scholarly journals DSINE: Deep Structural Influence Learning via Network Embedding

2019 ◽  
Vol 33 ◽  
pp. 10065-10066 ◽  
Author(s):  
Jianjun Wu ◽  
Ying Sha ◽  
Bo Jiang ◽  
Jianlong Tan
Keyword(s):  
Social Influence ◽  
Real World ◽  
Local Structure ◽  
State Of The Art ◽  
Viral Marketing ◽  
Network Embedding ◽  
Proposed Model ◽  
Real World Datasets ◽  
Global And Local ◽  
Structural Influence

Structural representations of user social influence are critical for a variety of applications such as viral marketing and recommendation products. However, existing studies only focus on capturing and preserving the structure of relations, and ignore the diversity of influence relations patterns among users. To this end, we propose a deep structural influence learning model to learn social influence structure via mining rich features of each user, and fuse information from the aligned selfnetwork component for preserving global and local structure of the influence relations among users. Experiments on two real-world datasets demonstrate that the proposed model outperforms the state-of-the-art algorithms for learning rich representations in multi-label classification task.

scholarly journals Discrete Trust-aware Matrix Factorization for Fast Recommendation

2019 ◽  
Author(s):  
Guibing Guo ◽  
Enneng Yang ◽  
Li Shen ◽  
Xiaochun Yang ◽  
Xiaodong He
Keyword(s):  
Social Influence ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Social Relations ◽  
Real World ◽  
Matrix Factorization ◽  
State Of The Art ◽  
Proposed Model ◽  
Hamming Space ◽  
Real World Datasets

Trust-aware recommender systems have received much attention recently for their abilities to capture the influence among connected users. However, they suffer from the efficiency issue due to large amount of data and time-consuming real-valued operations. Although existing discrete collaborative filtering may alleviate this issue to some extent, it is unable to accommodate social influence. In this paper we propose a discrete trust-aware matrix factorization (DTMF) model to take dual advantages of both social relations and discrete technique for fast recommendation. Specifically, we map the latent representation of users and items into a joint hamming space by recovering the rating and trust interactions between users and items. We adopt a sophisticated discrete coordinate descent (DCD) approach to optimize our proposed model. In addition, experiments on two real-world datasets demonstrate the superiority of our approach against other state-of-the-art approaches in terms of ranking accuracy and efficiency.

scholarly journals Exploring Periodicity and Interactivity in Multi-Interest Framework for Sequential Recommendation

2021 ◽  
Author(s):  
Gaode Chen ◽  
Xinghua Zhang ◽  
Yanyan Zhao ◽  
Cong Xue ◽  
Ji Xiang
Keyword(s):  
Real World ◽  
Information Overload ◽  
State Of The Art ◽  
Recommendation Systems ◽  
Time Interval ◽  
Interest Representation ◽  
Novel Method ◽  
Real World Datasets ◽  
Item Representation ◽  
Global And Local

Sequential recommendation systems alleviate the problem of information overload, and have attracted increasing attention in the literature. Most prior works usually obtain an overall representation based on the user’s behavior sequence, which can not sufficiently reflect the multiple interests of the user. To this end, we propose a novel method called PIMI to mitigate this issue. PIMI can model the user’s multi-interest representation effectively by considering both the periodicity and interactivity in the item sequence. Specifically, we design a periodicity-aware module to utilize the time interval information between user’s behaviors. Meanwhile, an ingenious graph is proposed to enhance the interactivity between items in user’s behavior sequence, which can capture both global and local item features. Finally, a multi-interest extraction module is applied to describe user’s multiple interests based on the obtained item representation. Extensive experiments on two real-world datasets Amazon and Taobao show that PIMI outperforms state-of-the-art methods consistently.

scholarly journals 3-in-1 Correlated Embedding via Adaptive Exploration of the Structure and Semantic Subspaces

2018 ◽  
Author(s):  
Liang Yang ◽  
Yuanfang Guo ◽  
Di Jin ◽  
Huazhu Fu ◽  
Xiaochun Cao
Keyword(s):  
Real World ◽  
Posterior Distribution ◽  
State Of The Art ◽  
Variational Inference ◽  
Experimental Results ◽  
Generation Process ◽  
Network Embedding ◽  
Topological Information ◽  
Proposed Model ◽  
Types Of Information

