scholarly journals UGSD: User Generated Sentiment Dictionaries from Online Customer Reviews

2019 ◽  
Vol 33 ◽  
pp. 313-320 ◽  
Author(s):  
Chun-Hsiang Wang ◽  
Kang-Chun Fan ◽  
Chuan-Ju Wang ◽  
Ming-Feng Tsai
Keyword(s):  
Representation Learning ◽  
Customer Reviews ◽  
Fine Grained ◽  
Learning Framework ◽  
Domain Specific ◽  
Entity Ranking ◽  
Online Customer Reviews ◽  
Rich Information ◽  
Real World Datasets ◽  
Low Dimensional

Customer reviews on platforms such as TripAdvisor and Amazon provide rich information about the ways that people convey sentiment on certain domains. Given these kinds of user reviews, this paper proposes UGSD, a representation learning framework for constructing domain-specific sentiment dictionaries from online customer reviews, in which we leverage the relationship between user-generated reviews and the ratings of the reviews to associate the reviewer sentiment with certain entities. The proposed framework has the following three main advantages. First, no additional annotations of words or external dictionaries are needed for the proposed framework; the only resources needed are the review texts and entity ratings. Second, the framework is applicable across a variety of user-generated content from different domains to construct domain-specific sentiment dictionaries. Finally, each word in the constructed dictionary is associated with a low-dimensional dense representation and a degree of relatedness to a certain rating, which enable us to obtain more fine-grained dictionaries and enhance the application scalability of the constructed dictionaries as the word representations can be adopted for various tasks or applications, such as entity ranking and dictionary expansion. The experimental results on three real-world datasets show that the framework is effective in constructing high-quality domain-specific sentiment dictionaries from customer reviews.

An Unsupervised Aspect-Aware Recommendation Model with Explanation Text Generation

2022 ◽  
Vol 40 (3) ◽  
pp. 1-29
Author(s):  
Peijie Sun ◽  
Le Wu ◽  
Kun Zhang ◽  
Yu Su ◽  
Meng Wang
Keyword(s):  
Data Privacy ◽  
Auxiliary Information ◽  
Generation Process ◽  
Text Generation ◽  
Generation Task ◽  
Auxiliary Data ◽  
Fine Grained ◽  
Aspect Extraction ◽  
Learning Framework ◽  
Real World Datasets

Review based recommendation utilizes both users’ rating records and the associated reviews for recommendation. Recently, with the rapid demand for explanations of recommendation results, reviews are used to train the encoder–decoder models for explanation text generation. As most of the reviews are general text without detailed evaluation, some researchers leveraged auxiliary information of users or items to enrich the generated explanation text. Nevertheless, the auxiliary data is not available in most scenarios and may suffer from data privacy problems. In this article, we argue that the reviews contain abundant semantic information to express the users’ feelings for various aspects of items, while these information are not fully explored in current explanation text generation task. To this end, we study how to generate more fine-grained explanation text in review based recommendation without any auxiliary data. Though the idea is simple, it is non-trivial since the aspect is hidden and unlabeled. Besides, it is also very challenging to inject aspect information for generating explanation text with noisy review input. To solve these challenges, we first leverage an advanced unsupervised neural aspect extraction model to learn the aspect-aware representation of each review sentence. Thus, users and items can be represented in the aspect space based on their historical associated reviews. After that, we detail how to better predict ratings and generate explanation text with the user and item representations in the aspect space. We further dynamically assign review sentences which contain larger proportion of aspect words with larger weights to control the text generation process, and jointly optimize rating prediction accuracy and explanation text generation quality with a multi-task learning framework. Finally, extensive experimental results on three real-world datasets demonstrate the superiority of our proposed model for both recommendation accuracy and explainability.

scholarly journals Exponential Family Graph Embeddings

2020 ◽  
Vol 34 (04) ◽  
pp. 3357-3364
Author(s):  
Abdulkadir Celikkanat ◽  
Fragkiskos D. Malliaros
Keyword(s):  
Random Walk ◽  
Exponential Family ◽  
Representation Learning ◽  
Learning Problems ◽  
Interaction Patterns ◽  
Network Representation ◽  
Learning Tasks ◽  
Learning Techniques ◽  
Real World Datasets ◽  
Low Dimensional

