pwc-archive/papers-with-abstracts huggingface dataset:
ChatGPT: Vision and Challenges
Authors: Sukhpal Singh Gill, Rupinder Kaur
Date: 2023-05-08 00:00:00
Tasks: Ethics, Language Modeling, Language Modelling

Artificial intelligence (AI) and machine learning have changed the nature of scientific inquiry in recent years. Of these, the development of virtual assistants has accelerated greatly in the past few years, with ChatGPT becoming a prominent AI language model. In this study, we examine the foundations, vision, research challenges of ChatGPT. This article investigates into the background and development of the technology behind it, as well as its popular applications. Moreover, we discuss the advantages of bringing everything together through ChatGPT and Internet of Things (IoT). Further, we speculate on the future of ChatGPT by considering various possibilities for study and development, such as energy-efficiency, cybersecurity, enhancing its applicability to additional technologies (Robotics and Computer Vision), strengthening human-AI communications, and bridging the technological gap. Finally, we discuss the important ethics and current trends of ChatGPT.
Extracting Shopping Interest-Related Product Types from the Web
Authors: Yinghao Li, Colin Lockard, Prashant Shiralkar, Chao Zhang
Date: 2023-05-23 00:00:00
Tasks: Node Classification

Recommending a diversity of product types (PTs) is important for a good shopping experience when customers are looking for products around their high-level shopping interests (SIs) such as hiking. However, the SI-PT connection is typically absent in e-commerce product catalogs and expensive to construct manually due to the volume of potential SIs, which prevents us from establishing a recommender with easily accessible knowledge systems. To establish such connections, we propose to extract PTs from the Web pages containing hand-crafted PT recommendations for SIs. The extraction task is formulated as binary HTML node classification given the general observation that an HTML node in our target Web pages can present one and only one PT phrase. Accordingly, we introduce TrENC, which stands for Tree-Transformer Encoders for Node Classification. It improves the inter-node dependency modeling with modified attention mechanisms that preserve the long-term sibling and ancestor-descendant relations. TrENC also injects SI into node features for better semantic representation. Trained on pages regarding limited SIs, TrEnc is ready to be applied to other unobserved interests. Experiments on our manually constructed dataset, WebPT, show that TrENC outperforms the best baseline model by 2.37 F1 points in the zero-shot setup. The performance indicates the feasibility of constructing SI-PT relations and using them to power downstream applications such as search and recommendation.
Fusion-in-T5: Unifying Document Ranking Signals for Improved Information Retrieval
Authors: Shi Yu, Chenghao Fan, Chenyan Xiong, David Jin, Zhiyuan Liu, Zhenghao Liu
Date: 2023-05-24 00:00:00
Tasks: Document Ranking, Information Retrieval, Passage Ranking, Retrieval, Text Matching

Common document ranking pipelines in search systems are cascade systems that involve multiple ranking layers to integrate different information step-by-step. In this paper, we propose a novel re-ranker Fusion-in-T5 (FiT5), which integrates text matching information, ranking features, and global document information into one single unified model via templated-based input and global attention. Experiments on passage ranking benchmarks MS MARCO and TREC DL show that FiT5, as one single model, significantly improves ranking performance over complex cascade pipelines. Analysis finds that through attention fusion, FiT5 jointly utilizes various forms of ranking information via gradually attending to related documents and ranking features, and improves the detection of subtle nuances. Our code is open-sourced at https://github.com/OpenMatch/FiT5.
JDsearch: A Personalized Product Search Dataset with Real Queries and Full Interactions
Authors: Jiongnan Liu, Zhicheng Dou, Guoyu Tang, Sulong Xu
Date: 2023-05-24 00:00:00

