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 Ctrip 1 dataset, the results show that the proposed hybrid framework is competitive against classical approaches.

An Intelligent Data Analysis for Recommendation Systems Using Machine Learning

“…According to Zhang et al [9] Collaborative Filtering (CF) based approach is very successful technology in all RSs. The [10], [11] has reported that there are three fundamental challenges faced by CF approaches such as − Cold start challenge rise where an item appears which has not been rated before, recommendations cannot be made for it or when a new user without any prerecorded profile appears [12,13].…”

“…− Sparsity challenge appears when there are numerous items but too less rating values are available in the initial stage of recommendation [10], [13].…”

A comprehensive mechanism for hotel recommendation to achieve personalized search engine

“…However, most studies use only a comprehensive score, which results in a serious loss of information [14,15]. In recent years, with the development of text analysis technology, several researches have combined ratings and online reviews to select items [16,17].…”

A Novel Hotel Recommendation Model

“…Zhang et al combine collaborative filtering with content-based method to overcome the sparsity issue from low rating frequency problem [1]. Fumiyo Fukumoto et al incorporate different aspects of a hotel into account for a hotel recommendation model.…”

“…Hotel review mining has been studied by many researchers [1][2][3]. Zhang et al combine collaborative filtering with content-based method to overcome the sparsity issue from low rating frequency problem [1].…”