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Point-of-interest (POI) retrieval that searches for relevant destination locations plays a significant role in on-demand ridehailing services. Existing solutions to POI retrieval mainly retrieve and rank POIs based on their semantic similarity scores. Although intuitive, quantifying the relevance of a Query-POI pair by single-field semantic similarity is subject to inherent limitations. In this paper, we propose a novel Query-POI relevance model for effective POI retrieval for ondemand ride-hailing services. Different from existing relevance models, we capture and represent multi-field and local&global semantic features of a Query-POI pair to measure the semantic similarity. Besides, we observe a hidden correlation between origin-destination locations in ride-hailing scenarios, and propose two location embeddings to characterize the specific correlation. By incorporating the geographic correlation with the semantic similarity, our model achieves better performance in POI ranking. Experimental results on two real-world click-through datasets demonstrate the improvements of our model over state-of-the-art methods.

Clustering analysis has been an important area of machine learning and data mining research, it can help us know the connection between things more clearly. In recent years, the research of spectral clustering algorithm has been a new and efficient clustering analysis algorithm. In this paper, the sparsity and the real-time problem of traditional recommendation algorithms, a new recommendation algorithm based on spectral clustering is proposed. The spectral clustering process can improve the efficiency of spectral clustering algorithm. Spectral clustering can be performed offline, which will accelerate the speed of online recommendation. The experimental results on Movie lens show that the new algorithm improves recommendation quality in MAE and coverage.

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