| dc.description.abstract | With the popularity of internet, the demand of people’s lives is searching local POI (Points of Interest) by intelligent mobile device (location-based services) e.g., stores, landmarks. Today, people used to look for POI on Google Map. However, not all that we want POIs on the Google Map. Some POIs appear on the web page but does not appear on the map, so the POI is still insufficient on the Google Map. Therefore, integrating multiple sources and providing an effective retrieval POI map service is our goal in this paper. People give a query on the map service. They may look for multiple matching query of POIs or a specific POI. In the search scope part, people may want to look for relevant POIs in the neighborhood or out of the scope. How to sort the POI is also a problem. The search results may need to sort by relevance or distance. If the POI Information is insufficient in the map service, the effectiveness would be low. In addition to finding relevant POIs by retrieval system, query expansion is a good method to help people find relevant POIs,
In this paper, our goal is to improve the effectiveness of search results. The approach has two parts. The first part is learning to rank. We design several features and get the most relevant POI to query through correlative prediction. The second part is query expansion. After collecting POI descriptions and clustering it by LDA (Latent Dirichlet allocation) , we build a bi-partite graph and give a certain domain of words by graph.
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