Multimodal Conversational Emotion recognition (MMCER) aims to detect the muti-emotion label for each utterance from heterogeneous visual, text and audio modalities. In this paper, we focus on applying multi-modal graph data structures to conversational emotion recognition and use a novel and efficient graph—MMDCGs to better integrate multi-modal contextual information into conversations. MMDCG provides a new way of encoding intrinsic structural connectivity. Besides, inspired by time series analysis, we set a rolling time window as the receptive field, which can reduce the interference of remote information on the current utterances detection and achieve the purpose of data enhancement. We innovatively ensemble such graph structures with transformers, named rolling-windows MMDCGs (RW-MMDCG). Comprehensive experiments are performed on two representative multi-modal datasets, IEMOCAP and MELD, and we compare them with existing baselines, demonstrating the great advantages and effectiveness of RW-MMDCG.

Knowledge representation learning is an automatic learning technique that can embeds a knowledge graph into a low-dimensional vector space. With use of this, knowledge becomes computable and various intelligent applications can be realized. Traditional semantic discriminating models suggest that the embeddings of entities should depend on the specific semantic environment. We find that the multiple latent information of relations has not been put to use by these models. In this paper, a gated semantic discriminating model (GSDM) is proposed to select useful latent information and neglect useless information according to the specific semantic environment for both entities and relations. Experiments show that GSDM achieves better performance than related state-of-the-art baselines on most indicators. The better trade-off between the discriminate parameter pressure and the model performance has proved the correctness and feasibility of semantic discriminating mechanism to some extent.

Recently, top-k recommendation system is getting more and more attention from researchers and unlike the rating prediction task, the purpose of top-k recommendation is to present the user with a list of items they are most interested in. Many rating prediction models do not produce good ranking results, so the top-k recommendation faces two challenges: the first is how to accurately obtain user and item latent vector from the user-item interaction rating matrix, and the second challenge is how to combine the ranking strategy with the recommendation model deeply. The booming deep learning technology, such as autoencoder and graph neural networks, brings us new solutions. So in this paper, we propose a novel model: Graph Attention Autoencoder for Collaborative Pair-wise Ranking. The autoencoder consists of graph attention encoder and collaborative neural decoder, which is used to generate user and item latent vector accurately. And then we use the pairwise ranking learning process to ensure the rating value accuracy and rating value pairwise ranking consistency. Finally, experimental results on three real-world datasets demonstrate the superiority of our model.

In recent years, audiences can find their interested TV play or movie videos by labels easily. However, for finding shots with certain semantic content in these videos, it is still a problem to annotate video shots by labels. Some existing approaches train models with annotated shots which cost a lot in labeling manually. Some other methods in solving this kind of task assume that the content of a video is only limited in the labels of the video. They ignore that the labels of a video are too coarse-grained to cover all content of the video. In this paper, we propose a multi-label, multi-instance topic model to annotate video shots by video labels. In a multi-label, multi-instance framework, video shots can be regarded as instances and shot labels are learned from labels in video level which makes the cost of labeling cheaper. On the other hand, our model learns label semantics by controlling the relationship between video labels and shots to solve coarse-grained problem. Furthermore, we also learn keywords for every video. The experiments on a large-scale real-world dataset show that our model outperforms other baseline models substantially.

Dynamic graph link prediction has attracted increasing attention in various fields such as social networks, paper citation networks and knowledge graphs. Many models have been developed to predict the future graph structure. In this paper, we propose a link prediction model with semantic evolution (LISE), to predict links in a sequence of graph over time. Our approach is based on the discovery of non-random initialization dynamic word embedding which is a kind of method to study semantic evolution. It can help us train node embedding in the same space and introduce temporal context into the embedding training of nodes. Based on node embedding in the same space, LISE can unify historical behavior, graph snapshots structure information and dynamic attributes into a frame. We evaluate our proposed method and various comparing methods on two real-world datasets. The experimental results prove the effectiveness of the link prediction made by LISE model.

In recent years, social recommendation is a research hotspot because it contains social network information which can effectively solve the problem of data sparsity and cold start. But the social recommendation task faces two problems: one is that how to accurately learn user latent vector and item latent vector from user-item interaction graph and social graph, the other is that how to depict the intrinsic and complex interaction between users and items. With the development of graph neural networks, node embedding is becoming more and more accurate on the graph. Besides neural collaborative filtering explores the interaction of users and items deeply. So in this paper, we propose a novel model: graph attention networks for neural social recommendation (GAT-NSR). This model adopts multi-head attention mechanism for message passing on the two graphs, which get user & item latent vector from different perspectives. And also we design a neural collaborative recommendation module to capture the inherent characteristics of user-item interaction behavior for recommendation. Finally, detailed experimental results on two real-world datasets clearly prove the effectiveness of our proposed model.

With the thriving of location-based social networks, a large number of user check-in data have been accumulated. Tasks such as the prediction of the next check-in location can be addressed through the usage of LBSN data. Previous work mainly uses the historical trajectories of users to analyze users' check-in behavior, while the social information of users was rarely used. In this paper, we propose a unified location prediction framework to integrate the effect of history check-in and the influence of social circles. We first employ the most frequent check-in model (MFC) and the user-based collaborative filtering model (UCF) to capture users' historical trajectories and users' implicit preference, respectively. Then we use the multi-social circle model (MSC) to model the influence of three social circles. Finally, we evaluate our location prediction framework in the real-world data sets, and the experimental results show that our model performs better than the state-of-the-art approaches in predicting the next check-in location.