Rating prediction is a classic problem underlying recommender systems. It is traditionally tackled with matrix factorization. Recently, deep learning based methods, especially graph neural networks, have made impressive progress on this problem. Despite their effectiveness, existing methods focus on modeling the user-item interaction graph. The inherent drawback of such methods is that their performance is bound to the density of the interactions, which is however usually of high sparsity. More importantly, for a strict cold start user/item that neither appears in the training data nor has any interactions in the test stage, such methods are unable to learn the preference embedding of the user/item since there is no link to this user/item in the graph. In this work, we develop a novel framework Attribute Graph Neural Networks (AGNN) by exploiting the attribute graph rather than the commonly used interaction graph. This leads to the capability of learning embeddings for the strict cold start users/items. Our AGNN can produce the preference embedding for a strict cold user/item by learning on the distribution of attributes with an extended variational auto-encoder (eVAE) structure. Moreover, we propose a new graph neural network variant, i.e., gated-GNN, to effectively aggregate various attributes of different modalities in a neighborhood. Empirical results on three real-world datasets demonstrate that our model yields significant improvements for strict cold start recommendations and outperforms or matches the state-of-the-art performance in the warm start scenario.

Attributed networks are ubiquitous in the real world, such as social networks. Therefore, many researchers take the node attributes into consideration in the network representation learning to improve the downstream task performance. In this article, we mainly focus on an untouched “oversmoothing” problem in the research of the attributed network representation learning. Although the Laplacian smoothing has been applied by the state-of-the-art works to learn a more robust node representation, these works cannot adapt to the topological characteristics of different networks, thereby causing the new oversmoothing problem and reducing the performance on some networks. In contrast, we adopt a smoothing parameter that is evaluated from the topological characteristics of a specified network, such as small worldness or node convergency and, thus, can smooth the nodes’ attribute and structure information adaptively and derive both robust and distinguishable node features for different networks. Moreover, we develop an integrated autoencoder to learn the node representation by reconstructing the combination of the smoothed structure and attribute information. By observation of extensive experiments, our approach can preserve the intrinsical information of networks more effectively than the state-of-the-art works on a number of benchmark datasets with very different topological characteristics.

End-to-end aspect based sentiment analysis (E2E-ABSA) aims to jointly extract aspect terms and predict aspect-level sentiment for opinion reviews. Though supervised methods show effectiveness for E2E-ABSA tasks, the annotation cost is extremely high due to the necessity of fine-grained labels. Recent attempts alleviate this problem using the domain adaptation technique to transfer the word-level common knowledge across domains. However, the biggest issue in domain adaptation, i.e., how to transfer the domain-specific words like pizza and delicious in the source “Restaurant” to the target “Laptop” domain, has not been resolved. In this paper, we propose a novel domain adaptation method to address this issue by enhancing the transferability of domain-specific source words in a retrieve-and-edit way. Specifically, for all source words, we first retrieve the transferable prototypes from unlabeled target data via their syntactic and semantic roles. We then edit the source words to enhance their transferability by absorbing the knowledge carried in prototypes. Finally, we design an end-to-end framework to jointly accomplish cross-domain aspect term extraction and aspect-level sentiment classification. We conduct extensive experiments on four real-world datasets. The results prove that, by introducing transferable prototypes, our method significantly outperforms the state-of-the-art methods, achieving an absolute 3.95% F1 increase over the best baseline.

One key property in recommender systems is the long-tail distribution in user-item interactions where most items only have few user feedback. Improving the recommendation of tail items can promote novelty and bring positive effects to both users and providers, and thus is a desirable property of recommender systems. Current novel recommendation methods over-emphasize the importance of tail items without differentiating the degree of users’ intent on popularity and often incur a sharp decline of accuracy. Moreover, none of existing studies has ever taken the extreme case of tail items, i.e., cold-start items without any interaction, into consideration. In this work, we first disclose the mechanism that drives a user's interaction towards popular or niche items by disentangling her intent into conformity influence (popularity) and personal interests (preference). We then present a unified end-to-end framework to simultaneously optimize accuracy and novelty targets based on the disentangled intent of popularity and that of preference. We further develop a new paradigm for novel recommendation of cold-start items which exploits the self-supervised learning technique to model the correlation between collaborative features and content features. We conduct extensive experiments on three real-world datasets. The results demonstrate that our proposed model yields significant improvements over the state-of-the-art baselines in terms of the trade-off between accuracy and novelty.