pwc-archive/papers-with-abstracts huggingface dataset:
Scaling Down, LiTting Up: Efficient Zero-Shot Listwise Reranking with Seq2seq Encoder-Decoder Models
Authors: Manveer Singh Tamber, Ronak Pradeep, Jimmy Lin
Date: 2023-12-26 00:00:00
Tasks: Decoder, Reranking

Recent work in zero-shot listwise reranking using LLMs has achieved state-of-the-art results. However, these methods are not without drawbacks. The proposed methods rely on large LLMs with billions of parameters and limited context sizes. This paper introduces LiT5-Distill and LiT5-Score, two methods for efficient zero-shot listwise reranking, leveraging T5 sequence-to-sequence encoder-decoder models. Our approaches demonstrate competitive reranking effectiveness compared to recent state-of-the-art LLM rerankers with substantially smaller models. Through LiT5-Score, we also explore the use of cross-attention to calculate relevance scores to perform reranking, eliminating the reliance on external passage relevance labels for training. We present a range of models from 220M parameters to 3B parameters, all with strong reranking results, challenging the necessity of large-scale models for effective zero-shot reranking and opening avenues for more efficient listwise reranking solutions. We provide code and scripts to reproduce our results at https://github.com/castorini/LiT5.
RecRanker: Instruction Tuning Large Language Model as Ranker for Top-k Recommendation
Authors: Sichun Luo, Bowei He, Haohan Zhao, Wei Shao, Yanlin Qi, Yinya Huang, Aojun Zhou, Yuxuan Yao, Zongpeng Li, Yuanzhang Xiao, Mingjie Zhan, Linqi Song
Date: 2023-12-26 00:00:00
Tasks: In-Context Learning, Language Modeling, Language Modelling, Large Language Model, Position, Recommendation Systems, Sequential Recommendation

Large Language Models (LLMs) have demonstrated remarkable capabilities and have been extensively deployed across various domains, including recommender systems. Prior research has employed specialized \textit{prompts} to leverage the in-context learning capabilities of LLMs for recommendation purposes. More recent studies have utilized instruction tuning techniques to align LLMs with human preferences, promising more effective recommendations. However, existing methods suffer from several limitations. The full potential of LLMs is not fully elicited due to low-quality tuning data and the overlooked integration of conventional recommender signals. Furthermore, LLMs may generate inconsistent responses for different ranking tasks in the recommendation, potentially leading to unreliable results. In this paper, we introduce \textbf{RecRanker}, tailored for instruction tuning LLMs to serve as the \textbf{Ranker} for top-\textit{k} \textbf{Rec}ommendations. Specifically, we introduce an adaptive sampling module for sampling high-quality, representative, and diverse training data. To enhance the prompt, we introduce a position shifting strategy to mitigate position bias and augment the prompt with auxiliary information from conventional recommendation models, thereby enriching the contextual understanding of the LLM. Subsequently, we utilize the sampled data to assemble an instruction-tuning dataset with the augmented prompts comprising three distinct ranking tasks: pointwise, pairwise, and listwise rankings. We further propose a hybrid ranking method to enhance the model performance by ensembling these ranking tasks. Our empirical evaluations demonstrate the effectiveness of our proposed RecRanker in both direct and sequential recommendation scenarios.
An Incremental Update Framework for Online Recommenders with Data-Driven Prior
Authors: Chen Yang, Jin Chen, Qian Yu, Xiangdong Wu, Kui Ma, Zihao Zhao, Zhiwei Fang, Wenlong Chen, Chaosheng Fan, Jie He, Changping Peng, Zhangang Lin, Jingping Shao
Date: 2023-12-26 00:00:00
Tasks: Continual Learning

