pwc-archive/papers-with-abstracts huggingface dataset:
A Sequence-Aware Recommendation Method Based on Complex Networks
Authors: Abdullah Alhadlaq, Said Kerrache, Hatim AboAlSamh
Date: 2022-09-30 00:00:00
Tasks: Matrix Completion, Recommendation Systems, Time Series, Time Series Analysis

Online stores and service providers rely heavily on recommendation softwares to guide users through the vast amount of available products. Consequently, the field of recommender systems has attracted increased attention from the industry and academia alike, but despite this joint effort, the field still faces several challenges. For instance, most existing work models the recommendation problem as a matrix completion problem to predict the user preference for an item. This abstraction prevents the system from utilizing the rich information from the ordered sequence of user actions logged in online sessions. To address this limitation, researchers have recently developed a promising new breed of algorithms called sequence-aware recommender systems to predict the user's next action by utilizing the time series composed of the sequence of actions in an ongoing user session. This paper proposes a novel sequence-aware recommendation approach based on a complex network generated by the hidden metric space model, which combines node similarity and popularity to generate links. We build a network model from data and then use it to predict the user's subsequent actions. The network model provides an additional source of information that improves the accuracy of the recommendations. The proposed method is implemented and tested experimentally on a large dataset. The results prove that the proposed approach performs better than state-of-the-art recommendation methods.
Intra-session Context-aware Feed Recommendation in Live Systems
Authors: Luo Ji, Gao Liu, Mingyang Yin, Hongxia Yang
Date: 2022-09-30 00:00:00

Feed recommendation allows users to constantly browse items until feel uninterested and leave the session, which differs from traditional recommendation scenarios. Within a session, user's decision to continue browsing or not substantially affects occurrences of later clicks. However, such type of exposure bias is generally ignored or not explicitly modeled in most feed recommendation studies. In this paper, we model this effect as part of intra-session context, and propose a novel intra-session Context-aware Feed Recommendation (INSCAFER) framework to maximize the total views and total clicks simultaneously. User click and browsing decisions are jointly learned by a multi-task setting, and the intra-session context is encoded by the session-wise exposed item sequence. We deploy our model online with all key business benchmarks improved. Our method sheds some lights on feed recommendation studies which aim to optimize session-level click and view metrics.
A Recommendation Approach based on Similarity-Popularity Models of Complex Networks
Authors: Abdullah Alhadlaq, Said Kerrache, Hatim AboAlSamh
Date: 2022-09-29 00:00:00
Tasks: Data Visualization, Recommendation Systems

Recommender systems have become an essential tool for providers and users of online services and goods, especially with the increased use of the Internet to access information and purchase products and services. This work proposes a novel recommendation method based on complex networks generated by a similarity-popularity model to predict ones. We first construct a model of a network having users and items as nodes from observed ratings and then use it to predict unseen ratings. The prospect of producing accurate rating predictions using a similarity-popularity model with hidden metric spaces and dot-product similarity is explored. The proposed approach is implemented and experimentally compared against baseline and state-of-the-art recommendation methods on 21 datasets from various domains. The experimental results demonstrate that the proposed method produces accurate predictions and outperforms existing methods. We also show that the proposed approach produces superior results in low dimensions, proving its effectiveness for data visualization and exploration.
PrivMVMF: Privacy-Preserving Multi-View Matrix Factorization for Recommender Systems
Authors: Peihua Mai, Yan Pang
Date: 2022-09-29 00:00:00
Tasks: Federated Learning, Privacy Preserving, Recommendation Systems, Reconstruction Attack

