PROJECT TITLE :
Efficient Keyword-aware Representative Travel Route Recommendation - 2017
With the recognition of social media (e.g., Facebook and Flicker), users will easily share their check-in records and photos during their visits. In view of the massive variety of user historical mobility records in social media, we aim to discover travel experiences to facilitate trip coming up with. When designing a visit, users continually have specific preferences regarding their visits. Instead of proscribing users to restricted query choices like locations, activities, or time periods, we take into account arbitrary text descriptions as keywords about customized needs. Moreover, a numerous and representative set of recommended travel routes is needed. Previous works have elaborated on mining and ranking existing routes from check-in knowledge. To meet the need for automatic trip organization, we claim that more options of Places of Interest (POIs) ought to be extracted. So, during this paper, we have a tendency to propose an efficient Keyword-aware Representative Travel Route framework that uses data extraction from users' historical mobility records and social interactions. Explicitly, we tend to have designed a keyword extraction module to classify the POI-connected tags, for effective matching with query keywords. We have a tendency to have any designed a route reconstruction algorithm to construct route candidates that fulfill the necessities. To provide befitting query results, we have a tendency to explore Representative Skyline ideas, that is, the Skyline routes which best describe the trade-offs among completely different POI features. To evaluate the effectiveness and efficiency of the proposed algorithms, we have conducted extensive experiments on real location-based social network datasets, and the experiment results show that our ways do indeed demonstrate sensible performance compared to state-of-the-art works.
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