Online recommenders have attained growing interest and created great revenue for businesses. Given numerous users and items, incremental update becomes a mainstream paradigm for learning large-scale models in industrial scenarios, where only newly arrived data within a sliding window is fed into the model, meeting the strict requirements of quick response. However, this strategy would be prone to overfitting to newly arrived data. When there exists a significant drift of data distribution, the long-term information would be discarded, which harms the recommendation performance. Conventional methods address this issue through native model-based continual learning methods, without analyzing the data characteristics for online recommenders. To address the aforementioned issue, we propose an incremental update framework for online recommenders with Data-Driven Prior (DDP), which is composed of Feature Prior (FP) and Model Prior (MP). The FP performs the click estimation for each specific value to enhance the stability of the training process. The MP incorporates previous model output into the current update while strictly following the Bayes rules, resulting in a theoretically provable prior for the robust update. In this way, both the FP and MP are well integrated into the unified framework, which is model-agnostic and can accommodate various advanced interaction models. Extensive experiments on two publicly available datasets as well as an industrial dataset demonstrate the superior performance of the proposed framework.
Adversarial Item Promotion on Visually-Aware Recommender Systems by Guided Diffusion
Authors: Lijian Chen, Wei Yuan, Tong Chen, Guanhua Ye, Quoc Viet Hung Nguyen, Hongzhi Yin
Date: 2023-12-25 00:00:00
Tasks: Recommendation Systems

Visually-aware recommender systems have found widespread application in domains where visual elements significantly contribute to the inference of users' potential preferences. While the incorporation of visual information holds the promise of enhancing recommendation accuracy and alleviating the cold-start problem, it is essential to point out that the inclusion of item images may introduce substantial security challenges. Some existing works have shown that the item provider can manipulate item exposure rates to its advantage by constructing adversarial images. However, these works cannot reveal the real vulnerability of visually-aware recommender systems because (1) The generated adversarial images are markedly distorted, rendering them easily detectable by human observers; (2) The effectiveness of the attacks is inconsistent and even ineffective in some scenarios. To shed light on the real vulnerabilities of visually-aware recommender systems when confronted with adversarial images, this paper introduces a novel attack method, IPDGI (Item Promotion by Diffusion Generated Image). Specifically, IPDGI employs a guided diffusion model to generate adversarial samples designed to deceive visually-aware recommender systems. Taking advantage of accurately modeling benign images' distribution by diffusion models, the generated adversarial images have high fidelity with original images, ensuring the stealth of our IPDGI. To demonstrate the effectiveness of our proposed methods, we conduct extensive experiments on two commonly used e-commerce recommendation datasets (Amazon Beauty and Amazon Baby) with several typical visually-aware recommender systems. The experimental results show that our attack method has a significant improvement in both the performance of promoting the long-tailed (i.e., unpopular) items and the quality of generated adversarial images.
Preliminary Study on Incremental Learning for Large Language Model-based Recommender Systems
Authors: Tianhao Shi, Yang Zhang, Zhijian Xu, Chong Chen, Fuli Feng, Xiangnan He, Qi Tian
Date: 2023-12-25 00:00:00
Tasks: Attribute, Incremental Learning, Language Modeling, Language Modelling, Large Language Model, Recommendation Systems

Adapting Large Language Models for Recommendation (LLM4Rec) has shown promising results. However, the challenges of deploying LLM4Rec in real-world scenarios remain largely unexplored. In particular, recommender models need incremental adaptation to evolving user preferences, while the suitability of traditional incremental learning methods within LLM4Rec remains ambiguous due to the unique characteristics of Large Language Models (LLMs). In this study, we empirically evaluate two commonly employed incremental learning strategies (full retraining and fine-tuning) for LLM4Rec. Surprisingly, neither approach shows significant improvements in the performance of LLM4Rec. Instead of dismissing the role of incremental learning, we attribute the lack of anticipated performance enhancement to a mismatch between the LLM4Rec architecture and incremental learning: LLM4Rec employs a single adaptation module for learning recommendations, limiting its ability to simultaneously capture long-term and short-term user preferences in the incremental learning context. To test this speculation, we introduce a Long- and Short-term Adaptation-aware Tuning (LSAT) framework for incremental learning in LLM4Rec. Unlike the single adaptation module approach, LSAT utilizes two distinct adaptation modules to independently learn long-term and short-term user preferences. Empirical results verify that LSAT enhances performance, thereby validating our speculation. We release our code at: https://github.com/TianhaoShi2001/LSAT.
Aspect category learning and sentimental analysis using weakly supervised learning
Authors: Kalpa Subbaih, Bharath Kumar Bolla
Date: 2023-12-24 00:00:00
Tasks: Multi-class Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Sentiment Analysis, Sentiment Classification, Weakly-supervised Learning

