Recently, deep learning models have been widely explored in recommender systems. Though having achieved remarkable success, the design of task-aware recommendation models usually requires manual feature engineering and architecture engineering from domain experts. To relieve those efforts, we explore the potential of neural architecture search (NAS) and introduce AMEIR for Automatic behavior Modeling, interaction Exploration and multi-layer perceptron (MLP) Investigation in the Recommender system. Specifically, AMEIR divides the complete recommendation models into three stages of behavior modeling, interaction exploration, MLP aggregation, and introduces a novel search space containing three tailored subspaces that cover most of the existing methods and thus allow for searching better models. To find the ideal architecture efficiently and effectively, AMEIR realizes the one-shot random search in recommendation progressively on the three stages and assembles the search results as the final outcome. The experiment over various scenarios reveals that AMEIR outperforms competitive baselines of elaborate manual design and leading algorithmic complex NAS methods with lower model complexity and comparable time cost, indicating efficacy, efficiency, and robustness of the proposed method.

Recently, sequential recommendation has become a significant demand for many real-world applications, where the recommended items would be displayed to users one after another and the order of the displays influences the satisfaction of users. An extensive number of models have been developed for sequential recommendation by recommending the next items with the highest scores based on the user histories while few efforts have been made on identifying the transition dependency and behavior continuity in the recommended sequences. In this paper, we introduce the Adversarial Oracular Seq2seq learning for sequential Recommendation (AOS4Rec), which formulates the sequential recommendation as a seq2seq learning problem to portray time-varying interactions in the recommendation, and exploits the oracular learning and adversarial learning to enhance the recommendation quality. We examine the performance of AOS4Rec over RNN-based and Transformer-based recommender systems on two large datasets from real-world applications and make comparisons with state-of-the-art methods. Results indicate the accuracy and efficiency of AOS4Rec, and further analysis verifies that AOS4Rec has both robustness and practicability for real-world scenarios.

Benefited from rapid developments of deep learning, 3D shape recognition has become a remarkable subject in computer vision systems.The existing methods of multi-perspective views have shown competitive performance in 3D shape recognition.However, they have not yet fully exploited the information among all views of projection.In this paper, we propose a novel Multi-view Moments Embedding Network(MMEN) for capturing multiple moments information.MMEN obtains the similarity between different views and retains the description of the original view by generating moments matrix for representing the general features of the 3D shape.Additionally, we apply the matrix square-root layer to perform a non-linear scaling to the eigenvalues of the moment embedding matrix.We compare the performance of our proposed network with several state-of-the-art models on the ModelNet datasets, and the results of the average instance/class accuracy demonstrate the promising performance of MMEN on 3D shape recognition.

In the era of big data, information on relationships changes along with time, and the graphs of relationships captured at consecutive timestamps form the multiple temporal relational (MTR) network. To identify the relations in the network while preserving the network structure, a common solution is to learn the network representations through network embedding methods, and then build the relations upon the similarity among these representations. However, the existing network embedding methods either focus on a single relation or ignore the correlation between the heterogeneous and homogeneous relations, and thus it is difficult to investigate the multiple temporal features in the network. In this paper, we propose a novel network embedding method, named Homo- Hetero Network Embedding (HHNE), for the MTR networks. The HHNE utilizes the Graph Convolutional Network (GCN) to extract homogeneous features from each temporal relational network and then generates the homo-hetero network embeddings by fusing the single temporal relational features through a Multi- Layer Perceptron (MLP). Therefore, HHNE could capture the multi- dimensional characteristics in the network, including both intra-relation information and inter-relation information. To show the efficiency of HHNE, we conduct experiments in a real-world dataset on predicting new relations in the MTR networks. The result reveals that our method could outperform several legacy network embedding methods and state- of-the-art multi-relational network embedding methods in the task of relation prediction, demonstrating that the proposed embedding method is more suitable for the MTR network.