scholarly journals Collaborative Self-Attention Network for Session-based Recommendation

2020 ◽  
Author(s):  
Anjing Luo ◽  
Pengpeng Zhao ◽  
Yanchi Liu ◽  
Fuzhen Zhuang ◽  
Deqing Wang ◽  
...  
Keyword(s):  
Long Range ◽  
Real World ◽  
State Of The Art ◽  
Time Step ◽  
Attention Network ◽  
Art Methods ◽  
Real World Datasets ◽  
Item Representation

Session-based recommendation becomes a research hotspot for its ability to make recommendations for anonymous users. However, existing session-based methods have the following limitations: (1) They either lack the capability to learn complex dependencies or focus mostly on the current session without explicitly considering collaborative information. (2) They assume that the representation of an item is static and fixed for all users at each time step. We argue that even the same item can be represented differently for different users at the same time step. To this end, we propose a novel solution, Collaborative Self-Attention Network (CoSAN) for session-based recommendation, to learn the session representation and predict the intent of the current session by investigating neighborhood sessions. Specially, we first devise a collaborative item representation by aggregating the embedding of neighborhood sessions retrieved according to each item in the current session. Then, we apply self-attention to learn long-range dependencies between collaborative items and generate collaborative session representation. Finally, each session is represented by concatenating the collaborative session representation and the embedding of the current session. Extensive experiments on two real-world datasets show that CoSAN constantly outperforms state-of-the-art methods.

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 Sentence Generation for Entity Description with Content-Plan Attention

2020 ◽  
Vol 34 (05) ◽  
pp. 9057-9064
Author(s):  
Bayu Trisedya ◽  
Jianzhong Qi ◽  
Rui Zhang
Keyword(s):  
State Of The Art ◽  
Neural Models ◽  
Time Step ◽  
Two Stage ◽  
Sentence Generation ◽  
Neural Data ◽  
Attention Model ◽  
Linear Sequence ◽  
Proper Order ◽  
Real World Datasets

We study neural data-to-text generation. Specifically, we consider a target entity that is associated with a set of attributes. We aim to generate a sentence to describe the target entity. Previous studies use encoder-decoder frameworks where the encoder treats the input as a linear sequence and uses LSTM to encode the sequence. However, linearizing a set of attributes may not yield the proper order of the attributes, and hence leads the encoder to produce an improper context to generate a description. To handle disordered input, recent studies propose two-stage neural models that use pointer networks to generate a content-plan (i.e., content-planner) and use the content-plan as input for an encoder-decoder model (i.e., text generator). However, in two-stage models, the content-planner may yield an incomplete content-plan, due to missing one or more salient attributes in the generated content-plan. This will in turn cause the text generator to generate an incomplete description. To address these problems, we propose a novel attention model that exploits content-plan to highlight salient attributes in a proper order. The challenge of integrating a content-plan in the attention model of an encoder-decoder framework is to align the content-plan and the generated description. We handle this problem by devising a coverage mechanism to track the extent to which the content-plan is exposed in the previous decoding time-step, and hence it helps our proposed attention model select the attributes to be mentioned in the description in a proper order. Experimental results show that our model outperforms state-of-the-art baselines by up to 3% and 5% in terms of BLEU score on two real-world datasets, respectively.

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.

scholarly journals Self-Attention ConvLSTM for Spatiotemporal Prediction

2020 ◽  
Vol 34 (07) ◽  
pp. 11531-11538
Author(s):  
Zhihui Lin ◽  
Maomao Li ◽  
Zhuobin Zheng ◽  
Yangyang Cheng ◽  
Chun Yuan
Keyword(s):  
Long Range ◽  
State Of The Art ◽  
The Self ◽  
Time Step ◽  
Traffic Flow Prediction ◽  
Spatial Features ◽  
Gating Mechanism ◽  
Spatial Dependencies ◽  
Previous State ◽  
Global And Local

