{'fraction_non_alphanumeric': 0.02528735632183908, 'fraction_numerical': 0.0, 'mean_word_length': 5.629441624365482, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Next Location Recommendation Based on Semantic-Behavior Prediction

In recent years, research on location prediction and recommendation of mobile users has attracted wide attention. To the best of our knowledge, many relevant studies are based on users' check-in dataset which suffer from the data sparsity. Some studies utilize spatial-temporal information of users' trajectories, but ignore the semantic information implied by these trajectory data. In this paper, we propose a novel approach for recommending the next locations for a user based on semantic-behavior prediction which utilizes both geographic features and semantic features of users' GPS trajectories, thus improving the recommendation's accuracy and diversity. The framework performs location recommendation for a user with two steps. Firstly, we predict the user's next semantic-behaviors using the Bidirectional LSTM model(BiLSTM). Secondly, the Similarity-based Markov Model(SMM) is constructed using the trajectories of the target user and the cluster to which he belongs to obtain the candidate locations, and then a query is performed to get the recommended location set based on the predicted semantic-behaviors and spatial distance. Through a comprehensive evaluation by experiments, our proposal delivers excellent performance.