Combinational  network embedding, which learns the node representation by exploring both  topological and non-topological information, becomes popular due to the fact that the two types of information are complementing each other.  Most of the existing methods either consider the  topological and non-topological  information being aligned or possess predetermined preferences during the embedding process.Unfortunately, previous methods  fail to either explicitly describe the correlations between topological and non-topological information or adaptively weight their impacts. To address the existing issues, three new assumptions are proposed to better describe the embedding space and its properties. With the proposed assumptions, nodes, communities and topics are mapped into one embedding space. A novel generative model is proposed to formulate the generation process of the network and content from the embeddings, with respect to the Bayesian framework. The proposed model automatically leans to the information which is more discriminative.The embedding result can be obtained by maximizing the posterior distribution by adopting the variational inference and reparameterization trick. Experimental results indicate that the proposed method gives superior performances compared to the state-of-the-art methods when a variety of real-world networks is analyzed.

scholarly journals Inferring Substitutable Products with Deep Network Embedding

2019 ◽  
Author(s):  
Shijie Zhang ◽  
Hongzhi Yin ◽  
Qinyong Wang ◽  
Tong Chen ◽  
Hongxu Chen ◽  
...  
Keyword(s):  
Semantic Similarity ◽  
Real World ◽  
State Of The Art ◽  
Network Embedding ◽  
Online Transactions ◽  
First Order ◽  
Substitutable Products ◽  
Deep Embedding ◽  
Real World Datasets ◽  
Explicit Feedback

On E-commerce platforms, understanding the relationships (e.g., substitute and complement) among products from user's explicit feedback, such as users' online transactions, is of great importance to boost extra sales. However, the significance of such relationships is usually neglected by existing recommender systems. In this paper, we propose a semisupervised deep embedding model, namely, Substitute Products Embedding Model (SPEM), which models the substitutable relationships between products by preserving the second-order proximity, negative first-order proximity and semantic similarity in a product co-purchasing graph based on user's purchasing behaviours. With SPEM, the learned representations of two substitutable products align closely in the latent embedding space. Extensive experiments on real-world datasets are conducted, and the results verify that our model outperforms state-of-the-art baselines.

scholarly journals Understanding Users' Budgets for Recommendation with Hierarchical Poisson Factorization

2017 ◽  
Author(s):  
Yunhui Guo ◽  
Congfu Xu ◽  
Hanzhang Song ◽  
Xin Wang
Keyword(s):  
Recommender Systems ◽  
Real World ◽  
Online Shopping ◽  
State Of The Art ◽  
Generative Model ◽  
Personal Consumption ◽  
Proposed Model ◽  
Recommendation Algorithms ◽  
Real World Datasets ◽  
Consumption Habits

People consume and rate products in online shopping websites. The historical purchases of customers reflect their personal consumption habits and indicate their future shopping behaviors. Traditional preference-based recommender systems try to provide recommendations by analyzing users' feedback such as ratings and clicks. But unfortunately, most of the existing recommendation algorithms ignore the budget of the users. So they cannot avoid recommending users with products that will exceed their budgets. And they also cannot understand how the users will assign their budgets to different products. In this paper, we develop a generative model named collaborative budget-aware Poisson factorization (CBPF) to connect users' ratings and budgets. The CBPF model is intuitive and highly interpretable. We compare the proposed model with several state-of-the-art budget-unaware recommendation methods on several real-world datasets. The results show the advantage of uncovering users' budgets for recommendation.

scholarly journals Neighborhood-Aware Attentional Representation for Multilingual Knowledge Graphs

2019 ◽  
Author(s):  
Qiannan Zhu ◽  
Xiaofei Zhou ◽  
Jia Wu ◽  
Jianlong Tan ◽  
Li Guo
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Attention Mechanism ◽  
Feature Representations ◽  
Proposed Model ◽  
Level Information ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Representation Method ◽  
Weighted Combination

Multilingual knowledge graphs constructed by entity alignment are the indispensable resources for numerous AI-related applications. Most existing entity alignment methods only use the triplet-based knowledge to find the aligned entities across multilingual knowledge graphs, they usually ignore the neighborhood subgraph knowledge of entities that implies more richer alignment information for aligning entities. In this paper, we incorporate neighborhood subgraph-level information of entities, and propose a neighborhood-aware attentional representation method NAEA for multilingual knowledge graphs. NAEA devises an attention mechanism to learn neighbor-level representation by aggregating neighbors' representations with a weighted combination. The attention mechanism enables entities not only capture different impacts of their neighbors on themselves, but also attend over their neighbors' feature representations with different importance. We evaluate our model on two real-world datasets DBP15K and DWY100K, and the experimental results show that the proposed model NAEA significantly and consistently outperforms state-of-the-art entity alignment models.

