A SURVEY ON A COCKTAIL APPROACH FOR TRAVEL PACKAGE RECOMMENDATION

Abstract

Providing better travel services for tourists is one of the important applications in urban computing. The worlds is of commerce, travel, entertainment, and Internet technology are linked, different types of business data is accessible for innovative use and regular analysis. Here it provides a study of utilizing online travel information for the personalized travel package recommendation. Though many recommender systems have been developed for enhancing the quality of travel service, most of them lack a systematic and open framework to dynamically incorporate multiple types of additional context information existing in the tourism domain, such as the travel area, season, and price of the travel packages. First analyze the properties of the old travel packages and develop a tourist-area-season topic (TAST) model. This TAST model represents different travel packages and different topic distributions of tourist, the topic extraction is stated on both the tourists and the natural characteristics of the landscapes. According to the topic model representation, a cocktail approach is generated so that to form lists for personalized travel package recommendation. The TAST model is extended to the tourist-relation-area-season topic (TRAST) model for collecting the relationships among the tourists for all travel groups. Then analyze TAST model, TRAST model, and cocktail recommendation approach on the current travel package data. The TAST model can effectively grabs the individual characteristics of travel data and cocktail approach, so it is more efficient than old recommendation techniques for travel package recommendation by including tourist relationships, TRAST model is used as an effective evaluation for travel group formation.