scholarly journals Identifying the Relatedness between Tourism Attractions from Online Reviews with Heterogeneous Information Network Embedding

2021 ◽  
Vol 10 (12) ◽  
pp. 797
Author(s):  
Peiyuan Qiu ◽  
Jialiang Gao ◽  
Feng Lu
Keyword(s):  
Topic Model ◽  
Online Reviews ◽  
Information Network ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Mass Tourism ◽  
Different Types ◽  
Different Dimensions ◽  
Low Dimensional ◽  
Tourism Attractions

The relatedness between tourism attractions can be used in a variety of tourism applications, such as destination collaboration, commercial marketing, travel recommendations, and so on. Existing studies have identified the relatedness between attractions through measuring their co-occurrence—these attractions are mentioned in a text at the same time—extracted from online tourism reviews. However, the implicit semantic information in these reviews, which definitely contributes to modelling the relatedness from a more comprehensive perspective, is ignored due to the difficulty of quantifying the importance of different dimensions of information and fusing them. In this study, we considered both the co-occurrence and images of attractions and introduce a heterogeneous information network (HIN) to reorganize the online reviews representing this information, and then used HIN embedding to comprehensively identify the relatedness between attractions. First, an online review-oriented HIN was designed to form the different types of elements in the reviews. Second, a topic model was employed to extract the nodes of the HIN from the review texts. Third, an HIN embedding model was used to capture the semantics in the HIN, which comprehensively represents the attractions with low-dimensional vectors. Finally, the relatedness between attractions was identified by calculating the similarity of their vectors. The method was validated with mass tourism reviews from the popular online platform MaFengWo. It is argued that the proposed HIN effectively expresses the semantics of attraction co-occurrences and attraction images in reviews, and the HIN embedding captures the differences in these semantics, which facilitates the identification of the relatedness between attractions.

scholarly journals Relation Structure-Aware Heterogeneous Information Network Embedding

2019 ◽  
Vol 33 ◽  
pp. 4456-4463 ◽  
Author(s):  
Yuanfu Lu ◽  
Chuan Shi ◽  
Linmei Hu ◽  
Zhiyuan Liu
Keyword(s):  
Real World ◽  
Dimensional Space ◽  
Structural Characteristics ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Real World Datasets ◽  
Low Dimensional ◽  
Embedding Methods

Heterogeneous information network (HIN) embedding aims to embed multiple types of nodes into a low-dimensional space. Although most existing HIN embedding methods consider heterogeneous relations in HINs, they usually employ one single model for all relations without distinction, which inevitably restricts the capability of network embedding. In this paper, we take the structural characteristics of heterogeneous relations into consideration and propose a novel Relation structure-aware Heterogeneous Information Network Embedding model (RHINE). By exploring the real-world networks with thorough mathematical analysis, we present two structure-related measures which can consistently distinguish heterogeneous relations into two categories: Affiliation Relations (ARs) and Interaction Relations (IRs). To respect the distinctive characteristics of relations, in our RHINE, we propose different models specifically tailored to handle ARs and IRs, which can better capture the structures and semantics of the networks. At last, we combine and optimize these models in a unified and elegant manner. Extensive experiments on three real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods in various tasks, including node clustering, link prediction, and node classification.

scholarly journals Network Embedding-Based Approach for Detecting Collusive Spamming Groups on E-Commerce Platforms

2022 ◽  
Vol 2022 ◽  
pp. 1-13
Author(s):  
Jinbo Chao ◽  
Chunhui Zhao ◽  
Fuzhi Zhang
Keyword(s):  
Information Network ◽  
Clustering Methods ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Fair Competition ◽  
Key Issues ◽  
Low Dimensional ◽  
Vector Representations ◽  
Fake Reviews

Information security is one of the key issues in e-commerce Internet of Things (IoT) platform research. The collusive spamming groups on e-commerce platforms can write a large number of fake reviews over a period of time for the evaluated products, which seriously affect the purchase decision behaviors of consumers and destroy the fair competition environment among merchants. To address this problem, we propose a network embedding based approach to detect collusive spamming groups. First, we use the idea of a meta-graph to construct a heterogeneous information network based on the user review dataset. Second, we exploit the modified DeepWalk algorithm to learn the low-dimensional vector representations of user nodes in the heterogeneous information network and employ the clustering methods to obtain candidate spamming groups. Finally, we leverage an indicator weighting strategy to calculate the spamming score of each candidate group, and the top-k groups with high spamming scores are considered to be the collusive spamming groups. The experimental results on two real-world review datasets show that the overall detection performance of the proposed approach is much better than that of baseline methods.

