| Summary: | 碩士 === 國立臺北科技大學 === 電腦與通訊研究所 === 101 === In the rapid growth in smart phone users, location-based services (LBS) have become an extremely important service. However, in many LBS recommendation systems, the most important things is how to accurately response the user''s preferences, and recommend some suitable items. In the past, such a recommendation system was based on the historical evaluation of the user and everyone interested ranking. However, only a small number of users ever take the initiative to score items, causing the system cannot accurately recommend suitable items. In this paper, we proposed SoLoMo-based Collaborative Filtering Recommendation System. Using the social website''s check-in as a point of interest (POI) score, it thus can significantly improve the deficiencies explicit rating, and more precise recommended suitable POIs to the users.
The simulation result showed that SCF has not only less recommend error and more recommend coverage, but also less average recommend time than LCFDTP, DFBT and TWCF. In recommend error, when user’s velocity is 50 km/hr and query range is 0.5 km, SCF is better than the others about 58 %. In average recommend time, SCF is better than the others about 49 %. Finally, in recommend coverage, SCF is better than the others about 7 %. These showed that SCF has better effect on recommendation.
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