Recently, personalized product search attracts great attention and many models have been proposed. To evaluate the effectiveness of these models, previous studies mainly utilize the simulated Amazon recommendation dataset, which contains automatically generated queries and excludes cold users and tail products. We argue that evaluating with such a dataset may yield unreliable results and conclusions, and deviate from real user satisfaction. To overcome these problems, in this paper, we release a personalized product search dataset comprised of real user queries and diverse user-product interaction types (clicking, adding to cart, following, and purchasing) collected from JD.com, a popular Chinese online shopping platform. More specifically, we sample about 170,000 active users on a specific date, then record all their interacted products and issued queries in one year, without removing any tail users and products. This finally results in roughly 12,000,000 products, 9,400,000 real searches, and 26,000,000 user-product interactions. We study the characteristics of this dataset from various perspectives and evaluate representative personalization models to verify its feasibility. The dataset can be publicly accessed at Github: https://github.com/rucliujn/JDsearch.
How Graph Convolutions Amplify Popularity Bias for Recommendation?
Authors: Jiajia Chen, Jiancan Wu, Jiawei Chen, Xin Xin, Yong Li, Xiangnan He
Date: 2023-05-24 00:00:00
Tasks: Recommendation Systems

Graph convolutional networks (GCNs) have become prevalent in recommender system (RS) due to their superiority in modeling collaborative patterns. Although improving the overall accuracy, GCNs unfortunately amplify popularity bias -- tail items are less likely to be recommended. This effect prevents the GCN-based RS from making precise and fair recommendations, decreasing the effectiveness of recommender systems in the long run. In this paper, we investigate how graph convolutions amplify the popularity bias in RS. Through theoretical analyses, we identify two fundamental factors: (1) with graph convolution (\textit{i.e.,} neighborhood aggregation), popular items exert larger influence than tail items on neighbor users, making the users move towards popular items in the representation space; (2) after multiple times of graph convolution, popular items would affect more high-order neighbors and become more influential. The two points make popular items get closer to almost users and thus being recommended more frequently. To rectify this, we propose to estimate the amplified effect of popular nodes on each node's representation, and intervene the effect after each graph convolution. Specifically, we adopt clustering to discover highly-influential nodes and estimate the amplification effect of each node, then remove the effect from the node embeddings at each graph convolution layer. Our method is simple and generic -- it can be used in the inference stage to correct existing models rather than training a new model from scratch, and can be applied to various GCN models. We demonstrate our method on two representative GCN backbones LightGCN and UltraGCN, verifying its ability in improving the recommendations of tail items without sacrificing the performance of popular items. Codes are open-sourced \footnote{https://github.com/MEICRS/DAP}.
Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights
Authors: Junchen Fu, Fajie Yuan, Yu Song, Zheng Yuan, Mingyue Cheng, Shenghui Cheng, JiaQi Zhang, Jie Wang, Yunzhu Pan
Date: 2023-05-24 00:00:00
Tasks: Recommendation Systems, Transfer Learning

Adapters, a plug-in neural network module with some tunable parameters, have emerged as a parameter-efficient transfer learning technique for adapting pre-trained models to downstream tasks, especially for natural language processing (NLP) and computer vision (CV) fields. Meanwhile, learning recommendation models directly from raw item modality features -- e.g., texts of NLP and images of CV -- can enable effective and transferable recommender systems (called TransRec). In view of this, a natural question arises: can adapter-based learning techniques achieve parameter-efficient TransRec with good performance? To this end, we perform empirical studies to address several key sub-questions. First, we ask whether the adapter-based TransRec performs comparably to TransRec based on standard full-parameter fine-tuning? does it hold for recommendation with different item modalities, e.g., textual RS and visual RS. If yes, we benchmark these existing adapters, which have been shown to be effective in NLP and CV tasks, in item recommendation tasks. Third, we carefully study several key factors for the adapter-based TransRec in terms of where and how to insert these adapters? Finally, we look at the effects of adapter-based TransRec by either scaling up its source training data or scaling down its target training data. Our paper provides key insights and practical guidance on unified & transferable recommendation -- a less studied recommendation scenario. We release our codes and other materials at: https://github.com/westlake-repl/Adapter4Rec/.
Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies
Authors: Yubao Tang, Ruqing Zhang, Jiafeng Guo, Jiangui Chen, Zuowei Zhu, Shuaiqiang Wang, Dawei Yin, Xueqi Cheng
Date: 2023-05-24 00:00:00
Tasks: Memorization, Retrieval