scholarly journals Temporal Heterogeneous Information Network Embedding

2021 ◽  
Author(s):  
Hong Huang ◽  
Ruize Shi ◽  
Wei Zhou ◽  
Xiao Wang ◽  
Hai Jin ◽  
...  
Keyword(s):  
Temporal Dynamics ◽  
Information Network ◽  
Hawkes Process ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Meta Path ◽  
Low Dimensional ◽  
Type Node ◽  
Link Recommendation ◽  
Node Embeddings

Heterogeneous information network (HIN) embedding, learning the low-dimensional representation of multi-type nodes, has been applied widely and achieved excellent performance. However, most of the previous works focus more on static heterogeneous networks or learning node embedding within specific snapshots, and seldom attention has been paid to the whole evolution process and capturing all temporal dynamics. In order to fill the gap of obtaining multi-type node embeddings by considering all temporal dynamics during the evolution, we propose a novel temporal HIN embedding method (THINE). THINE not only uses attention mechanism and meta-path to preserve structures and semantics in HIN but also combines the Hawkes process to simulate the evolution of the temporal network. Our extensive evaluations with various real-world temporal HINs demonstrate that THINE achieves state-of-the-art performance in both static and dynamic tasks, including node classification, link prediction, and temporal link recommendation.

scholarly journals Side Information Fusion for Recommender Systems over Heterogeneous Information Network

2021 ◽  
Vol 15 (4) ◽  
pp. 1-32
Author(s):  
Huan Zhao ◽  
Quanming Yao ◽  
Yangqiu Song ◽  
James T. Kwok ◽  
Dik Lun Lee
Keyword(s):  
Information Fusion ◽  
Side Information ◽  
Low Rank ◽  
Information Network ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Different Types ◽  
Latent Features ◽  
Real World Datasets ◽  
Types Of Information

Collaborative filtering (CF) has been one of the most important and popular recommendation methods, which aims at predicting users’ preferences (ratings) based on their past behaviors. Recently, various types of side information beyond the explicit ratings users give to items, such as social connections among users and metadata of items, have been introduced into CF and shown to be useful for improving recommendation performance. However, previous works process different types of information separately, thus failing to capture the correlations that might exist across them. To address this problem, in this work, we study the application of heterogeneous information network (HIN), which offers a unifying and flexible representation of different types of side information, to enhance CF-based recommendation methods. However, we face challenging issues in HIN-based recommendation, i.e., how to capture similarities of complex semantics between users and items in a HIN, and how to effectively fuse these similarities to improve final recommendation performance. To address these issues, we apply metagraph to similarity computation and solve the information fusion problem with a “matrix factorization (MF) + factorization machine (FM)” framework. For the MF part, we obtain the user-item similarity matrix from each metagraph and then apply low-rank matrix approximation to obtain latent features for both users and items. For the FM part, we apply FM with Group lasso (FMG) on the features obtained from the MF part to train the recommending model and, at the same time, identify the useful metagraphs. Besides FMG, a two-stage method, we further propose an end-to-end method, hierarchical attention fusing, to fuse metagraph-based similarities for the final recommendation. Experimental results on four large real-world datasets show that the two proposed frameworks significantly outperform existing state-of-the-art methods in terms of recommendation performance.

W-MMP2Vec: Topic-driven network embedding model for link prediction in content-based heterogeneous information network

2021 ◽  
Vol 25 (3) ◽  
pp. 711-738
Author(s):  
Phu Pham ◽  
Phuc Do
Keyword(s):  
Link Prediction ◽  
Representation Learning ◽  
Information Network ◽  
Network Embedding ◽  
Heterogeneous Information Network ◽  
Heterogeneous Information ◽  
Learning Framework ◽  
Novel Approach ◽  
Proposed Model ◽  
Meta Path

Link prediction on heterogeneous information network (HIN) is considered as a challenge problem due to the complexity and diversity in types of nodes and links. Currently, there are remained challenges of meta-path-based link prediction in HIN. Previous works of link prediction in HIN via network embedding approach are mainly focused on exploiting features of node rather than existing relations in forms of meta-paths between nodes. In fact, predicting the existence of new links between non-linked nodes is absolutely inconvincible. Moreover, recent HIN-based embedding models also lack of thorough evaluations on the topic similarity between text-based nodes along given meta-paths. To tackle these challenges, in this paper, we proposed a novel approach of topic-driven multiple meta-path-based HIN representation learning framework, namely W-MMP2Vec. Our model leverages the quality of node representations by combining multiple meta-paths as well as calculating the topic similarity weight for each meta-path during the processes of network embedding learning in content-based HINs. To validate our approach, we apply W-TMP2Vec model in solving several link prediction tasks in both content-based and non-content-based HINs (DBLP, IMDB and BlogCatalog). The experimental outputs demonstrate the effectiveness of proposed model which outperforms recent state-of-the-art HIN representation learning models.

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