scholarly journals DA-GCN: A Domain-aware Attentive Graph Convolution Network for Shared-account Cross-domain Sequential Recommendation

2021 ◽  
Author(s):  
Lei Guo ◽  
Li Tang ◽  
Tong Chen ◽  
Lei Zhu ◽  
Quoc Viet Hung Nguyen ◽  
...  
Keyword(s):  
Real World ◽  
Message Passing ◽  
Sequential Patterns ◽  
Graph Structure ◽  
Domain Specific ◽  
User Behaviors ◽  
Cross Domain ◽  
Latent Space ◽  
Real World Datasets ◽  
Multiple Domains

Shared-account Cross-domain Sequential Recommendation (SCSR) is the task of recommending the next item based on a sequence of recorded user behaviors, where multiple users share a single account, and their behaviours are available in multiple domains. Existing work on solving SCSR mainly relies on mining sequential patterns via RNN-based models, which are not expressive enough to capture the relationships among multiple entities. Moreover, all existing algorithms try to bridge two domains via knowledge transfer in the latent space, and the explicit cross-domain graph structure is unexploited. In this work, we propose a novel graph-based solution, namely DA-GCN, to address the above challenges. Specifically, we first link users and items in each domain as a graph. Then, we devise a domain-aware graph convolution network to learn user-specific node representations. To fully account for users' domain-specific preferences on items, two novel attention mechanisms are further developed to selectively guide the message passing process. Extensive experiments on two real-world datasets are conducted to demonstrate the superiority of our DA-GCN method.

scholarly journals Cross-Domain Recommendation via Coupled Factorization Machines

2019 ◽  
Vol 33 ◽  
pp. 9965-9966 ◽  
Author(s):  
Lile Li ◽  
Quan Do ◽  
Wei Liu
Keyword(s):  
Recommender Systems ◽  
Real World ◽  
Original Data ◽  
Data Sets ◽  
Latent Factors ◽  
Domain Specific ◽  
Coupled Mode ◽  
Cross Domain ◽  
Factorization Machine ◽  
Real World Datasets

Data across many business domains can be represented by two or more coupled data sets. Correlations among these coupled datasets have been studied in the literature for making more accurate cross-domain recommender systems. However, in existing methods, cross-domain recommendations mostly assume the coupled mode of data sets share identical latent factors, which limits the discovery of potentially useful domain-specific properties of the original data. In this paper, we proposed a novel cross-domain recommendation method called Coupled Factorization Machine (CoFM) that addresses this limitation. Compared to existing models, our research is the first model that uses factorization machines to capture both common characteristics of coupled domains while simultaneously preserving the differences among them. Our experiments with real-world datasets confirm the advantages of our method in making across-domain recommendations.

scholarly journals Semi-supervised User Profiling with Heterogeneous Graph Attention Networks

2019 ◽  
Author(s):  
Weijian Chen ◽  
Yulong Gu ◽  
Zhaochun Ren ◽  
Xiangnan He ◽  
Hongtao Xie ◽  
...  
Keyword(s):  
Social Networks ◽  
Real World ◽  
User Profiling ◽  
Graph Structure ◽  
User Characteristics ◽  
Attention Networks ◽  
User Behaviors ◽  
Real World Applications ◽  
Purchase Behaviors ◽  
Personal Interests

Aiming to represent user characteristics and personal interests, the task of user profiling is playing an increasingly important role for many real-world applications, e.g., e-commerce and social networks platforms. By exploiting the data like texts and user behaviors, most existing solutions address user profiling as a classification task, where each user is formulated as an individual data instance. Nevertheless, a user's profile is not only reflected from her/his affiliated data, but also can be inferred from other users, e.g., the users that have similar co-purchase behaviors in e-commerce, the friends in social networks, etc. In this paper, we approach user profiling in a semi-supervised manner, developing a generic solution based on heterogeneous graph learning. On the graph, nodes represent the entities of interest (e.g., users, items, attributes of items, etc.), and edges represent the interactions between entities. Our heterogeneous graph attention networks (HGAT) method learns the representation for each entity by accounting for the graph structure, and exploits the attention mechanism to discriminate the importance of each neighbor entity. Through such a learning scheme, HGAT can leverage both unsupervised information and limited labels of users to build the predictor. Extensive experiments on a real-world e-commerce dataset verify the effectiveness and rationality of our HGAT for user profiling.

