The era of big textual corpora and machine learning technologies have paved the way for researchers in numerous data mining fields. Among them, causality mining (CM) from textual data has become a significant area of concern and has more attention from researchers. Causality (cause-effect relations) serves as an essential category of relationships, which plays a significant role in question answering, future events predication, discourse comprehension, decision making, future scenario generation, medical text mining, behavior prediction, and textual prediction entailment. While, decades of development techniques for CM are still prone to performance enhancement, especially for ambiguous and implicitly expressed causalities. The ineffectiveness of the early attempts is mainly due to small, ambiguous, heterogeneous, and domain-specific datasets constructed by manually linguistic and syntactic rules. Many researchers have deployed shallow machine learning (ML) and deep learning (DL) techniques to deal with such datasets, and they achieved satisfactory performance. In this survey, an effort has been made to address a comprehensive review of some state-of-the-art shallow ML and DL approaches in CM. We present a detailed taxonomy of CM and discuss popular ML and DL approaches with their comparative weaknesses and strengths, applications, popular datasets, and frameworks. Lastly, the future research challenges are discussed with illustrations of how to transform them into productive future research directions.

KSII Transactions on Internet and Information Systems TIIS Paper Details

The aim of explainable recommendation is not only to provide recommended items to users, but also to make users aware of why these items are recommended. Traditional recommendation methods infer user preferences for items using user–item rating information. However, the expressive power of latent representations of users and items is relatively limited due to the sparseness of the user–item rating matrix. Heterogeneous information networks (HIN) provide contextual information for improving recommendation performance and interpreting the interactions between users and items. However, due to the heterogeneity and complexity of context information in HIN, it is still a challenge to integrate this contextual information into explainable recommendation systems effectively. In this paper, we propose a novel framework—the dual-attention networks for explainable recommendation (DANER) in HINs. We first used multiple meta-paths to capture high-order semantic relations between users and items in HIN for generating similarity matrices, and then utilized matrix decomposition on similarity matrices to obtain low-dimensional sparse representations of users and items. Secondly, we introduced two-level attention networks, namely a local attention network and a global attention network, to integrate the representations of users and items from different meta-paths for obtaining high-quality representations. Finally, we use a standard multi-layer perceptron to model the interactions between users and items, which predict users’ ratings of items. Furthermore, the dual-attention mechanism also contributes to identifying critical meta-paths to generate relevant explanations for users. Comprehensive experiments on two real-world datasets demonstrate the effectiveness of DANER on recommendation performance as compared with the state-of-the-art methods. A case study illustrates the interpretability of DANER.

As a data augmentation method, masking word is commonly used in many natural language processing tasks. However, most mask methods are based on rules and are not related to downstream tasks. In this paper, we propose a novel masking word generator, named Actor-Critic Mask Model (ACMM), which can adaptively adjust the mask strategy according to the performance of downstream tasks. In order to demonstrate the effectiveness of the method, we conducted experiments on two causal event extraction datasets. Experiment results show that, compared with various rule-based masking methods, the masked sentences generated by our proposed method can significantly enhance the generalization of the model and improve the model performance.

Recently, with the triumph of deep learning, attention mechanism, and graph convolutional networks in their respective fields, using new representation learning techniques or introducing auxiliary information to improve the representation ability of embedding has become the core content of the recommendation algorithm research. Generally, most existing GNN-based recommendation methods recursively propagate embedding information on the graph structure and capture collaborative signals by exploring the high-level connectivity between users and items. Despite the great success, those methods do not consider the influence of temporal context on user preferences embedding information propagation, nor do they distinguish the contribution of different neighbor node information to the target node. In order to address the two problems, we propose a graph collaborative filtering model TAGCF combing time factors and attention based on the existing method. The model uses the time factor to integrate temporal information into the process of embedding information propagation and uses the attention mechanism to distinguish the influence of embedding information from different neighbors. The effectiveness of TAGCF, time information, and attention mechanism are verified through comparative experiments with multiple baseline methods on the two recommendation system datasets, MovieLens and Amazon-books.