31-10-2014, 02:26 PM
Abstracts: Recommender systems are commonly defined as applications that e-commerce sites exploit to suggest products and provide consumers with information to facilitate their decision-making processes. They implicitly assume that we can map user needs and constraints, through appropriate recommendation algorithms, and convert them into product selections using knowledge compiled into the intelligent recommender. Following are the approaches that are required to build the recommender system though selection is purely based on application. These approaches are Collaborative filtering, Content based filtering, Knowledge based filtering and Hybrid System. Recommender systems are now a popular research area and increasingly used for ecommerce. But travel recommender systems are still difficult to build because developers of a travel recommender system need to consider not only the specific nature of the travel decision process, but also the wide range of heterogeneous information available in this domain. There are various factors affect all stages of the traveler’s decision-making process, which is a complex constructive activity. Most system focus on destination selection relates to the filtering (content- based) approach. Same filtering technology is not effectively applicable to all tourism systems. Pure collaborative filtering approaches cannot be readily applicable in the travel domain. The major issue is the complexity of travel objects; we can’t simplify a trip to the point where two travelers’ trips are the same. One approach could be to simplify the travel description to a certain point—for instance, representing just the destination—but then we will discover that the already visited destinations are insufficient to predict the next one. Additional context information must be included, so we must query the user about the content of his or her trip. Hybrid approaches that combine content- and collaborative-based approaches will more likely succeed.