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Nowadays, artificial neural networks are widely used for users’ online travel planning. Personalized travel planning has many real applications and is affected by various factors, such as transportation type, intention destination estimation, budget limit and crowdness prediction. Among those factors, users’ intention destination prediction is an essential task in online travel platforms. The reason is that, the user may be interested in the travel plan only when the plan matches his real intention destination. Therefore, in this paper, we focus on predicting users’ intention destinations in online travel platforms. In detail, we act as online travel platforms (such as Fliggy and Airbnb) to recommend travel plans for users, and the plan consists of various vacation items including hotel package, scenic packages and so on. Predicting the actual intention destination in travel planning is challenging. Firstly, users’ intention destination is highly related to their travel status (e.g., planning for a trip or finishing a trip). Secondly, users’ actions (e.g. clicking, searching) over different product types (e.g. train tickets, visa application) have different indications in destination prediction. Thirdly, users may mostly visit the travel platforms just before public holidays, and thus user behaviors in online travel platforms are more sparse, low-frequency and long-period. Therefore, we propose a Deep Multi-Sequences fused neural Networks (DMSN) to predict intention destinations from fused multi-behavior sequences. Real datasets are used to evaluate the performance of our proposed DMSN models. Experimental results indicate that the proposed DMSN models can achieve high intention destination prediction accuracy. [ABSTRACT FROM AUTHOR]

YouTube offers a great opportunity for people to entertain, advertise, gain popularity, and generate revenue. How to increase views for a video has become the key question for anyone who wish to be famous or gain more revenue. Recognizing that a recommendation system is a major view source for videos, our goal in this paper is to increase video views in YouTube by leveraging on the recommendation system. We first perform measurements to understand how views are propagated among videos through recommendation links and identify factors that influence the recommendation produced by the system. Our measurement results show that similarity in video meta-data is a crucial ingredient in connecting videos. We then propose a keyword suggestion method for a video with the aim to raise video views through the recommendation system. The keyword suggestion method utilizes video clusters on a referrer video graph to obtain relevant keywords and ranks keywords based on both their relevance and their potential to attract video views. The effectiveness of the keyword suggestion method is demonstrated through a case study, showing that using the keywords suggested by our method leads to a larger number of video views and higher average watching time per video playback compared to initial user-given keywords. [ABSTRACT FROM AUTHOR]

With the explosive growth of products and multimedia contents on the Internet, the desire of users to find these online resources matching their interests makes it imperative to develop high quality recommendation systems. In this paper, we propose one novel neural network, namely Dynamic Heterogeneous Graph Convolutional Network (DHGCN), for item recommendation. Specifically, our proposed DHGCN consists of two components, namely the graph learner and heterogeneous graph convolution. The graph learner considers not only the user–item interactions but also user–user and item–item interactions, which are dynamically established during the graph evolution process. The heterogeneous graph convolution relies on a novel cross gating strategy to aggregate the representations yielded by the convolution over the learned heterogeneous graph and the item content information. Through comprehensive experiments on two real-world datasets, the proposed model is demonstrated to be effective on item recommendation task, outperforming the existing state-of-the-art models. [ABSTRACT FROM AUTHOR]