The surge of e-commerce reviews has presented a challenge in manually annotating the vast volume of reviews to comprehend their underlying aspects and sentiments. This research focused on leveraging weakly supervised learning to tackle aspect category learning and the sentiment classification of reviews. Our approach involves the generation of labels for both aspects and sentiments, employing the Snorkel framework of WSL, which incorporates aspect terms, review sentiment scores, and review ratings as sources of weak signals. This innovative strategy significantly reduces the laborious labeling efforts required for processing such extensive datasets. In this study, we deployed hybrid models, namely BiLSTM, CNN-BiLSTM, and CNN-LSTM, which harness multiple inputs, including review text, aspect terms, and ratings. Our proposed model employs two distinct loss functions: Binary Cross Entropy with Sigmoid Activation for Multi-Label Classification, enabling us to learn aspect Labels such as Quality, Usability, Service, Size, and Price, and Categorical Cross Entropy with Softmax Activations for Multi-Class Classification. Subsequently, we meticulously evaluate the performance metrics of these three implemented models, including Macro F1 score and Macro Precision. CNN & Bi-LSTM model attained 0.78 and 0.79 F1 scores on aspect and sentiment identification, respectively. The outcomes of this research are poised to make a substantial contribution to e-commerce platforms, offering an efficient and automated means to label and analyze vast troves of user reviews.
Browsing behavior exposes identities on the Web
Authors: Marcos Oliveira, Junran Yang, Daniel Griffiths, Denis Bonnay, Juhi Kulshrestha
Date: 2023-12-24 00:00:00
Tasks: Navigate

How easy is it to uniquely identify a person based solely on their web browsing behavior? Here we show that when people navigate the Web, their online traces produce fingerprints that identify them. Merely the four most visited web domains are enough to identify 95% of the individuals. These digital fingerprints are stable and render high re-identifiability. We demonstrate that we can re-identify 80% of the individuals in separate time slices of data. Such a privacy threat persists even with limited information about individuals' browsing behavior, reinforcing existing concerns around online privacy.
Agent4Ranking: Semantic Robust Ranking via Personalized Query Rewriting Using Multi-agent LLM
Authors: Xiaopeng Li, Lixin Su, Pengyue Jia, Xiangyu Zhao, Suqi Cheng, Junfeng Wang, Dawei Yin
Date: 2023-12-24 00:00:00
Tasks: Mixture-of-Experts

Search engines are crucial as they provide an efficient and easy way to access vast amounts of information on the internet for diverse information needs. User queries, even with a specific need, can differ significantly. Prior research has explored the resilience of ranking models against typical query variations like paraphrasing, misspellings, and order changes. Yet, these works overlook how diverse demographics uniquely formulate identical queries. For instance, older individuals tend to construct queries more naturally and in varied order compared to other groups. This demographic diversity necessitates enhancing the adaptability of ranking models to diverse query formulations. To this end, in this paper, we propose a framework that integrates a novel rewriting pipeline that rewrites queries from various demographic perspectives and a novel framework to enhance ranking robustness. To be specific, we use Chain of Thought (CoT) technology to utilize Large Language Models (LLMs) as agents to emulate various demographic profiles, then use them for efficient query rewriting, and we innovate a robust Multi-gate Mixture of Experts (MMoE) architecture coupled with a hybrid loss function, collectively strengthening the ranking models' robustness. Our extensive experimentation on both public and industrial datasets assesses the efficacy of our query rewriting approach and the enhanced accuracy and robustness of the ranking model. The findings highlight the sophistication and effectiveness of our proposed model.
Toward Rapid Bug Resolution for Android Apps
Authors: Junayed Mahmud
Date: 2023-12-23 00:00:00
Tasks: Management