scholarly journals Multi-agent Attentional Activity Recognition

2019 ◽  
Author(s):  
Kaixuan Chen ◽  
Lina Yao ◽  
Dalin Zhang ◽  
Bin Guo ◽  
Zhiwen Yu
Keyword(s):  
Activity Recognition ◽  
State Of The Art ◽  
Body Part ◽  
Body Parts ◽  
Temporal Attention ◽  
Attention Model ◽  
Proposed Model ◽  
Collective Motions ◽  
Multi Agent ◽  
Real World Datasets

Multi-modality is an important feature of sensor based activity recognition. In this work, we consider two inherent characteristics of human activities, the spatially-temporally varying salience of features and the relations between activities and corresponding body part motions. Based on these, we propose a multi-agent spatial-temporal attention model. The spatial-temporal attention mechanism helps intelligently select informative modalities and their active periods. And the multiple agents in the proposed model represent activities with collective motions across body parts by independently selecting modalities associated with single motions. With a joint recognition goal, the agents share gained information and coordinate their selection policies to learn the optimal recognition model. The experimental results on four real-world datasets demonstrate that the proposed model outperforms the state-of-the-art methods.

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 Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 19-26 ◽  
Author(s):  
Chong Chen ◽  
Min Zhang ◽  
Yongfeng Zhang ◽  
Weizhi Ma ◽  
Yiqun Liu ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Real World ◽  
Large Scale ◽  
State Of The Art ◽  
Heterogeneous Data ◽  
Model Parameters ◽  
Online Systems ◽  
Practical Applications ◽  
Real World Datasets ◽  
Primary Type

Recent studies on recommendation have largely focused on exploring state-of-the-art neural networks to improve the expressiveness of models, while typically apply the Negative Sampling (NS) strategy for efficient learning. Despite effectiveness, two important issues have not been well-considered in existing methods: 1) NS suffers from dramatic fluctuation, making sampling-based methods difficult to achieve the optimal ranking performance in practical applications; 2) although heterogeneous feedback (e.g., view, click, and purchase) is widespread in many online systems, most existing methods leverage only one primary type of user feedback such as purchase. In this work, we propose a novel non-sampling transfer learning solution, named Efficient Heterogeneous Collaborative Filtering (EHCF) for Top-N recommendation. It can not only model fine-grained user-item relations, but also efficiently learn model parameters from the whole heterogeneous data (including all unlabeled data) with a rather low time complexity. Extensive experiments on three real-world datasets show that EHCF significantly outperforms state-of-the-art recommendation methods in both traditional (single-behavior) and heterogeneous scenarios. Moreover, EHCF shows significant improvements in training efficiency, making it more applicable to real-world large-scale systems. Our implementation has been released 1 to facilitate further developments on efficient whole-data based neural methods.

Expert Detection and Recommendation Model With User-Generated Tags in Collaborative Tagging Systems

2020 ◽  
Vol 31 (4) ◽  
pp. 24-45
Author(s):  
Mengmeng Shen ◽  
Jun Wang ◽  
Ou Liu ◽  
Haiying Wang
Keyword(s):  
Collaborative Filtering ◽  
Community Detection ◽  
Real World ◽  
Agglomerative Hierarchical Clustering ◽  
Collaborative Tagging ◽  
Excellent Performance ◽  
Recommendation Algorithm ◽  
Proposed Model ◽  
Target User ◽  
Real World Datasets

Tags generated in collaborative tagging systems (CTSs) may help users describe, categorize, search, discover, and navigate content, whereas the difficulty is how to go beyond the information explosion and obtain experts and the required information quickly and accurately. This paper proposes an expert detection and recommendation (EDAR) model based on semantics of tags; the framework consists of community detection and EDAR. Specifically, this paper firstly mines communities based on an improved agglomerative hierarchical clustering (I-AHC) to cluster tags and then presents a community expert detection (CED) algorithm for identifying community experts, and finally, an expert recommendation algorithm is proposed based the improved collaborative filtering (CF) algorithm to recommend relevant experts for the target user. Experiments are carried out on real world datasets, and the results from data experiments and user evaluations have shown that the proposed model can provide excellent performance compared to the benchmark method.

Sign in / Sign up

Export Citation Format

Share Document