scholarly journals Personalized Multimedia Item and Key Frame Recommendation

2019 ◽  
Author(s):  
Le Wu ◽  
Lei Chen ◽  
Yonghui Yang ◽  
Richang Hong ◽  
Yong Ge ◽  
...  
Keyword(s):  
General Problem ◽  
Real World ◽  
Experimental Results ◽  
Visual Images ◽  
Key Frame ◽  
Fine Grained ◽  
Interaction Behavior ◽  
Proposed Model ◽  
Latent Space ◽  
Visual Preferences

When recommending or advertising items to users, an emerging trend is to present each multimedia item with  a key frame image (e.g., the poster of a movie). As each multimedia item can be represented as  multiple fine-grained  visual images (e.g., related images of the movie), personalized key frame recommendation is necessary in these applications to attract users' unique visual preferences. However, previous personalized key frame recommendation models relied on users' fine grained image  behavior of  multimedia items (e.g., user-image interaction behavior), which is often not available in real scenarios.  In this paper, we study the general problem of joint multimedia item and key frame recommendation in the absence of the fine-grained user-image behavior. We argue that the key challenge of this problem lies in discovering users' visual profiles for key frame recommendation, as most recommendation models  would fail without any users' fine-grained image behavior. To tackle this challenge, we leverage users' item behavior by projecting users(items) in two latent spaces: a collaborative latent space and a visual latent space. We further design a model to discern both the collaborative and  visual dimensions of users, and model how users make decisive item preferences from these two spaces. As a result, the learned user visual profiles could be directly applied for key frame recommendation. Finally, experimental results on a real-world dataset clearly show the effectiveness of our proposed model on the two recommendation tasks.

An Adaptive Scheme to Achieve Fine Grained Video Scaling

2017 ◽  
Vol 8 (1) ◽  
pp. 43
Author(s):  
S Safinaz ◽  
A. V. Ravi Kumar
Keyword(s):  
Reconstruction Error ◽  
Experimental Results ◽  
Adaptive Scheme ◽  
Fine Grained ◽  
Proposed Model ◽  
Reconstruction Quality ◽  
Adaptive Reconstruction ◽  
Robust Adaptive ◽  
Parallel Configuration ◽  
Very High

<p>A robust Adaptive Reconstruction Error Minimization Convolution Neural Network (<strong> ARemCNN</strong>) architecture introduced to provide high reconstruction quality from low resolution using parallel configuration. Our proposed model can easily train the bulky datasets such as YUV21 and Videoset4.Our experimental results shows that our model outperforms many existing techniques in terms of PSNR, SSIM and reconstruction quality. The experimental results shows that our average PSNR result is 39.81 considering upscale-2, 35.56 for upscale-3 and 33.77 for upscale-4 for Videoset4 dataset which is very high in contrast to other existing techniques. Similarly, the experimental results shows that our average PSNR result is 38.71 considering upscale-2, 34.58 for upscale-3 and 33.047 for upscale-4 for YUV21 dataset.</p>

scholarly journals A Generative Model for Dynamic Networks with Applications

2019 ◽  
Vol 33 ◽  
pp. 7842-7849
Author(s):  
Shubham Gupta ◽  
Gaurav Sharma ◽  
Ambedkar Dukkipati
Keyword(s):  
Neural Network ◽  
Social Networks ◽  
Real World ◽  
Link Prediction ◽  
Temporal Dynamics ◽  
Dynamic Networks ◽  
Collaboration Networks ◽  
Proposed Model ◽  
Latent Space ◽  
Over Time

Networks observed in real world like social networks, collaboration networks etc., exhibit temporal dynamics, i.e. nodes and edges appear and/or disappear over time. In this paper, we propose a generative, latent space based, statistical model for such networks (called dynamic networks). We consider the case where the number of nodes is fixed, but the presence of edges can vary over time. Our model allows the number of communities in the network to be different at different time steps. We use a neural network based methodology to perform approximate inference in the proposed model and its simplified version. Experiments done on synthetic and real world networks for the task of community detection and link prediction demonstrate the utility and effectiveness of our model as compared to other similar existing approaches.