Recently, a new paradigm called Differentiable Search Index (DSI) has been proposed for document retrieval, wherein a sequence-to-sequence model is learned to directly map queries to relevant document identifiers. The key idea behind DSI is to fully parameterize traditional ``index-retrieve'' pipelines within a single neural model, by encoding all documents in the corpus into the model parameters. In essence, DSI needs to resolve two major questions: (1) how to assign an identifier to each document, and (2) how to learn the associations between a document and its identifier. In this work, we propose a Semantic-Enhanced DSI model (SE-DSI) motivated by Learning Strategies in the area of Cognitive Psychology. Our approach advances original DSI in two ways: (1) For the document identifier, we take inspiration from Elaboration Strategies in human learning. Specifically, we assign each document an Elaborative Description based on the query generation technique, which is more meaningful than a string of integers in the original DSI; and (2) For the associations between a document and its identifier, we take inspiration from Rehearsal Strategies in human learning. Specifically, we select fine-grained semantic features from a document as Rehearsal Contents to improve document memorization. Both the offline and online experiments show improved retrieval performance over prevailing baselines.
Bert4XMR: Cross-Market Recommendation with Bidirectional Encoder Representations from Transformer
Authors: Zheng Hu, Satoshi Nakagawa, Shi-Min Cai, Fuji Ren
Date: 2023-05-24 00:00:00
Tasks: General Knowledge, Transfer Learning

Real-world multinational e-commerce companies, such as Amazon and eBay, serve in multiple countries and regions. Some markets are data-scarce, while others are data-rich. In recent years, cross-market recommendation (XMR) has been proposed to bolster data-scarce markets by leveraging auxiliary information from data-rich markets. Previous XMR algorithms have employed techniques such as sharing bottom or incorporating inter-market similarity to optimize the performance of XMR. However, the existing approaches suffer from two crucial limitations: (1) They ignore the co-occurrences of items provided by data-rich markets. (2) They do not adequately tackle the issue of negative transfer stemming from disparities across diverse markets. In order to address these limitations, we propose a novel session-based model called Bert4XMR, which is able to model item co-occurrences across markets and mitigate negative transfer. Specifically, we employ the pre-training and fine-tuning paradigm to facilitate knowledge transfer across markets. Pre-training occurs on global markets to learn item co-occurrences, while fine-tuning happens in the target market for model customization. To mitigate potential negative transfer, we separate the item representations into market embeddings and item embeddings. Market embeddings model the bias associated with different markets, while item embeddings learn generic item representations. Extensive experiments conducted on seven real-world datasets illustrate our model's effectiveness. It outperforms the suboptimal model by an average of $4.82\%$, $4.73\%$, $7.66\%$, and $6.49\%$ across four metrics. Through the ablation study, we experimentally demonstrate that the market embedding approach helps prevent negative transfer, especially in data-scarce markets. Our implementations are available at https://github.com/laowangzi/Bert4XMR.
Collaborative Recommendation Model Based on Multi-modal Multi-view Attention Network: Movie and literature cases
Authors: Zheng Hu, Shi-Min Cai, Jun Wang, Tao Zhou
Date: 2023-05-24 00:00:00
Tasks: Multi-modal Recommendation

