Open AccessRestaurant Recommendation in Vehicle Context Based on Prediction of Traffic Conditions
Author(s) -
Zehong Wang,
Jianhua Liu,
Shigen Shen,
Minglu Li
Publication year - 2021
Publication title -
international journal of pattern recognition and artificial intelligence
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 55
eISSN - 1793-6381
pISSN - 0218-0014
DOI - 10.1142/s0218001421590448
Subject(s) - recommender system , computer science , context (archaeology) , the internet , preference , selection (genetic algorithm) , world wide web , machine learning , paleontology , economics , biology , microeconomics
Restaurant recommendation is one of the most recommendation problems because the result of recommendation varies in different environments. Many methods have been proposed to recommend restaurants in a mobile environment by considering user preference, restaurant attributes, and location. However, there are few restaurant recommender systems according to the internet of vehicles environment. This paper presents a recommender system based on the prediction of traffic conditions in the internet of vehicles environment. This recommender system uses a phased selection method to recommend restaurants. The first stage is to screen restaurants that are on the user’s driving route; the second stage is to recommend restaurants from the user attributes, restaurant attributes (with traffic conditions), and vehicle context, using a deep learning model. The experimental evaluation shows that the proposed recommender system is both efficient and effective.