Sequential recommendation seeks to predict users' next behaviors and recommend related items over time. Existing research has mainly focused on modeling users' dynamic preferences from their sequential behaviors. However, most of these studies have ignored the negative effects of noise behaviors in the given sequences, which may mislead the recommender. In addition, users' behavior data is always sparse, which makes it difficult to effectively learn users' preferences purely from their historical behaviors. Most recently, knowledge graphs (KGs) have been exploited by few researchers for sequential recommendation. However, they always assume all information in KGs or KG paths with limited length are useful for recommendation, which may bring irrelevant information from KGs into the recommender and further mislead the recommender. To address these issues, we propose a novel KG-based behavior denoising and preference learning model named KGDPL for sequential recommendation. We argue that the paths in KGs that reflect semantic relations between entities can not only help to remove noise behaviors and recommend successive items for users, but also provide relevant explanations. Therefore, we first devise a supervised knowledge path selection module to select effective paths between items from KGs for behavior prediction, which aims to filter out irrelevant information from KGs for the given recommendation task. Then, we design a knowledge-enhanced behavior denoising module to mitigate the negative effects of the noise behaviors contained in historical sequences by using the knowledge path information. After that, we propose a knowledge-enhanced preference learning module to better learn users' personalized and dynamic preferences from their historical behavior sequences and related knowledge information, which can also help tag users and provide explanations for recommendation results. Experimental results on four real-world datasets demonstrate the effectiveness and interpretability of the proposed model KGDPL.

Entity alignment which aims at linking entities with the same meaning from different knowledge graphs (KGs) is a vital step for knowledge fusion. Existing research focused on learning embeddings of entities by utilizing structural information of KGs for entity alignment. These methods can aggregate information from neighboring nodes but may also bring noise from neighbors. Most recently, several researchers attempted to compare neighboring nodes in pairs to enhance the entity alignment. However, they ignored the relations between entities which are also important for neighborhood matching. In addition, existing methods paid less attention to the positive interactions between the entity alignment and the relation alignment. To deal with these issues, we propose a novel Relation-aware Neighborhood Matching model named RNM for entity alignment. Specifically, we propose to utilize the neighborhood matching to enhance the entity alignment. Besides comparing neighbor nodes when matching neighborhood, we also try to explore useful information from the connected relations. Moreover, an iterative framework is designed to leverage the positive interactions between the entity alignment and the relation alignment in a semi-supervised manner. Experimental results on three real-world datasets demonstrate that the proposed model RNM performs better than state-of-the-art methods.

Online reviews, which contain the quality information and user experience about products, always affect the consumption decisions of customers. Unfortunately, quite a number of spammers attempt to mislead consumers by writing fake reviews for some intents. Existing methods for detecting spam reviews mainly focus on constructing discriminative features, which heavily depend on experts and may miss some complex but effective features. Recently, some models attempt to learn the latent representations of reviews, users, and items. However, the learned embeddings usually lack interpretability. Moreover, most of existing methods are based on single classification model while ignoring the complementarity of different classification models.

Yao Zhu, Hongzhi Liu, Zhonghai Wu, Yang Song, Tao Zhang. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.