An Outdoor Recommendation System based on User Location History
Year: 2005
Type: dissertation
Abstract: Recommendation systems are now widely used in online shopping sites, but so far there have been few attempts of applying them to real-world shopping. In this paper we propose a novel real-world recommendation system, which recommends shops to users based on their individual preferences and needs, estimated by analyzing their past location history acquired using GPS. The system automatically figures out each user's frequently visited shops using a custom estimation algorithm, and makes recommendations by using the shops as input to the item-based collaborative filtering algorithm. Furthermore, to provide more timely recommendations, our system takes into account the user's usual shopping routes, and the ease of access from the user's current location to each shop. We have conducted an evaluation test using a prototype of our system at Daikanyama, Tokyo, and the results show great promise in the system's ability to make accurate recommendations.
Authors: Yuichiro Takeuchi, Masanori Sugimoto
Institution: The University of Tokyo
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1484428697