Representing networks in a low dimensional latent space is a crucial task with many interesting applications in graph learning problems, such as link prediction and node classification. A widely applied network representation learning paradigm is based on the combination of random walks for sampling context nodes and the traditional Skip-Gram model to capture center-context node relationships. In this paper, we emphasize on exponential family distributions to capture rich interaction patterns between nodes in random walk sequences. We introduce the generic exponential family graph embedding model, that generalizes random walk-based network representation learning techniques to exponential family conditional distributions. We study three particular instances of this model, analyzing their properties and showing their relationship to existing unsupervised learning models. Our experimental evaluation on real-world datasets demonstrates that the proposed techniques outperform well-known baseline methods in two downstream machine learning tasks.

scholarly journals Adaptive-Weighted Multiview Deep Basis Matrix Factorization for Multimedia Data Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Shicheng Li ◽  
Qinghua Liu ◽  
Jiangyan Dai ◽  
Wenle Wang ◽  
Xiaolin Gui ◽  
...  
Keyword(s):  
Data Analysis ◽  
Matrix Factorization ◽  
Feature Learning ◽  
Representation Learning ◽  
Feature Representation ◽  
Multimedia Data ◽  
Basis Matrix ◽  
Rich Information ◽  
Low Dimensional ◽  
Better Than

Feature representation learning is a key issue in artificial intelligence research. Multiview multimedia data can provide rich information, which makes feature representation become one of the current research hotspots in data analysis. Recently, a large number of multiview data feature representation methods have been proposed, among which matrix factorization shows the excellent performance. Therefore, we propose an adaptive-weighted multiview deep basis matrix factorization (AMDBMF) method that integrates matrix factorization, deep learning, and view fusion together. Specifically, we first perform deep basis matrix factorization on data of each view. Then, all views are integrated to complete the procedure of multiview feature learning. Finally, we propose an adaptive weighting strategy to fuse the low-dimensional features of each view so that a unified feature representation can be obtained for multiview multimedia data. We also design an iterative update algorithm to optimize the objective function and justify the convergence of the optimization algorithm through numerical experiments. We conducted clustering experiments on five multiview multimedia datasets and compare the proposed method with several excellent current methods. The experimental results demonstrate that the clustering performance of the proposed method is better than those of the other comparison methods.

LkeRec: Toward Lightweight End-to-End Joint Representation Learning for Building Accurate and Effective Recommendation

2022 ◽  
Vol 40 (3) ◽  
pp. 1-28
Author(s):  
Surong Yan ◽  
Kwei-Jay Lin ◽  
Xiaolin Zheng ◽  
Haosen Wang
Keyword(s):  
Side Information ◽  
Dimensional Space ◽  
Representation Learning ◽  
Knowledge Graph ◽  
Learning Framework ◽  
Relation Type ◽  
End To End ◽  
Low Dimensional ◽  
Item Knowledge ◽  
Explicit And Implicit Knowledge

Explicit and implicit knowledge about users and items have been used to describe complex and heterogeneous side information for recommender systems (RSs). Many existing methods use knowledge graph embedding (KGE) to learn the representation of a user-item knowledge graph (KG) in low-dimensional space. In this article, we propose a lightweight end-to-end joint learning framework for fusing the tasks of KGE and RSs at the model level. Our method proposes a lightweight KG embedding method by using bidirectional bijection relation-type modeling to enable scalability for large graphs while using self-adaptive negative sampling to optimize negative sample generating. Our method further generates the integrated views for users and items based on relation-types to explicitly model users’ preferences and items’ features, respectively. Finally, we add virtual “recommendation” relations between the integrated views of users and items to model the preferences of users on items, seamlessly integrating RS with user-item KG over a unified graph. Experimental results on multiple datasets and benchmarks show that our method can achieve a better accuracy of recommendation compared with existing state-of-the-art methods. Complexity and runtime analysis suggests that our method can gain a lower time and space complexity than most of existing methods and improve scalability.

scholarly journals Multi-view Feature Learning with Discriminative Regularization

2017 ◽  
Author(s):  
Jinglin Xu ◽  
Junwei Han ◽  
Feiping Nie
Keyword(s):  
Clustering Algorithms ◽  
Feature Learning ◽  
Feature Weighting ◽  
Learning Framework ◽  
Discriminative Feature ◽  
Heterogeneous Features ◽  
Real World Applications ◽  
Different Types ◽  
Rich Information ◽  
Low Dimensional

More and more multi-view data which can capture rich information from heterogeneous features are widely used in real world applications. How to integrate different types of features, and how to learn low dimensional and discriminative information from high dimensional data are two main challenges. To address these challenges, this paper proposes a novel multi-view feature learning framework, which is regularized by discriminative information and obtains a feature learning model that contains multiple discriminative feature weighting matrices for different views, and then yields multiple low dimensional features used for subsequent multi-view clustering. To optimize the formulated objective function, we transform the proposed framework into a trace optimization problem which obtains the global solution in a closed form. Experimental evaluations on four widely used datasets and comparisons with a number of state-of-the-art multi-view clustering algorithms demonstrate the superiority of the proposed work.