A multi-grained aspect vector learning model for unsupervised aspect identification

2021 ◽  
pp. 1-11
Author(s):  
Jinglei Shi ◽  
Junjun Guo ◽  
Zhengtao Yu ◽  
Yan Xiang
Keyword(s):  
Vector Space ◽  
Sentiment Analysis ◽  
Membership Function ◽  
Topic Models ◽  
Experimental Results ◽  
Coarse Grained ◽  
Product Reviews ◽  
Fine Grained ◽  
Proposed Model ◽  
Identification Model

Unsupervised aspect identification is a challenging task in aspect-based sentiment analysis. Traditional topic models are usually used for this task, but they are not appropriate for short texts such as product reviews. In this work, we propose an aspect identification model based on aspect vector reconstruction. A key of our model is that we make connections between sentence vectors and multi-grained aspect vectors using fuzzy k-means membership function. Furthermore, to make full use of different aspect representations in vector space, we reconstruct sentence vectors based on coarse-grained aspect vectors and fine-grained aspect vectors simultaneously. The resulting model can therefore learn better aspect representations. Experimental results on two datasets from different domains show that our proposed model can outperform a few baselines in terms of aspect identification and topic coherence of the extracted aspect terms.

scholarly journals Opinion Relation Co-Extraction Based on Partially-Supervised Topical Relations Word Alignment Model

2020 ◽  
Vol 8 (6) ◽  
pp. 135-142
Keyword(s):  
Experimental Results ◽  
Word Alignment ◽  
Ranking Algorithm ◽  
Fine Grained ◽  
Opinion Extraction ◽  
Proposed Model ◽  
On Line ◽  
Alignment Model ◽  
Associated Variable

The most critical tools for fine-grained opinion extraction are opinion goals and opinion terms extracted from on-line comments. The key part of this process is to identify the connection between terms. To do this, the Word Alignment Model (WAM) was introduced in which the associated variable can be identified by word alignment by an opinion goal. Nevertheless, its ability to extract opinion words was less successful. In order to determine opinion connections as a process of alignment, the partially supervised Word Alienation Model (PSWAM) has therefore been created. Then a visual co-ranking algorithm was implemented together with the Opinion Relationship Map, to model all the candidates and to measure the confidence of each voter by defining their opinion. In addition, higher-confidence candidates were extracted as opinions or opinions. This method, though, involves an added kind of interaction with terms such as topical connections in graphic thought. Therefore the current relationship is assumed in this report in order to model the applicants and derive the feelings, views and opinions. The efficiency of co-extracting thoughts, viewpoints and issues is enhanced effectively by using this method. The experimental results further indicate that compared to the existing paradigm, the efficiency of the proposed model.

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 Session-Level User Satisfaction Prediction for Customer Service Chatbot in E-Commerce (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13973-13974
Author(s):  
Riheng Yao ◽  
Shuangyong Song ◽  
Qiudan Li ◽  
Chao Wang ◽  
Huan Chen ◽  
...  
Keyword(s):  
Customer Service ◽  
Real World ◽  
User Satisfaction ◽  
Experimental Results ◽  
Practical Significance ◽  
Proposed Model ◽  
Questions And Answers ◽  
The Relationship

This paper aims to predict user satisfaction for customer service chatbot in session level, which is of great practical significance yet rather untouched. It requires to explore the relationship between questions and answers across different rounds of interactions, and handle user bias. We propose an approach to model multi-round conversations within one session and take user information into account. Experimental results on a dataset from a real-world industrial customer service chatbot Alime demonstrate the good performance of our proposed model.

scholarly journals Large-Scale Fine-Grained Bird Recognition Based on a Triplet Network and Bilinear Model

2018 ◽  
Vol 8 (10) ◽  
pp. 1906 ◽  
Author(s):  
Zhicheng Zhao ◽  
Ze Luo ◽  
Jian Li ◽  
Kaihua Wang ◽  
Bingying Shi
Keyword(s):  
Image Classification ◽  
Large Scale ◽  
Experimental Results ◽  
Manual Annotation ◽  
Bilinear Model ◽  
Basic Category ◽  
Generalization Ability ◽  
Fine Grained ◽  
Annotation Information ◽  
Proposed Model

The main purpose of fine-grained classification is to distinguish among many subcategories of a single basic category, such as birds or flowers. We propose a model based on a triple network and bilinear methods for fine-grained bird identification. Our proposed model can be trained in an end-to-end manner, which effectively increases the inter-class distance of the network extraction features and improves the accuracy of bird recognition. When experimentally tested on 1096 birds in a custom-built dataset and on Caltech-UCSD (a public bird dataset), the model achieved an accuracy of 88.91% and 85.58%, respectively. The experimental results confirm the high generalization ability of our model in fine-grained image classification. Moreover, our model requires no additional manual annotation information such as object-labeling frames and part-labeling points, which guarantees good versatility and robustness in fine-grained bird recognition.

