SPORE: A Sequential Personalized Spatial Item Recommender System

Abstract

SPORE: A sequential personalized spatial item recommender system Project report on web mining With the rapid development of location-based social networks (LBSNs), spatial item recommendation has become an important way of helping users discover interesting locations to increase their engagement with location-based services.

The advantages of modeling the sequential effect at the topic-region level include a significantly reduced prediction space, an effective alleviation of data sparsity and a direct expression of the semantic meaning of users’ spatial activities. Furthermore, we design an asymmetric Locality Sensitive Hashing (ALSH) technique to speed up the online top-k recommendation process by extending the traditional LSH.

Conclusion

In this paper, we proposed a SPORE: A sequential personalized spatial item recommender system project report on web mining. To effectively overcome the challenges arising from low-sampling rate and huge prediction space, SPORE introduces a novel latent variable topic-region to model and fuse the sequential influence and personal interests in the latent space.

A topicregion corresponds to both a semantic topic (i.e., a soft cluster of words) and a geographical region (i.e., a soft cluster of locations). The advantages of modeling sequential effects at the topic-region level include a significantly reduced prediction space, an effective alleviation of data sparsity and a direct expression of the semantic meaning of users’ spatial activities.

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