scholarly journals Auxiliary Template-Enhanced Generative Compatibility Modeling

2020 ◽  
Author(s):  
Jinhuan Liu ◽  
Xuemeng Song ◽  
Zhaochun Ren ◽  
Liqiang Nie ◽  
Zhaopeng Tu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real World ◽  
Economic Value ◽  
Fashion Industry ◽  
Learning Methods ◽  
Latent Space ◽  
Generation Network ◽  
Real World Datasets ◽  
Essential Problem ◽  
Item Item

In recent years, there has been a growing interest in the fashion analysis (e.g., clothing matching) due to the huge economic value of the fashion industry. The essential problem is to model the compatibility between the complementary fashion items, such as the top and bottom in clothing matching. The majority of existing work on fashion analysis has focused on measuring the item-item compatibility in a latent space with deep learning methods. In this work, we aim to improve the compatibility modeling by sketching a compatible template for a given item as an auxiliary link between fashion items. Specifically, we propose an end-to-end Auxiliary Template-enhanced Generative Compatibility Modeling (AT-GCM) scheme, which introduces an auxiliary complementary template generation network equipped with the pixel-wise consistency and compatible template regularization. Extensive experiments on two real-world datasets demonstrate the superiority of the proposed approach.

scholarly journals Cross-Domain Recommendation: An Embedding and Mapping Approach

2017 ◽  
Author(s):  
Tong Man ◽  
Huawei Shen ◽  
Xiaolong Jin ◽  
Xueqi Cheng
Keyword(s):  
State Of The Art ◽  
Mapping Function ◽  
Multi Layer Perceptron ◽  
Data Sparsity ◽  
Domain Specific ◽  
Cross Domain ◽  
Robustness To Noise ◽  
Promising Solution ◽  
High Flexibility ◽  
Multiple Domains

Data sparsity is one of the most challenging problems for recommender systems. One promising solution to this problem is cross-domain recommendation, i.e., leveraging feedbacks or ratings from multiple domains to improve recommendation performance in a collective manner. In this paper, we propose an Embedding and Mapping framework for Cross-Domain Recommendation, called EMCDR. The proposed EMCDR framework distinguishes itself from existing cross-domain recommendation models in two aspects. First, a multi-layer perceptron is used to capture the nonlinear mapping function across domains, which offers high flexibility for learning domain-specific features of entities in each domain. Second, only the entities with sufficient data are used to learn the mapping function, guaranteeing its robustness to noise caused by data sparsity in single domain. Extensive experiments on two cross-domain recommendation scenarios demonstrate that EMCDR significantly outperforms state-of-the-art cross-domain recommendation methods.

scholarly journals Concept Extraction and Prerequisite Relation Learning from Educational Data

2019 ◽  
Vol 33 ◽  
pp. 9678-9685 ◽  
Author(s):  
Weiming Lu ◽  
Yangfan Zhou ◽  
Jiale Yu ◽  
Chenhao Jia
Keyword(s):  
Real World ◽  
High Quality ◽  
Learning Materials ◽  
Concept Extraction ◽  
Learning Framework ◽  
Domain Specific ◽  
Concept Pair ◽  
Real World Datasets ◽  
Educational Applications ◽  
Relation Learning

Prerequisite relations among concepts are crucial for educational applications. However, it is difficult to automatically extract domain-specific concepts and learn the prerequisite relations among them without labeled data.In this paper, we first extract high-quality phrases from a set of educational data, and identify the domain-specific concepts by a graph based ranking method. Then, we propose an iterative prerequisite relation learning framework, called iPRL, which combines a learning based model and recovery based model to leverage both concept pair features and dependencies among learning materials. In experiments, we evaluated our approach on two real-world datasets Textbook Dataset and MOOC Dataset, and validated that our approach can achieve better performance than existing methods. Finally, we also illustrate some examples of our approach.

