HINRL4Rec: A Novel Integrated Heterogeneous Network Embedding with Reinforcement Learning for Recommendation

Proposed Model ◽

Meta Path ◽

Rich Information ◽

The Rich ◽

Real World Datasets

Abstract Recent KG-oriented recommendation techniques mainly focus on the direct interaction between entities in the given KGs as the rich information sources for leveraging the quality of recommendation outputs. However, they are still hindered by the heterogeneity, type-varied entities and their relationships in knowledge graphs (KG) as the heterogeneous information networks (HIN). This limitation seems challenging to build up an effective approach for the KG-based recommendation system in both semantic path-based exploitation and heterogeneous information extraction. To meet these challenges, we proposed a novel integrated HIN embedding with reinforcement learning (RL)-based feature engineering for recommendation, called as: HINRL4Rec. First of all, we apply the combined textual meta-path-based embedding approach for learning multiple-rich-schematic representations of user/item and their associated entities. Then, these extracted multi-typed embeddings of user and item entities are fused into the unified embedding spaces during the KG embedding process. Finally, the unified representations of users and items are then used to facilitate the RL-based policy-driven searching process in the next steps for performing the recommendation task. Extensive experiments in real-world datasets demonstrate the effectiveness of our proposed model in comparing with recent state-of-the-art recommendation baselines.

TransPath: Representation Learning for Heterogeneous Information Networks via Translation Mechanism

10.20944/preprints201801.0147.v1 ◽

2018 ◽

Author(s):

Yang Fang ◽

Xiang Zhao ◽

Zhen Tan

Keyword(s):

Large Scale ◽

Representation Learning ◽

Information Networks ◽

Structure Information ◽

Network Representation ◽

Meta Path ◽

Translation Mechanism ◽

Real World Datasets

In this paper, we propose a novel network representation learning model TransPath to encode heterogeneous information networks (HINs). Traditional network representation learning models aim to learn the embeddings of a homogeneous network. TransPath is able to capture the rich semantic and structure information of a HIN via meta-paths. We take advantage of the concept of translation mechanism in knowledge graph which regards a meta-path, instead of an edge, as a translating operation from the first node to the last node. Moreover, we propose a user-guided meta-path sampling strategy which takes users' preference as a guidance, which could explore the semantics of a path more precisely, and meanwhile improve model efficiency via the avoidance of other noisy and meaningless meta-paths. We evaluate our model on two large-scale real-world datasets DBLP and YELP, and two benchmark tasks similarity search and node classification. We observe that TransPath outperforms other state-of-the-art baselines consistently and significantly.

A Deep Content-Based Model for Persian Rumor Verification

ACM Transactions on Asian and Low-Resource Language Information Processing ◽

10.1145/3487289 ◽

2022 ◽

Vol 21 (1) ◽

pp. 1-29

Author(s):

Zoleikha Jahanbakhsh-Nagadeh ◽

Mohammad-Reza Feizi-Derakhshi ◽

Arash Sharifi

Keyword(s):

Early Stage ◽

Structural Features ◽

Proposed Model ◽

The Social ◽

Rich Information ◽

The Rich ◽

Real World Datasets ◽

Rumor Detection ◽

Content Information

During the development of social media, there has been a transformation in social communication. Despite their positive applications in social interactions and news spread, it also provides an ideal platform for spreading rumors. Rumors can endanger the security of society in normal or critical situations. Therefore, it is important to detect and verify the rumors in the early stage of their spreading. Many research works have focused on social attributes in the social network to solve the problem of rumor detection and verification, while less attention has been paid to content features. The social and structural features of rumors develop over time and are not available in the early stage of rumor. Therefore, this study presented a content-based model to verify the Persian rumors on Twitter and Telegram early. The proposed model demonstrates the important role of content in spreading rumors and generates a better-integrated representation for each source rumor document by fusing its semantic, pragmatic, and syntactic information. First, contextual word embeddings of the source rumor are generated by a hybrid model based on ParsBERT and parallel CapsNets. Then, pragmatic and syntactic features of the rumor are extracted and concatenated with embeddings to capture the rich information for rumor verification. Experimental results on real-world datasets demonstrated that the proposed model significantly outperforms the state-of-the-art models in the early rumor verification task. Also, it can enhance the performance of the classifier from 2% to 11% on Twitter and from 5% to 23% on Telegram. These results validate the model's effectiveness when limited content information is available.