scholarly journals MRLR: Multi-level Representation Learning for Personalized Ranking in Recommendation

2017 ◽  
Author(s):  
Zhu Sun ◽  
Jie Yang ◽  
Jie Zhang ◽  
Alessandro Bozzon ◽  
Yu Chen ◽  
...  
Keyword(s):  
State Of The Art ◽  
Representation Learning ◽  
Full Potential ◽  
Prediction Problem ◽  
Fine Grained ◽  
Multi Level ◽  
Rating Prediction ◽  
Real World Datasets ◽  
Interaction Record ◽  
Personalized Ranking

Representation learning (RL) has recently proven to be effective in capturing local item relationships by modeling item co-occurrence in individual user's interaction record. However, the value of RL for recommendation has not reached the full potential due to two major drawbacks: 1) recommendation is modeled as a rating prediction problem but should essentially be a personalized ranking one; 2) multi-level organizations of items are neglected for fine-grained item relationships. We design a unified Bayesian framework MRLR to learn user and item embeddings from a multi-level item organization, thus benefiting from RL as well as achieving the goal of personalized ranking. Extensive validation on real-world datasets shows that MRLR consistently outperforms state-of-the-art algorithms.

scholarly journals An Information-Explainable Random Walk Based Unsupervised Network Representation Learning Framework on Node Classification Tasks

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1767
Author(s):  
Xin Xu ◽  
Yang Lu ◽  
Yupeng Zhou ◽  
Zhiguo Fu ◽  
Yanjie Fu ◽  
...  
Keyword(s):  
Random Walk ◽  
Representation Learning ◽  
Local Information ◽  
Learning Framework ◽  
Network Representation ◽  
Label Node ◽  
Label Information ◽  
Classification Tasks ◽  
Node Classification ◽  
Low Dimensional

Network representation learning aims to learn low-dimensional, compressible, and distributed representational vectors of nodes in networks. Due to the expensive costs of obtaining label information of nodes in networks, many unsupervised network representation learning methods have been proposed, where random walk strategy is one of the wildly utilized approaches. However, the existing random walk based methods have some challenges, including: 1. The insufficiency of explaining what network knowledge in the walking path-samplings; 2. The adverse effects caused by the mixture of different information in networks; 3. The poor generality of the methods with hyper-parameters on different networks. This paper proposes an information-explainable random walk based unsupervised network representation learning framework named Probabilistic Accepted Walk (PAW) to obtain network representation from the perspective of the stationary distribution of networks. In the framework, we design two stationary distributions based on nodes’ self-information and local-information of networks to guide our proposed random walk strategy to learn representational vectors of networks through sampling paths of nodes. Numerous experimental results demonstrated that the PAW could obtain more expressive representation than the other six widely used unsupervised network representation learning baselines on four real-world networks in single-label and multi-label node classification tasks.

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 Rule-Guided Compositional Representation Learning on Knowledge Graphs with Hierarchical Types

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1978
Author(s):  
Yanying Mao ◽  
Honghui Chen
Keyword(s):  
Knowledge Representation ◽  
Representation Learning ◽  
Knowledge Graph ◽  
Path Information ◽  
Different Types ◽  
Rich Information ◽  
The Rich ◽  
Horn Rules ◽  
Low Dimensional ◽  
Type Information

The representation learning of the knowledge graph projects the entities and relationships in the triples into a low-dimensional continuous vector space. Early representation learning mostly focused on the information contained in the triplet itself but ignored other useful information. Since entities have different types of representations in different scenarios, the rich information in the types of entity levels is helpful for obtaining a more complete knowledge representation. In this paper, a new knowledge representation frame (TRKRL) combining rule path information and entity hierarchical type information is proposed to exploit interpretability of logical rules and the advantages of entity hierarchical types. Specifically, for entity hierarchical type information, we consider that entities have multiple representations of different types, as well as treat it as the projection matrix of entities, using the type encoder to model entity hierarchical types. For rule path information, we mine Horn rules from the knowledge graph to guide the synthesis of relations in paths. Experimental results show that TRKRL outperforms baselines on the knowledge graph completion task, which indicates that our model is capable of using entity hierarchical type information, relation paths information, and logic rules information for representation learning.

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