Spatiotemporal prediction is challenging due to the complex dynamic motion and appearance changes. Existing work concentrates on embedding additional cells into the standard ConvLSTM to memorize spatial appearances during the prediction. These models always rely on the convolution layers to capture the spatial dependence, which are local and inefficient. However, long-range spatial dependencies are significant for spatial applications. To extract spatial features with both global and local dependencies, we introduce the self-attention mechanism into ConvLSTM. Specifically, a novel self-attention memory (SAM) is proposed to memorize features with long-range dependencies in terms of spatial and temporal domains. Based on the self-attention, SAM can produce features by aggregating features across all positions of both the input itself and memory features with pair-wise similarity scores. Moreover, the additional memory is updated by a gating mechanism on aggregated features and an established highway with the memory of the previous time step. Therefore, through SAM, we can extract features with long-range spatiotemporal dependencies. Furthermore, we embed the SAM into a standard ConvLSTM to construct a self-attention ConvLSTM (SA-ConvLSTM) for the spatiotemporal prediction. In experiments, we apply the SA-ConvLSTM to perform frame prediction on the MovingMNIST and KTH datasets and traffic flow prediction on the TexiBJ dataset. Our SA-ConvLSTM achieves state-of-the-art results on both datasets with fewer parameters and higher time efficiency than previous state-of-the-art method.

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 EA Reader: Enhance Attentive Reader for Cloze-Style Question Answering via Multi-Space Context Fusion

2019 ◽  
Vol 33 ◽  
pp. 6375-6382
Author(s):  
Chengzhen Fu ◽  
Yan Zhang
Keyword(s):  
Real World ◽  
Question Answering ◽  
State Of The Art ◽  
Unified Model ◽  
Inference Process ◽  
Context Vector ◽  
Attentive Reader ◽  
Semantic Spaces ◽  
Real World Datasets

Query-document semantic interactions are essential for the success of many cloze-style question answering models. Recently, researchers have proposed several attention-based methods to predict the answer by focusing on appropriate subparts of the context document. In this paper, we design a novel module to produce the query-aware context vector, named Multi-Space based Context Fusion (MSCF), with the following considerations: (1) interactions are applied across multiple latent semantic spaces; (2) attention is measured at bit level, not at token level. Moreover, we extend MSCF to the multi-hop architecture. This unified model is called Enhanced Attentive Reader (EA Reader). During the iterative inference process, the reader is equipped with a novel memory update rule and maintains the understanding of documents through read, update and write operations. We conduct extensive experiments on four real-world datasets. Our results demonstrate that EA Reader outperforms state-of-the-art models.

scholarly journals Revisiting and Optimising a CNN Colour Constancy Method for Multi-Illuminant Estimation

2021 ◽  
Vol 2021 (1) ◽  
pp. 68-72
Author(s):  
Ghalia Hemrit ◽  
Joseph Meehan
Keyword(s):  
Real World ◽  
State Of The Art ◽  
Colour Constancy ◽  
Illuminant Estimation ◽  
Art Methods ◽  
Deep Cnn ◽  
Scene Illumination ◽  
Visual Results

The aim of colour constancy is to discount the effect of the scene illumination from the image colours and restore the colours of the objects as captured under a ‘white’ illuminant. For the majority of colour constancy methods, the first step is to estimate the scene illuminant colour. Generally, it is assumed that the illumination is uniform in the scene. However, real world scenes have multiple illuminants, like sunlight and spot lights all together in one scene. We present in this paper a simple yet very effective framework using a deep CNN-based method to estimate and use multiple illuminants for colour constancy. Our approach works well in both the multi and single illuminant cases. The output of the CNN method is a region-wise estimate map of the scene which is smoothed and divided out from the image to perform colour constancy. The method that we propose outperforms other recent and state of the art methods and has promising visual results.

scholarly journals DEEPCON: protein contact prediction using dilated convolutional neural networks with dropout

2019 ◽  
Vol 36 (2) ◽  
pp. 470-477 ◽  
Author(s):  
Badri Adhikari
Keyword(s):  
Neural Networks ◽  
Long Range ◽  
Convolutional Neural Networks ◽  
High Throughput Sequencing ◽  
State Of The Art ◽  
Deep Convolutional Neural Networks ◽  
Contact Prediction ◽  
Learning Techniques ◽  
Medium Range ◽  
Art Methods