With an increasing focus on data privacy, there have been pilot studies on recommender systems in a federated learning (FL) framework, where multiple parties collaboratively train a model without sharing their data. Most of these studies assume that the conventional FL framework can fully protect user privacy. However, there are serious privacy risks in matrix factorization in federated recommender systems based on our study. This paper first provides a rigorous theoretical analysis of the server reconstruction attack in four scenarios in federated recommender systems, followed by comprehensive experiments. The empirical results demonstrate that the FL server could infer users' information with accuracy >80% based on the uploaded gradients from FL nodes. The robustness analysis suggests that our reconstruction attack analysis outperforms the random guess by >30% under Laplace noises with b no larger than 0.5 for all scenarios. Then, the paper proposes a new privacy-preserving framework based on homomorphic encryption, Privacy-Preserving Multi-View Matrix Factorization (PrivMVMF), to enhance user data privacy protection in federated recommender systems. The proposed PrivMVMF is successfully implemented and tested thoroughly with the MovieLens dataset.
Discussion about Attacks and Defenses for Fair and Robust Recommendation System Design
Authors: Mirae Kim, Simon Woo
Date: 2022-09-28 00:00:00
Tasks: Collaborative Filtering, Fairness, Position, Recommendation Systems

Information has exploded on the Internet and mobile with the advent of the big data era. In particular, recommendation systems are widely used to help consumers who struggle to select the best products among such a large amount of information. However, recommendation systems are vulnerable to malicious user biases, such as fake reviews to promote or demote specific products, as well as attacks that steal personal information. Such biases and attacks compromise the fairness of the recommendation model and infringe the privacy of users and systems by distorting data.Recently, deep-learning collaborative filtering recommendation systems have shown to be more vulnerable to this bias. In this position paper, we examine the effects of bias that cause various ethical and social issues, and discuss the need for designing the robust recommendation system for fairness and stability.
FeatureBox: Feature Engineering on GPUs for Massive-Scale Ads Systems
Authors: Weijie Zhao, Xuewu Jiao, Xinsheng Luo, Jingxue Li, Belhal Karimi, Ping Li
Date: 2022-09-26 00:00:00
Tasks: Feature Engineering, GPU, Management, Scheduling

Deep learning has been widely deployed for online ads systems to predict Click-Through Rate (CTR). Machine learning researchers and practitioners frequently retrain CTR models to test their new extracted features. However, the CTR model training often relies on a large number of raw input data logs. Hence, the feature extraction can take a significant proportion of the training time for an industrial-level CTR model. In this paper, we propose FeatureBox, a novel end-to-end training framework that pipelines the feature extraction and the training on GPU servers to save the intermediate I/O of the feature extraction. We rewrite computation-intensive feature extraction operators as GPU operators and leave the memory-intensive operator on CPUs. We introduce a layer-wise operator scheduling algorithm to schedule these heterogeneous operators. We present a light-weight GPU memory management algorithm that supports dynamic GPU memory allocation with minimal overhead. We experimentally evaluate FeatureBox and compare it with the previous in-production feature extraction framework on two real-world ads applications. The results confirm the effectiveness of our proposed method.
Flattened Graph Convolutional Networks For Recommendation
Authors: Yue Xu, Hao Chen, Zengde Deng, Yuanchen Bei, Feiran Huang
Date: 2022-09-25 00:00:00

Graph Convolutional Networks (GCNs) and their variants have achieved significant performances on various recommendation tasks. However, many existing GCN models tend to perform recursive aggregations among all related nodes, which can arise severe computational burden to hinder their application to large-scale recommendation tasks. To this end, this paper proposes the flattened GCN~(FlatGCN) model, which is able to achieve superior performance with remarkably less complexity compared with existing models. Our main contribution is three-fold. First, we propose a simplified but powerful GCN architecture which aggregates the neighborhood information using one flattened GCN layer, instead of recursively. The aggregation step in FlatGCN is parameter-free such that it can be pre-computed with parallel computation to save memory and computational cost. Second, we propose an informative neighbor-infomax sampling method to select the most valuable neighbors by measuring the correlation among neighboring nodes based on a principled metric. Third, we propose a layer ensemble technique which improves the expressiveness of the learned representations by assembling the layer-wise neighborhood representations at the final layer. Extensive experiments on three datasets verify that our proposed model outperforms existing GCN models considerably and yields up to a few orders of magnitude speedup in training efficiency.
Towards Trustworthy AI-Empowered Real-Time Bidding for Online Advertisement Auctioning
Authors: Xiaoli Tang, Han Yu
Date: 2022-09-21 00:00:00
Tasks: Fairness

