Context-aware hotel recommendation system based on hybrid approach to mitigate cold-start-problem

Recommender System is an efficient tool that consider an individual's opinion to identify their content more appropriately and selectively. This system has been applied to various domain, but in the field of research area, service based recommendation system plays a major role. In recent years, the growth rate of online hotel searching has been increased much faster and makes this online hotel searching a very difficult task due to the abundant amount of online information. Reviews written by the travelers replaces the word-of-mouth but then to searching becomes the time consuming task based on user preference. Reviews crawled from the travelers visiting sites are a common and valuable source of information for recommendation of a hotel, yet little attention has been paid as, how to present the reviews of a reviewers in an understandable format. The purpose of this project is to recommend the travelers the name of the hotels based on their preferences, by analyzing the other travelers reviews together with the rating value to improve the prediction accuracy. Users do not rate adequate hotels to enable collaborating filtering based recommendation, which can lead to an issue called as cold start problem. To solve this issue context-aware hybrid approach is used where collaborative filtering technique aggregates with the sentimental analysis, to provide personalized hotel recommendation. Here aspect based sentiment analysis is performed where weights of each aspect is calculated to find its orientation score. Also we use semi-supervised clustering algorithm to group the aspects of similar meaning. Lexicon based approach is used to identify the sentiment towards the hotel aspects based on the defined context. Thus, context-based approach increases the recommendation results.