scholarly journals Pattern-enhanced Contrastive Policy Learning Network for Sequential Recommendation

2021 ◽  
Author(s):  
Xiaohai Tong ◽  
Pengfei Wang ◽  
Chenliang Li ◽  
Long Xia ◽  
Shaozhang Niu
Keyword(s):  
Real World ◽  
Decision Process ◽  
Target Item ◽  
Policy Learning ◽  
Sequential Patterns ◽  
Learning Mechanism ◽  
Learning Network ◽  
Markov Decision ◽  
Initial Item ◽  
Real World Datasets

Sequential recommendation aims to predict users’ future behaviors given their historical interactions. However, due to the randomness and diversity of a user’s behaviors, not all historical items are informative to tell his/her next choice. It is obvious that identifying relevant items and extracting meaningful sequential patterns are necessary for a better recommendation. Unfortunately, few works have focused on this sequence denoising process. In this paper, we propose a PatteRn-enhanced ContrAstive Policy Learning Network (RAP for short) for sequential recommendation, RAP formalizes the denoising problem in the form of Markov Decision Process (MDP), and sample actions for each item to determine whether it is relevant with the target item. To tackle the lack of relevance supervision, RAP fuses a series of mined sequential patterns into the policy learning process, which work as a prior knowledge to guide the denoising process. After that, RAP splits the initial item sequence into two disjoint subsequences: a positive subsequence and a negative subsequence. At this, a novel contrastive learning mechanism is introduced to guide the sequence denoising and achieve preference estimation from the positive subsequence simultaneously. Extensive experiments on four public real-world datasets demonstrate the effectiveness of our approach for sequential recommendation.

Categorization flexibility and unconventional choices: is life an adventure?

2020 ◽  
Vol 54 (8) ◽  
pp. 1963-1986
Author(s):  
Tilottama G. Chowdhury ◽  
Feisal Murshed
Keyword(s):  
Interactive Effect ◽  
Interactive Effects ◽  
Cognitive Capacity ◽  
Purchase Behavior ◽  
Consumer Decision Making ◽  
Cross Sectional ◽  
Content Type ◽  
Cross Domain ◽  
Multiple Domains

Purpose This paper proposes that categorization flexibility, operationalized as the cognitive capacity that cross-categorizes products in multiple situational categories across multiple domains, might favorably influence a consumer’s evaluation of unconventional options. Design/methodology/approach Experimental research design is used to test the theory. An exploratory study first establishes the effect of categorization flexibility in a non-food domain. Study 1 documents the moderating role of decision domain, showing that the effect works only under low- (vs high-) consequence domain. Studies 2A and 2B further refine the notion by showing that individuals can be primed in a relatively higher categorization flexibility frame of mind. Study 3 demonstrates the interactive effect of categorization flexibility and adventure priming in a high-consequence domain. Study 4 integrates the interactive effects of decisions with low- vs high-consequence, adventure priming and categorization flexibility within a single decision domain of high consequence. Findings Consumers with higher- (vs lower-) categorization flexibility tend to opt for unconventional choices when the decision domain entails low consequences, whereas such a result does not hold under decision domain of high consequences. The categorization flexibility effects in case of low-consequence decision domain holds true even when consumers are primed to be categorization flexible. Furthermore, with additional adventure priming, consumers show an increased preference for unconventional options even under a decision domain with high consequence. Research limitations/implications This study could not examine real purchase behavior as results are based on cross-sectional, behavioral intention data. In addition, it did not examine the underlying reason for presence of cross-domain categorization flexibility index. Practical implications The results suggest that stimuli may be tailored to consumers in ways that increase the salience and the perceived attractiveness of unconventional choices. Further, data reinforce the notion of cross-categorical interrelations among different domains, which could be leveraged by marketers. Originality/value This study represents the first documentation of the potential ways by which unconventional product choice might be a function of individuals’ categorization flexibility level across different types of decision domains. The findings yield implications that are novel to both categorization and consumer decision-making literature.