Abstract Motivation Exciting new opportunities have arisen to solve the protein contact prediction problem from the progress in neural networks and the availability of a large number of homologous sequences through high-throughput sequencing. In this work, we study how deep convolutional neural networks (ConvNets) may be best designed and developed to solve this long-standing problem. Results With publicly available datasets, we designed and trained various ConvNet architectures. We tested several recent deep learning techniques including wide residual networks, dropouts and dilated convolutions. We studied the improvements in the precision of medium-range and long-range contacts, and compared the performance of our best architectures with the ones used in existing state-of-the-art methods. The proposed ConvNet architectures predict contacts with significantly more precision than the architectures used in several state-of-the-art methods. When trained using the DeepCov dataset consisting of 3456 proteins and tested on PSICOV dataset of 150 proteins, our architectures achieve up to 15% higher precision when L/2 long-range contacts are evaluated. Similarly, when trained using the DNCON2 dataset consisting of 1426 proteins and tested on 84 protein domains in the CASP12 dataset, our single network achieves 4.8% higher precision than the ensembled DNCON2 method when top L long-range contacts are evaluated. Availability and implementation DEEPCON is available at https://github.com/badriadhikari/DEEPCON/.

scholarly journals Partial Label Learning with Self-Guided Retraining

2019 ◽  
Vol 33 ◽  
pp. 3542-3549 ◽  
Author(s):  
Lei Feng ◽  
Bo An
Keyword(s):  
Real World ◽  
Optimization Problem ◽  
State Of The Art ◽  
Ground Truth ◽  
Learning Approaches ◽  
High Confidence ◽  
Infinity Norm ◽  
Real World Datasets ◽  
Partial Label Learning ◽  
Optimization Efficiency

Partial label learning deals with the problem where each training instance is assigned a set of candidate labels, only one of which is correct. This paper provides the first attempt to leverage the idea of self-training for dealing with partially labeled examples. Specifically, we propose a unified formulation with proper constraints to train the desired model and perform pseudo-labeling jointly. For pseudo-labeling, unlike traditional self-training that manually differentiates the ground-truth label with enough high confidence, we introduce the maximum infinity norm regularization on the modeling outputs to automatically achieve this consideratum, which results in a convex-concave optimization problem. We show that optimizing this convex-concave problem is equivalent to solving a set of quadratic programming (QP) problems. By proposing an upper-bound surrogate objective function, we turn to solving only one QP problem for improving the optimization efficiency. Extensive experiments on synthesized and real-world datasets demonstrate that the proposed approach significantly outperforms the state-of-the-art partial label learning approaches.

scholarly journals Multi-Aspect Embedding for Attribute-Aware Trajectories

Symmetry ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1149
Author(s):  
Thapana Boonchoo ◽  
Xiang Ao ◽  
Qing He
Keyword(s):  
Real World ◽  
Execution Time ◽  
State Of The Art ◽  
Representation Learning ◽  
Learning Approach ◽  
Trajectory Data ◽  
Trajectory Mining ◽  
Trajectory Similarity ◽  
Effectiveness And Efficiency ◽  
Real World Datasets

Motivated by the proliferation of trajectory data produced by advanced GPS-enabled devices, trajectory is gaining in complexity and beginning to embroil additional attributes beyond simply the coordinates. As a consequence, this creates the potential to define the similarity between two attribute-aware trajectories. However, most existing trajectory similarity approaches focus only on location based proximities and fail to capture the semantic similarities encompassed by these additional asymmetric attributes (aspects) of trajectories. In this paper, we propose multi-aspect embedding for attribute-aware trajectories (MAEAT), a representation learning approach for trajectories that simultaneously models the similarities according to their multiple aspects. MAEAT is built upon a sentence embedding algorithm and directly learns whole trajectory embedding via predicting the context aspect tokens when given a trajectory. Two kinds of token generation methods are proposed to extract multiple aspects from the raw trajectories, and a regularization is devised to control the importance among aspects. Extensive experiments on the benchmark and real-world datasets show the effectiveness and efficiency of the proposed MAEAT compared to the state-of-the-art and baseline methods. The results of MAEAT can well support representative downstream trajectory mining and management tasks, and the algorithm outperforms other compared methods in execution time by at least two orders of magnitude.

Sign in / Sign up

Export Citation Format

Share Document