The existing collaborative recommendation models that use multi-modal information emphasize the representation of users' preferences but easily ignore the representation of users' dislikes. Nevertheless, modelling users' dislikes facilitates comprehensively characterizing user profiles. Thus, the representation of users' dislikes should be integrated into the user modelling when we construct a collaborative recommendation model. In this paper, we propose a novel Collaborative Recommendation Model based on Multi-modal multi-view Attention Network (CRMMAN), in which the users are represented from both preference and dislike views. Specifically, the users' historical interactions are divided into positive and negative interactions, used to model the user's preference and dislike views, respectively. Furthermore, the semantic and structural information extracted from the scene is employed to enrich the item representation. We validate CRMMAN by designing contrast experiments based on two benchmark MovieLens-1M and Book-Crossing datasets. Movielens-1m has about a million ratings, and Book-Crossing has about 300,000 ratings. Compared with the state-of-the-art knowledge-graph-based and multi-modal recommendation methods, the AUC, NDCG@5 and NDCG@10 are improved by 2.08%, 2.20% and 2.26% on average of two datasets. We also conduct controlled experiments to explore the effects of multi-modal information and multi-view mechanism. The experimental results show that both of them enhance the model's performance.
Prompting Language-Informed Distribution for Compositional Zero-Shot Learning
Authors: Wentao Bao, Lichang Chen, Heng Huang, Yu Kong
Date: 2023-05-23 00:00:00
Tasks: Compositional Zero-Shot Learning, Informativeness, Zero-shot Generalization, Zero-Shot Learning

Compositional zero-shot learning (CZSL) task aims to recognize unseen compositional visual concepts, e.g., sliced tomatoes, where the model is learned only from the seen compositions, e.g., sliced potatoes and red tomatoes. Thanks to the prompt tuning on large pre-trained visual language models such as CLIP, recent literature shows impressively better CZSL performance than traditional vision-based methods. However, the key aspects that impact the generalization to unseen compositions, including the diversity and informativeness of class context, and the entanglement between visual primitives, i.e., state and object, are not properly addressed in existing CLIP-based CZSL literature. In this paper, we propose a model by prompting the language-informed distribution, aka., PLID, for the CZSL task. Specifically, the PLID leverages pre-trained large language models (LLM) to (i) formulate the language-informed class distributions which are diverse and informative, and (ii) enhance the compositionality of the class embedding. Moreover, a visual-language primitive decomposition (VLPD) module is proposed to dynamically fuse the classification decisions from the compositional and the primitive space. Orthogonal to the existing literature of soft, hard, or distributional prompts, our method advocates prompting the LLM-supported class distributions, leading to a better zero-shot generalization. Experimental results on MIT-States, UT-Zappos, and C-GQA datasets show the superior performance of the PLID to the prior arts. Our code and models are released: https://github.com/Cogito2012/PLID.
Sorted Convolutional Network for Achieving Continuous Rotational Invariance
Authors: Hanlin Mo, Guoying Zhao
Date: 2023-05-23 00:00:00
Tasks: Data Augmentation, image-classification, Image Classification

The topic of achieving rotational invariance in convolutional neural networks (CNNs) has gained considerable attention recently, as this invariance is crucial for many computer vision tasks such as image classification and matching. In this letter, we propose a Sorting Convolution (SC) inspired by some hand-crafted features of texture images, which achieves continuous rotational invariance without requiring additional learnable parameters or data augmentation. Further, SC can directly replace the conventional convolution operations in a classic CNN model to achieve its rotational invariance. Based on MNIST-rot dataset, we first analyze the impact of convolutional kernel sizes, different sampling and sorting strategies on SC's rotational invariance, and compare our method with previous rotation-invariant CNN models. Then, we combine SC with VGG, ResNet and DenseNet, and conduct classification experiments on popular texture and remote sensing image datasets. Our results demonstrate that SC achieves the best performance in the aforementioned tasks.
Windscreen Optical Quality for AI Algorithms: Refractive Power and MTF not Sufficient
Authors: Dominik Werner Wolf, Markus Ulrich, Alexander Braun
Date: 2023-05-23 00:00:00
Tasks: Autonomous Driving

