Efficient and Effective Location Recommendation through Content Analysis

Location recommendation plays a vital role in helping people in finding beautiful places. The recent research has studied how to recommend locations with social and geographical information, but few of them addressed the cold-start problem. 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. Prior studies have empirically shown sampling-based methods do not perform well. Therefore, a novel approach has been implemented which recommend location based on machine learning process. The user reviews are taken into consideration as dataset. Dataset are preprocessed and meaning of user reviews is understood by system automatically through NLP. Then new recommendation has been suggested through this process and data base is loaded. Hence our system achieves more accurate recommendation compared to other existing approach. Finally, we evaluate LR-NLP with a user review dataset in which users have profiles and textual content. The results show that our proposed outperforms several competing baselines, and that user feedback is not only effective for improving recommendations but also overcomes cold-start problems.