This paper presents a class of Cohen–Grossberg neural networks (CGNNs) with discontinuous activations and time-varying delays. Firstly, under the framework of Filippov solution, we derive some general sufficient conditions to guarantee the global existence of the solutions to the proposed CGNNs with discontinuous activations and time-varying delays. Then, by constructing the new Lyapunov–Krasovskii functional, some new sufficient criteria are given to ascertain the globally exponential stability of the anti-periodic solution for the considered CGNNs with discontinuous activations and time-varying delays. To the authors’ knowledge, the results established in the paper are the only available results on the anti-periodic for the discontinuous CGNNs; some previously known results are extended and complemented. Finally, simulation results of two typical numerical examples are also delineated to demonstrate the effectiveness of our theoretical results.

Recently accumulated massive amounts of geo-tagged photos provide an excellent opportunity to understand human behaviors and can be used for personalized tour recommendation. However, no existing work has considered the visual content information in these photos for tour recommendation. We believe the visual features of photos provide valuable information on measuring user / Point-of-Interest (POI) similarities, which is challenging due to data sparsity. To this end, in this paper, we propose a visual feature enhanced tour recommender system, named ‘Photo2Trip’, to utilize the visual contents and collaborative filtering models for recommendation. Specifically, we propose a Visual-enhanced Probabilistic Matrix Factorization model (VPMF), which integrates visual features into the collaborative filtering model, to learn user interests by leveraging the historical travel records. We then extend VPMF to End-to-End training framework to incorporate users (POIs) latent factors into the learning process of the visual content of photos, which generalizes the applicability of the proposed VPMF framework in tour recommendation. Extensive empirical studies verify that our proposed visual-enhanced personalized tour recommendation method outperforms other benchmark methods in terms of recommendation accuracy. The results also show that visual features are effective in alleviating the data sparsity and cold start problems on personalized tour recommendation.

Session-based recommendation, which aims to predict the user's immediate next action based on anonymous sessions, is a key task in many online services (e.g., e-commerce, media streaming). Recently, Self-Attention Network (SAN) has achieved significant success in various sequence modeling tasks without using either recurrent or convolutional network. However, SAN lacks local dependencies that exist over adjacent items and limits its capacity for learning contextualized representations of items in sequences. In this paper, we propose a graph contextualized self-attention model (GC-SAN), which utilizes both graph neural network and self-attention mechanism, for session-based recommendation. In GC-SAN, we dynamically construct a graph structure for session sequences and capture rich local dependencies via graph neural network (GNN). Then each session learns long-range dependencies by applying the self-attention mechanism. Finally, each session is represented as a linear combination of the global preference and the current interest of that session. Extensive experiments on two real-world datasets show that GC-SAN outperforms state-of-the-art methods consistently.

The sequential recommendation, which models sequential behavioral patterns among users for the recommendation, plays a critical role in recommender systems. However, the state-of-the-art Recurrent Neural Networks (RNN) solutions rarely consider the non-linear feature interactions and non-monotone short-term sequential patterns, which are essential for user behavior modeling in sparse sequence data. In this paper, we propose a novel Recurrent Convolutional Neural Network model (RCNN). It not only utilizes the recurrent architecture of RNN to capture complex long-term dependencies, but also leverages the convolutional operation of Convolutional Neural Network (CNN) model to extract short-term sequential patterns among recurrent hidden states. Specifically, we first generate a hidden state at each time step with the recurrent layer. Then the recent hidden states are regarded as an “image”, and RCNN searches non-linear feature interactions and non-monotone local patterns via intra-step horizontal and inter-step vertical convolutional filters, respectively. Moreover, the output of convolutional filters and the hidden state are concatenated and fed into a fully-connected layer to generate the recommendation. Finally, we evaluate the proposed model using four real-world datasets from various application scenarios. The experimental results show that our model RCNN significantly outperforms the state-of-the-art approaches on sequential recommendation.