Artificial intelligence-empowred Real-Time Bidding (AIRTB) is regarded as one of the most enabling technologies for online advertising. It has attracted significant research attention from diverse fields such as pattern recognition, game theory and mechanism design. Despite of its remarkable development and deployment, the AIRTB system can sometimes harm the interest of its participants (e.g., depleting the advertisers' budget with various kinds of fraud). As such, building trustworthy AIRTB auctioning systems has emerged as an important direction of research in this field in recent years. Due to the highly interdisciplinary nature of this field and a lack of a comprehensive survey, it is a challenge for researchers to enter this field and contribute towards building trustworthy AIRTB technologies. This paper bridges this important gap in trustworthy AIRTB literature. We start by analysing the key concerns of various AIRTB stakeholders and identify three main dimensions of trust building in AIRTB, namely security, robustness and fairness. For each of these dimensions, we propose a unique taxonomy of the state of the art, trace the root causes of possible breakdown of trust, and discuss the necessity of the given dimension. This is followed by a comprehensive review of existing strategies for fulfilling the requirements of each trust dimension. In addition, we discuss the promising future directions of research essential towards building trustworthy AIRTB systems to benefit the field of online advertising.
Diversified Recommendations for Agents with Adaptive Preferences
Authors: Arpit Agarwal, William Brown
Date: 2022-09-20 00:00:00

When an Agent visits a platform recommending a menu of content to select from, their choice of item depends not only on fixed preferences, but also on their prior engagements with the platform. The Recommender's primary objective is typically to encourage content consumption which optimizes some reward, such as ad revenue, but they often also aim to ensure that a wide variety of content is consumed by the Agent over time. We formalize this problem as an adversarial bandit task. At each step, the Recommender presents a menu of $k$ (out of $n$) items to the Agent, who selects one item in the menu according to their unknown preference model, which maps their history of past items to relative selection probabilities. The Recommender then observes the Agent's chosen item and receives bandit feedback of the item's reward. In addition to optimizing reward from selected items, the Recommender must also ensure that the total distribution of chosen items has sufficiently high entropy. We define a class of preference models which are locally learnable, i.e. behavior over the entire domain can be estimated by only observing behavior in a small region; this includes models representable by bounded-degree polynomials as well as functions with a sparse Fourier basis. For this class, we give an algorithm for the Recommender which obtains $\tilde{O}(T^{3/4})$ regret against all item distributions satisfying two conditions: they are sufficiently diversified, and they are instantaneously realizable at any history by some distribution over menus. We show that these conditions are closely connected: all sufficiently high-entropy distributions are instantaneously realizable at any item history. We also give a set of negative results justifying our assumptions, in the form of a runtime lower bound for non-local learning and linear regret lower bounds for alternate benchmarks.
Learning To Rank Diversely At Airbnb
Authors: Malay Haldar, Mustafa Abdool, Liwei He, Dillon Davis, Huiji Gao, Sanjeev Katariya
Date: 2022-09-19 00:00:00
Tasks: Efficient Neural Network, Learning-To-Rank

Airbnb is a two-sided marketplace, bringing together hosts who own listings for rent, with prospective guests from around the globe. Applying neural network-based learning to rank techniques has led to significant improvements in matching guests with hosts. These improvements in ranking were driven by a core strategy: order the listings by their estimated booking probabilities, then iterate on techniques to make these booking probability estimates more and more accurate. Embedded implicitly in this strategy was an assumption that the booking probability of a listing could be determined independently of other listings in search results. In this paper we discuss how this assumption, pervasive throughout the commonly-used learning to rank frameworks, is false. We provide a theoretical foundation correcting this assumption, followed by efficient neural network architectures based on the theory. Explicitly accounting for possible similarities between listings, and reducing them to diversify the search results generated strong positive impact. We discuss these metric wins as part of the online A/B tests of the theory. Our method provides a practical way to diversify search results for large-scale production ranking systems.
A similarity measurement for time series and its application to the stock market
Authors: Feng Zhao, Yating Gao, Xinning Li, Zhiyong An, Shiyu Ge, Caiming Zhang
Date: 2021-11-15 00:00:00
Proceeding: Expert Systems with Applications 2021 11
Tasks: Dynamic Time Warping, Time Series, Time Series Analysis

