.

An Automatic User Grouping Model for a Group Recommender System in Location-Based Social Networks

“…Venues are shown by squares on the map. The categories of venues have different granularities, usually represented by a category hierarchy as shown in the bottom part of Figure 1a [27]. For example, the "food" category includes "Chinese restaurant" and "Italian restaurant", and the "art and entertainment" category includes "art gallery" and "museum", etc.…”

Development of a group recommender application in a Social Network

“…Besides, there is recent work reporting significant recommendation performance improvement for social recommender systems [21,34,35,36,37,38,39]. On the other hand, there are also unsuccessful attempts at applying social recommendation [40,41].…”

Recommendations Based on Purchase Patterns

“…Their system use a Bayesian network that models the probabilistic influences of the user's personal profile and contextual information on the restaurant attribute values. Bao et al [21] used location history of a user to derive personal preferences and social opinions mined from local experts to facilitate personalized location recommendations.…”

“…In the context of mobile environment, a lot more information about buyers can be obtained, such as location. Location based recommendation, in this case, is preferred for business such as restaurants [19] and tourist attractions [20], and is often related to social networks [21].…”