Prediction Sequence Patterns of Tourist from the Tourism Website by Hybrid Deep Learning Techniques

Tourism is an important industry that generates incomes and jobs in the country where this industry contributes considerably to GDP. Before traveling, tourists usually need to plan an itinerary listing a sequence of where to visit and what to do. To help plan, tourists usually gather information by reading blogs and boards where visitors who have previously traveled posted about traveling places and activities. Text from traveling posts can infer travel itinerary and sequences of places to visit and activities to experience. This research aims to analyze text postings using 21 deep learning techniques to learn sequential patterns of places and activities. The three main techniques are Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU) and a combination of these techniques including their adaptation with batch normalization. The output is sequential patterns for predicting places or activities that tourists are likely to go and plan to do. The results are evaluated using mean absolute error (MAE) and mean squared error (MSE) loss metrics. Moreover, the predicted sequences of places and activities are further assessed using a sequence alignment method called the Needleman–Wunsch algorithm (NW), which is a popular method to estimate sequence matching between two sequences

Spectral Collaborative Filtering

Despite the popularity of Collaborative Filtering (CF), CF-based methods are haunted by the \textit{cold-start} problem, which has a significantly negative impact on users' experiences with Recommender Systems (RS). In this paper, to overcome the aforementioned drawback, we first formulate the relationships between users and items as a bipartite graph. Then, we propose a new spectral convolution operation directly performing in the \textit{spectral domain}, where not only the proximity information of a graph but also the connectivity information hidden in the graph are revealed. With the proposed spectral convolution operation, we build a deep recommendation model called Spectral Collaborative Filtering (SpectralCF). Benefiting from the rich information of connectivity existing in the \textit{spectral domain}, SpectralCF is capable of discovering deep connections between users and items and therefore, alleviates the \textit{cold-start} problem for CF. To the best of our knowledge, SpectralCF is the first CF-based method directly learning from the \textit{spectral domains} of user-item bipartite graphs. We apply our method on several standard datasets. It is shown that SpectralCF significantly outperforms state-of-the-art models. Code and data are available at \url{https://github.com/lzheng21/SpectralCF}.Comment: RecSys201