Developing a context-aware location recommender system for location-based social networks

Abstract

People think about where to go many times throughout their lives. Although it is a very rapid and repetitive decision, generally it is hard to choose suitable places from endless number of options for some specific circumstances. Recommender systems are supposed to help to deal with those issues and take appropriate actions. However, the location decision is different from other decisions like what to listen, buy, or read from various aspects. The popularity of location-based social networks has prompted researchers to study recommendation systems for location. Traditional recommendation algorithms have been used for location recommendation. When used separately, each venue recommendation system algorithm has drawbacks. Another issue is that the context information is not commonly used in venue recommendation systems. Time, distance and weather conditions have more impact on decisions about where to go than all other decisions. Another point that should not be disregarded is that the effects of those contextual variables differ from user to user. This study proposes a hybrid recommendation model that combines contextual information, user- and item-based collaborative filtering and content-based filtering. For this purpose, user visit histories, venue-related information and contextual information related to individual user visits were collected from Twitter, Foursquare, and Weather Underground. The proposed hybrid system is evaluated using both offline experiments and a user study. This proposed system shows better results than baseline approaches.