@inproceedings{d3660ae76f3d419da021ada91de91b40,
title = "Hotel recommendation based on user preference analysis",
abstract = "Recommender system offers personalized suggestions by analyzing user preference. However, the performance falls sharply when it encounters sparse data, especially meets a cold start user. Hotel is such kind of goods that suffers a lot from sparsity issue due to extremely low rating frequency. In order to handle these issues, this paper proposes a novel hotel recommendation framework. The main contribution includes: 1) We combine collaboration filtering (CF) with content-based (CBF) method to overcome sparsity issue, while ensuring high accuracy. 2) Travel intents are introduced to provide additional information for user preference analysis. 3) To provide as broad as possible recommendations, diversity techniques are employed. 4) Several experiments are conducted on the real Ctrip1 dataset, the results show that the proposed hybrid framework is competitive against classical approaches.",
keywords = "cold start, diversity, matrix factorization, recommender system, text mining",
author = "Kai Zhang and Keqiang Wang and Xiaoling Wang and Cheqing Jin and Aoying Zhou",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 2015 31st IEEE International Conference on Data Engineering Workshops, ICDEW 2015 ; Conference date: 13-04-2015 Through 17-04-2015",
year = "2015",
month = jun,
day = "19",
doi = "10.1109/ICDEW.2015.7129564",
language = "英语",
series = "Proceedings - International Conference on Data Engineering",
publisher = "IEEE Computer Society",
pages = "134--138",
booktitle = "ICDEW 2015 - 2015 IEEE 31st International Conference on Data Engineering Workshops",
address = "美国",
}