scholarly journals Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification

2020 ◽  
Vol 34 (07) ◽  
pp. 12144-12151
Author(s):  
Guan-An Wang ◽  
Tianzhu Zhang ◽  
Yang Yang ◽  
Jian Cheng ◽  
Jianlong Chang ◽  
...  
Keyword(s):  
State Of The Art ◽  
Experimental Results ◽  
Fine Grained ◽  
Invariant Features ◽  
Proposed Model ◽  
Art Methods ◽  
Conventional Methods

RGB-Infrared (IR) person re-identification is very challenging due to the large cross-modality variations between RGB and IR images. The key solution is to learn aligned features to the bridge RGB and IR modalities. However, due to the lack of correspondence labels between every pair of RGB and IR images, most methods try to alleviate the variations with set-level alignment by reducing the distance between the entire RGB and IR sets. However, this set-level alignment may lead to misalignment of some instances, which limits the performance for RGB-IR Re-ID. Different from existing methods, in this paper, we propose to generate cross-modality paired-images and perform both global set-level and fine-grained instance-level alignments. Our proposed method enjoys several merits. First, our method can perform set-level alignment by disentangling modality-specific and modality-invariant features. Compared with conventional methods, ours can explicitly remove the modality-specific features and the modality variation can be better reduced. Second, given cross-modality unpaired-images of a person, our method can generate cross-modality paired images from exchanged images. With them, we can directly perform instance-level alignment by minimizing distances of every pair of images. Extensive experimental results on two standard benchmarks demonstrate that the proposed model favourably against state-of-the-art methods. Especially, on SYSU-MM01 dataset, our model can achieve a gain of 9.2% and 7.7% in terms of Rank-1 and mAP. Code is available at https://github.com/wangguanan/JSIA-ReID.

scholarly journals Identification of Micro-blog Opinion Leaders based on User Features and Outbreak Nodes

2017 ◽  
Vol 12 (01) ◽  
pp. 141
Author(s):  
Lin Cui ◽  
Dechang Pi
Keyword(s):  
Real World ◽  
Latent Variable ◽  
Probability Model ◽  
Opinion Leader ◽  
Experimental Results ◽  
Opinion Leaders ◽  
Proposed Model ◽  
Input Variables ◽  
Real World Datasets ◽  
The Ideal

At present, recognition of micro-blog opinion leaders mainly depends on the number of users posting micro-blogs, registration time, the number of good friends and other static attributes. However, it is very difficult to obtain the ideal recognition results through the above mentioned methods. This paper puts forward a new method that identifies the opinion leaders according to the change of user features and outbreak nodes. Deeply analyzing various attributes and behaviors of users, on the basis of user features and outbreak nodes, user’s attribute features are regarded as the input variables, behavior features of the user and outbreak nodes are regarded as observed variables. The probability as an opinion leader is the latent variable between input variables and observation variables, and the constructed probability model is used to recognize micro-blog opinion leaders. Experiments are carried out on the two real-world datasets from Sina micro-blog and Twitter, and the comparative experimental results show that the proposed model can more precisely find the micro-blog opinion leaders.

Using Latent Fine-Grained Sentiment for Cross-Domain Sentiment Analysis

2021 ◽  
Vol 11 (3) ◽  
pp. 29-45
Author(s):  
Kwun-Ping Lai ◽  
Jackie Chun-Sing Ho ◽  
Wai Lam
Keyword(s):  
Knowledge Transfer ◽  
Sentiment Analysis ◽  
Real World ◽  
Coarse Grained ◽  
Target Domain ◽  
Training Strategy ◽  
Fine Grained ◽  
Cross Domain ◽  
Proposed Model ◽  
Novel Model

The authors investigate the problem task of multi-source cross-domain sentiment classification under the constraint of little labeled data. The authors propose a novel model which is capable of capturing both sentiment terms with strong or weak polarity from various source domains which are useful for knowledge transfer to unlabeled target domain. The authors propose a two-step training strategy with different granularities helping the model to identify sentiment terms with different degrees of sentiment polarity. Specifically, the coarse-grained training step captures the strong sentiment terms from the whole review while the fine-grained training step focuses on the latent fine-grained sentence sentiment which are helpful under the constraint of little labeled data. Experiments on a real-world product review dataset show that the proposed model has a good performance even under the little labeled data constraint.

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