Time-Efficient Ensemble Learning with Sample Exchange for Edge Computing

2021 ◽  
Vol 21 (3) ◽  
pp. 1-17
Author(s):  
Wu Chen ◽  
Yong Yu ◽  
Keke Gai ◽  
Jiamou Liu ◽  
Kim-Kwang Raymond Choo
Keyword(s):  
Ensemble Learning ◽  
Real World ◽  
Interaction Mechanism ◽  
Training Model ◽  
Edge Computing ◽  
Learning Techniques ◽  
Multi Agent ◽  
Real World Datasets ◽  
Entire Dataset ◽  
Exchange Data

In existing ensemble learning algorithms (e.g., random forest), each base learner’s model needs the entire dataset for sampling and training. However, this may not be practical in many real-world applications, and it incurs additional computational costs. To achieve better efficiency, we propose a decentralized framework: Multi-Agent Ensemble. The framework leverages edge computing to facilitate ensemble learning techniques by focusing on the balancing of access restrictions (small sub-dataset) and accuracy enhancement. Specifically, network edge nodes (learners) are utilized to model classifications and predictions in our framework. Data is then distributed to multiple base learners who exchange data via an interaction mechanism to achieve improved prediction. The proposed approach relies on a training model rather than conventional centralized learning. Findings from the experimental evaluations using 20 real-world datasets suggest that Multi-Agent Ensemble outperforms other ensemble approaches in terms of accuracy even though the base learners require fewer samples (i.e., significant reduction in computation costs).

scholarly journals OFCOD: On the Fly Clustering Based Outlier Detection Framework

Data ◽  
2020 ◽  
Vol 6 (1) ◽  
pp. 1
Author(s):  
Ahmed Elmogy ◽  
Hamada Rizk ◽  
Amany M. Sarhan
Keyword(s):  
Data Mining ◽  
Image Processing ◽  
Intrusion Detection ◽  
Real Time ◽  
Outlier Detection ◽  
Real World ◽  
Medical Data ◽  
Experimental Results ◽  
Real Time Applications ◽  
Real World Datasets

In data mining, outlier detection is a major challenge as it has an important role in many applications such as medical data, image processing, fraud detection, intrusion detection, and so forth. An extensive variety of clustering based approaches have been developed to detect outliers. However they are by nature time consuming which restrict their utilization with real-time applications. Furthermore, outlier detection requests are handled one at a time, which means that each request is initiated individually with a particular set of parameters. In this paper, the first clustering based outlier detection framework, (On the Fly Clustering Based Outlier Detection (OFCOD)) is presented. OFCOD enables analysts to effectively find out outliers on time with request even within huge datasets. The proposed framework has been tested and evaluated using two real world datasets with different features and applications; one with 699 records, and another with five millions records. The experimental results show that the performance of the proposed framework outperforms other existing approaches while considering several evaluation metrics.

scholarly journals Overlapping Community Detection Based on Attribute Augmented Graph

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 680
Author(s):  
Hanyang Lin ◽  
Yongzhao Zhan ◽  
Zizheng Zhao ◽  
Yuzhong Chen ◽  
Chen Dong
Keyword(s):  
Community Detection ◽  
Real World ◽  
Detection Algorithm ◽  
Overlapping Community Detection ◽  
Overlapping Communities ◽  
Adjustment Strategy ◽  
Topology Information ◽  
Overlapping Community ◽  
Real World Datasets ◽  
Community Detection Algorithm

There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communities more accurately. Second, we enhance the algorithm to automatically determine the number of communities by a node-density-based fuzzy k-medoids process. Extensive experiments on both synthetic and real-world datasets demonstrate that the proposed algorithms can effectively detect overlapping communities with fewer parameters compared to the baseline methods.

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