Reinforcement Learning Based Meta-Path Discovery in Large-Scale Heterogeneous Information Networks

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.6073 ◽

2020 ◽

Vol 34 (04) ◽

pp. 6094-6101

Author(s):

Guojia Wan ◽

Bo Du ◽

Shirui Pan ◽

Gholameza Haffari

Keyword(s):

Reinforcement Learning ◽

Domain Knowledge ◽

Large Scale ◽

Information Networks ◽

Superior Performance ◽

Lowest Common Ancestor ◽

Path Discovery ◽

Meta-paths are important tools for a wide variety of data mining and network analysis tasks in Heterogeneous Information Networks (HINs), due to their flexibility and interpretability to capture the complex semantic relation among objects. To date, most HIN analysis still relies on hand-crafting meta-paths, which requires rich domain knowledge that is extremely difficult to obtain in complex, large-scale, and schema-rich HINs. In this work, we present a novel framework, Meta-path Discovery with Reinforcement Learning (MPDRL), to identify informative meta-paths from complex and large-scale HINs. To capture different semantic information between objects, we propose a novel multi-hop reasoning strategy in a reinforcement learning framework which aims to infer the next promising relation that links a source entity to a target entity. To improve the efficiency, moreover, we develop a type context representation embedded approach to scale the RL framework to handle million-scale HINs. As multi-hop reasoning generates rich meta-paths with various length, we further perform a meta-path induction step to summarize the important meta-paths using Lowest Common Ancestor principle. Experimental results on two large-scale HINs, Yago and NELL, validate our approach and demonstrate that our algorithm not only achieves superior performance in the link prediction task, but also identifies useful meta-paths that would have been ignored by human experts.

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

Intelligent Data Analysis ◽

10.3233/ida-205168 ◽

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 ◽

Learning Framework ◽

Novel Approach ◽

Proposed Model ◽

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.

HeteClass: A Meta-path based framework for transductive classification of objects in heterogeneous information networks

Expert Systems with Applications ◽

10.1016/j.eswa.2016.10.013 ◽

2017 ◽

Vol 68 ◽

pp. 106-122 ◽

Cited By ~ 17

Author(s):

Mukul Gupta ◽

Pradeep Kumar ◽

Bharat Bhasker

Keyword(s):

Information Networks ◽

Multityped Community Discovery in Time-Evolving Heterogeneous Information Networks Based on Tensor Decomposition

Complexity ◽

10.1155/2018/9653404 ◽

2018 ◽

Vol 2018 ◽

pp. 1-16 ◽

Cited By ~ 1

Author(s):

Jibing Wu ◽

Lianfei Yu ◽

Qun Zhang ◽

Peiteng Shi ◽

Lihua Liu ◽

...

Keyword(s):

Real World ◽

Tensor Decomposition ◽

Information Networks ◽

Community Discovery ◽

Star Network ◽

General Network ◽

Real World Datasets ◽

Discovery Method

The heterogeneous information networks are omnipresent in real-world applications, which consist of multiple types of objects with various rich semantic meaningful links among them. Community discovery is an effective method to extract the hidden structures in networks. Usually, heterogeneous information networks are time-evolving, whose objects and links are dynamic and varying gradually. In such time-evolving heterogeneous information networks, community discovery is a challenging topic and quite more difficult than that in traditional static homogeneous information networks. In contrast to communities in traditional approaches, which only contain one type of objects and links, communities in heterogeneous information networks contain multiple types of dynamic objects and links. Recently, some studies focus on dynamic heterogeneous information networks and achieve some satisfactory results. However, they assume that heterogeneous information networks usually follow some simple schemas, such as bityped network and star network schema. In this paper, we propose a multityped community discovery method for time-evolving heterogeneous information networks with general network schemas. A tensor decomposition framework, which integrates tensor CP factorization with a temporal evolution regularization term, is designed to model the multityped communities and address their evolution. Experimental results on both synthetic and real-world datasets demonstrate the efficiency of our framework.

Automatic Generation of Meta-Path Graph for Concept Recommendation in MOOCs

Electronics ◽

10.3390/electronics10141671 ◽

2021 ◽

Vol 10 (14) ◽

pp. 1671

Author(s):

Jibing Gong ◽

Cheng Wang ◽

Zhiyong Zhao ◽

Xinghao Zhang

Keyword(s):

Auxiliary Information ◽

Automatic Generation ◽

Information Network ◽

Heterogeneous Information Network ◽

Online Study ◽

Path Graph ◽

Proposed Model ◽

In MOOCs, generally speaking, curriculum designing, course selection, and knowledge concept recommendation are the three major steps that systematically instruct users to learn. This paper focuses on the knowledge concept recommendation in MOOCs, which recommends related topics to users to facilitate their online study. The existing approaches only consider the historical behaviors of users, but ignore various kinds of auxiliary information, which are also critical for user embedding. In addition, traditional recommendation models only consider the immediate user response to the recommended items, and do not explicitly consider the long-term interests of users. To deal with the above issues, this paper proposes AGMKRec, a novel reinforced concept recommendation model with a heterogeneous information network. We first clarify the concept recommendation in MOOCs as a reinforcement learning problem to offer a personalized and dynamic knowledge concept label list to users. To consider more auxiliary information of users, we construct a heterogeneous information network among users, courses, and concepts, and use a meta-path-based method which can automatically identify useful meta-paths and multi-hop connections to learn a new graph structure for learning effective node representations on a graph. Comprehensive experiments and analyses on a real-world dataset collected from XuetangX show that our proposed model outperforms some state-of-the-art methods.