Windscreen optical quality is an important aspect of any advanced driver assistance system, and also for future autonomous driving, as today at least some cameras of the sensor suite are situated behind the windscreen. Automotive mass production processes require measurement systems that characterize the optical quality of the windscreens in a meaningful way, which for modern perception stacks implies meaningful for artificial intelligence (AI) algorithms. The measured optical quality needs to be linked to the performance of these algorithms, such that performance limits - and thus production tolerance limits - can be defined. In this article we demonstrate that the main metric established in the industry - refractive power - is fundamentally not capable of capturing relevant optical properties of windscreens. Further, as the industry is moving towards the modulation transfer function (MTF) as an alternative, we mathematically show that this metric cannot be used on windscreens alone, but that the windscreen forms a novel optical system together with the optics of the camera system. Hence, the required goal of a qualification system that is installed at the windscreen supplier and independently measures the optical quality cannot be achieved using MTF. We propose a novel concept to determine the optical quality of windscreens and to use simulation to link this optical quality to the performance of AI algorithms, which can hopefully lead to novel inspection systems.
Design a Delicious Lunchbox in Style
Authors: Yutong Zhou
Date: 2023-05-22 00:00:00
Tasks: Generative Adversarial Network, Image Generation, Layout Generation

We propose a cyclic generative adversarial network with spatial-wise and channel-wise attention modules for text-to-image synthesis. To accurately depict and design scenes with multiple occluded objects, we design a pre-trained ordering recovery model and a generative adversarial network to predict layout and composite novel box lunch presentations. In the experiments, we devise the Bento800 dataset to evaluate the performance of the text-to-image synthesis model and the layout generation & image composition model. This paper is the continuation of our previous paper works. We also present additional experiments and qualitative performance comparisons to verify the effectiveness of our proposed method. Bento800 dataset is available at https://github.com/Yutong-Zhou-cv/Bento800_Dataset
Slovo: Russian Sign Language Dataset
Authors: Alexander Kapitanov, Karina Kvanchiani, Alexander Nagaev, Elizaveta Petrova
Date: 2023-05-23 00:00:00
Tasks: Sign Language Recognition

One of the main challenges of the sign language recognition task is the difficulty of collecting a suitable dataset due to the gap between hard-of-hearing and hearing societies. In addition, the sign language in each country differs significantly, which obliges the creation of new data for each of them. This paper presents the Russian Sign Language (RSL) video dataset Slovo, produced using crowdsourcing platforms. The dataset contains 20,000 FullHD recordings, divided into 1,000 classes of isolated RSL gestures received by 194 signers. We also provide the entire dataset creation pipeline, from data collection to video annotation, with the following demo application. Several neural networks are trained and evaluated on the Slovo to demonstrate its teaching ability. Proposed data and pre-trained models are publicly available.
GO-LDA: Generalised Optimal Linear Discriminant Analysis
Authors: Jiahui Liu, Xiaohao Cai, Mahesan Niranjan
Date: 2023-05-23 00:00:00

Linear discriminant analysis (LDA) has been a useful tool in pattern recognition and data analysis research and practice. While linearity of class boundaries cannot always be expected, nonlinear projections through pre-trained deep neural networks have served to map complex data onto feature spaces in which linear discrimination has served well. The solution to binary LDA is obtained by eigenvalue analysis of within-class and between-class scatter matrices. It is well known that the multiclass LDA is solved by an extension to the binary LDA, a generalised eigenvalue problem, from which the largest subspace that can be extracted is of dimension one lower than the number of classes in the given problem. In this paper, we show that, apart from the first of the discriminant directions, the generalised eigenanalysis solution to multiclass LDA does neither yield orthogonal discriminant directions nor maximise discrimination of projected data along them. Surprisingly, to the best of our knowledge, this has not been noted in decades of literature on LDA. To overcome this drawback, we present a derivation with a strict theoretical support for sequentially obtaining discriminant directions that are orthogonal to previously computed ones and maximise in each step the Fisher criterion. We show distributions of projections along these axes and demonstrate that discrimination of data projected onto these discriminant directions has optimal separation, which is much higher than those from the generalised eigenvectors of the multiclass LDA. Using a wide range of benchmark tasks, we present a comprehensive empirical demonstration that on a number of pattern recognition and classification problems, the optimal discriminant subspaces obtained by the proposed method, referred to as GO-LDA (Generalised Optimal LDA), can offer superior accuracy.