The stock market is a very important financial market, and the prediction of the stock has always been of great 
interest to many investors. Nowadays, many methods for predicting stocks have been developed and one of the 
most commonly adopted strategies is to seek similar stocks through historical data to make predictions. The key 
to this strategy is the construction of a reasonable similarity measurement. In this paper, for accurately 
describing the similarity between a pair of time series, a novel similarity measurement is proposed, which is 
named as the dynamic multi-perspective personalized similarity measurement (DMPSM). Specifically, the 
segmented stock series are weighted according to the principle that the closer to current data, the more weight 
will be given. Then, Canberra distance is embedded into the dynamic time warping (DTW) to measure the 
similarity between any pair of time series. By this way, the DMPSM can not only reflect the personalization of 
stock time series, but also eliminate the impact of singularities and apply to one-to-many matching. To validate 
the efficiency of DMPSM, experiments utilized 285 stocks from the Shanghai Stock Exchange and the results 
demonstrated the superiority of the proposed approach over similarity measurements, including Euclidean 
distance, Canberra distance and DTW.
Analyzing how BERT performs entity matching
Authors: Matteo Paganelli, Francesco Del Buono, Andrea Baraldi, Francesco Guerra
Date: 2022-04-01 00:00:00
Proceeding: Proceedings of the VLDB Endowment 2022 4
Tasks: Entity Resolution, Semantic Similarity, Semantic Textual Similarity

State-of-the-art Entity Matching (EM) approaches rely on transformer architectures, such as BERT, for generating highly contex-tualized embeddings of terms. The embeddings are then used to predict whether pairs of entity descriptions refer to the same real-world entity. BERT-based EM models demonstrated to be effective, but act as black-boxes for the users, who have limited insight into the motivations behind their decisions.

In this paper, we perform a multi-facet analysis of the components of pre-trained and fine-tuned BERT architectures applied to an EM task. The main findings resulting from our extensive experimental evaluation are (1) the fine-tuning process applied to the EM task mainly modifies the last layers of the BERT components, but in a different way on tokens belonging to descriptions of matching / non-matching entities; (2) the special structure of the EM datasets, where records are pairs of entity descriptions is recognized by BERT; (3) the pair-wise semantic similarity of tokens is not a key knowledge exploited by BERT-based EM models.
SAR-GPA: SAR Generation Perturbation Algorithm
Authors: Zhe Liu, Weijie Xia, Yongzhen Lei
Date: 2022-01-19 00:00:00
Proceeding: ACM 2022 1

The deep learning is widely used in optical image and synthetic
aperture radar (SAR) image. Current academic research shows that
adversarial perturbation can effectively attack the deep learning
network in optical image. However, in SAR image target recognition network, the existence of universal perturbations and generation approach needs to be further explored. Here, this article
firstly proposes a systematic SAR generation perturbation algorithm (SAR-GPA) for target recognition network. The modulation
phase sequences of the jamming points can vary casually by using the state-of-the-art electromagnetic metasurface technology.
Therefore, when it acts on the SAR deceptive jamming system, it
can produce artificial controllable perturbations. First, we take the
imperceptible perturbations from universal adversarial perturbations (UAP) as reference to construct a unconstrained minimum
optimization problem to find the specific sequences. Then, we solve
this issue by adaptive moment estimation (Adam) optimizer.Thus,
the SAR adversarial examples can be quickly and flexibly generated through our system. Finally, We design a series of simulation
and experiment to verify the effectiveness of the adversarial examples and also the modulation sequences. According to the results,
different from the traditional SAR blanket jamming methods, our
approach can quickly generate imperceptible jamming, which can
effectively attack three classical recognition models.
End-To-End Audio Replay Attack Detection Using Deep Convolutional Networks with Attention
Authors: Francis Tom, Mohit Jain, Prasenjit Dey
Date: 2018-09-03 00:00:00
Proceeding: Interspeech 2018 2018 9
Tasks: Speaker Verification

