PYTHON SOURCE CODE FOR Scalable Content-Aware Collaborative Filtering For Location Recommendation
PYTHON SOURCE CODE FOR Scalable Content-Aware Collaborative Filtering For Location Recommendation
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PYTHON SOURCE CODE FOR Scalable Content-Aware Collaborative Filtering For Location Recommendation
Abstract:
Location recommendation plays an essential role in helping people find attractive places. Though recent research has studied how to recommend locations with social and geographical information, few of them addressed the cold-start problem of new users. Because mobility records are often shared on social networks, semantic information can be leveraged to tackle this challenge. A typical method is to feed them into explicit-feedback-based content-aware collaborative filtering, but they require drawing negative samples for better learning performance, as users’ negative preference is not observable in human mobility. However, prior studies have empirically shown sampling-based methods do not perform well. To this end, we propose a scalable Implicit-feedback-based Content-aware Collaborative Filtering (ICCF) framework to incorporate semantic content and to steer clear of negative sampling.
PYTHON SOURCE CODE FOR Scalable Content-Aware Collaborative Filtering For Location Recommendation
#collaborative_filtering#content_based_retrieval#recommender_systems
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