Despite recommender systems being useful, for some applications it is hard to accumulate all the required information needed for the recommendation. In today‟s ubiquitous environment, mobile devices with different characteristics are widely available. Our work focuses on the recommendation service built on mobile environment to support tourists‟ traveling need. When tourists visit a new attraction, their recommender systems can exchange data with the attraction system to help obtain rating information of people with similar tastes. Such asynchronous rating exchange mechanisms allow a tourist to receive ratings from other people even though they may not collocate at the same time. We proposed four data exchange methods between a user and an attraction system. Our recommendation mechanism incorporates other users‟ opinions to provide recommendations once the user has collected enough ratings. Every method is compared under four conditions which attraction systems carry different amount of existing data. Then we compare these methods under different amount of existing rating data and shed the light on their advantages and disadvantages. Finally, we compare our proposed asynchronous methods with other synchronous data exchange methods proposed previously.

¡@¡@Recommendation techniques are developed to uncover users¡¥ real needs among large volume of information. Recommender systems help us filter information and present those similar to our tastes. As wireless technology develops and mobile devices become more and more powerful, new recommender systems appear to adapt to new implementation environment. We focus on travel recommender systems implemented in a mobile P2P environment using collaborative filtering recommendation algorithms which intend to provide real-time suggestions to travelers when they are on the move. Using the concept of incorporating other travelers¡¥ suggestions to the next attraction, we let users exchange their ratings toward visited attractions and use these ratings as a basis of recommendation. ¡@¡@We proposed six data exchange algorithms for travelers to exchange their ratings. The proposed methods were experimented in the homogeneous and heterogeneous environment. The experimental results show that the proposed data exchange methods have better recommendation hit ratio than content-based recommendation methods and better performance compared with other methods only using ratings of users when they meet face-to-face. Finally, all methods are compared and evaluated. An optimal method should be able to strike a balance between algorithm performance and the amount of data communication.