With automatic speaker verification (ASV) systems becoming
increasingly popular, the development of robust countermeasures against spoofing is needed. Replay attacks pose a significant threat to the reliability of ASV systems because of the
relative difficulty in detecting replayed speech and the ease with
which such attacks can be mounted. In this paper, we propose an end-to-end deep learning framework for audio replay
attack detection. Our proposed approach uses a novel visual
attention mechanism on time-frequency representations of utterances based on group delay features, via deep residual learning (an adaptation of ResNet-18 architecture). Using a single
model system, we achieve a perfect Equal Error Rate (EER) of
0% on both the development as well as the evaluation set of the
ASVspoof 2017 dataset, against a previous best of 0.12% on the
development set and 2.76% on the evaluation set reported in the
literature. This highlights the efficacy of our feature representation and attention-based architecture in tackling the challenging
task of audio replay attack detection.
Index Terms: Replay attack, group delay grams, end-to-end
deep learning, visual attention, ASVspoof 2017 dataset
An Efficient FPGA Accelerator for Point Cloud
Authors: Zilun Wang, Wendong Mao, Peixiang Yang, Zhongfeng Wang, Jun Lin
Date: 2022-10-14 00:00:00
Tasks: Autonomous Driving, Computational Efficiency, GPU

Deep learning-based point cloud processing plays an important role in various vision tasks, such as autonomous driving, virtual reality (VR), and augmented reality (AR). The submanifold sparse convolutional network (SSCN) has been widely used for the point cloud due to its unique advantages in terms of visual results. However, existing convolutional neural network accelerators suffer from non-trivial performance degradation when employed to accelerate SSCN because of the extreme and unstructured sparsity, and the complex computational dependency between the sparsity of the central activation and the neighborhood ones. In this paper, we propose a high performance FPGA-based accelerator for SSCN. Firstly, we develop a zero removing strategy to remove the coarse-grained redundant regions, thus significantly improving computational efficiency. Secondly, we propose a concise encoding scheme to obtain the matching information for efficient point-wise multiplications. Thirdly, we develop a sparse data matching unit and a computing core based on the proposed encoding scheme, which can convert the irregular sparse operations into regular multiply-accumulate operations. Finally, an efficient hardware architecture for the submanifold sparse convolutional layer is developed and implemented on the Xilinx ZCU102 field-programmable gate array board, where the 3D submanifold sparse U-Net is taken as the benchmark. The experimental results demonstrate that our design drastically improves computational efficiency, and can dramatically improve the power efficiency by 51 times compared to GPU.
Contrastive Audio-Visual Masked Autoencoder
Authors: Yuan Gong, Andrew Rouditchenko, Alexander H. Liu, David Harwath, Leonid Karlinsky, Hilde Kuehne, James Glass
Date: 2022-10-02 00:00:00
Tasks: Audio Classification, Audio Tagging, Contrastive Learning, Multi-modal Classification, Retrieval, Self-Supervised Learning, Sound Prompted Semantic Segmentation, Speech Prompted Semantic Segmentation

In this paper, we first extend the recent Masked Auto-Encoder (MAE) model from a single modality to audio-visual multi-modalities. Subsequently, we propose the Contrastive Audio-Visual Masked Auto-Encoder (CAV-MAE) by combining contrastive learning and masked data modeling, two major self-supervised learning frameworks, to learn a joint and coordinated audio-visual representation. Our experiments show that the contrastive audio-visual correspondence learning objective not only enables the model to perform audio-visual retrieval tasks, but also helps the model learn a better joint representation. As a result, our fully self-supervised pretrained CAV-MAE achieves a new SOTA accuracy of 65.9% on VGGSound, and is comparable with the previous best supervised pretrained model on AudioSet in the audio-visual event classification task. Code and pretrained models are at https://github.com/yuangongnd/cav-mae.