Bug reports document unexpected behaviors in software, enabling developers to understand, validate, and fix bugs. Unfortunately, a significant portion of bug reports is of low quality, which poses challenges for developers in terms of addressing these issues. Prior research has delved into the information needed for documenting high-quality bug reports and expediting bug report management. Furthermore, researchers have explored the challenges associated with bug report management and proposed various automated techniques. Nevertheless, these techniques exhibit several limitations, including a lexical gap between developers and reporters, difficulties in bug reproduction, and identifying bug locations. Therefore, there is a pressing need for additional efforts to effectively manage bug reports and enhance the quality of both desktop and mobile applications. In this paper, we describe the existing limitations of bug reports and identify potential strategies for addressing them. Our vision encompasses a future where the alleviation of these limitations and successful execution of our proposed new research directions can benefit both reporters and developers, ultimately making the entire software maintenance faster.
Monitoring the Evolution of Behavioural Embeddings in Social Media Recommendation
Authors: Srijan Saket, Olivier Jeunen, Md. Danish Kalim
Date: 2023-12-23 00:00:00
Tasks: Recommendation Systems

Emerging short-video platforms like TikTok, Instagram Reels, and ShareChat present unique challenges for recommender systems, primarily originating from a continuous stream of new content. ShareChat alone receives approximately 2 million pieces of fresh content daily, complicating efforts to assess quality, learn effective latent representations, and accurately match content with the appropriate user base, especially given limited user feedback. Embedding-based approaches are a popular choice for industrial recommender systems because they can learn low-dimensional representations of items, leading to effective recommendation that can easily scale to millions of items and users. Our work characterizes the evolution of such embeddings in short-video recommendation systems, comparing the effect of batch and real-time updates to content embeddings. We investigate \emph{how} embeddings change with subsequent updates, explore the relationship between embeddings and popularity bias, and highlight their impact on user engagement metrics. Our study unveils the contrast in the number of interactions needed to achieve mature embeddings in a batch learning setup versus a real-time one, identifies the point of highest information updates, and explores the distribution of $\ell_2$-norms across the two competing learning modes. Utilizing a production system deployed on a large-scale short-video app with over 180 million users, our findings offer insights into designing effective recommendation systems and enhancing user satisfaction and engagement in short-video applications.
Session-Based Recommendation by Exploiting Substitutable and Complementary Relationships from Multi-behavior Data
Authors: Huizi Wu, Cong Geng, Hui Fang
Date: 2023-12-13 00:00:00
Tasks: Denoising, Session-Based Recommendations

Session-based recommendation (SR) aims to dynamically recommend items to a user based on a sequence of the most recent user-item interactions. Most existing studies on SR adopt advanced deep learning methods. However, the majority only consider a special behavior type (e.g., click), while those few considering multi-typed behaviors ignore to take full advantage of the relationships between products (items). In this case, the paper proposes a novel approach, called Substitutable and Complementary Relationships from Multi-behavior Data (denoted as SCRM) to better explore the relationships between products for effective recommendation. Specifically, we firstly construct substitutable and complementary graphs based on a user's sequential behaviors in every session by jointly considering `click' and `purchase' behaviors. We then design a denoising network to remove false relationships, and further consider constraints on the two relationships via a particularly designed loss function. Extensive experiments on two e-commerce datasets demonstrate the superiority of our model over state-of-the-art methods, and the effectiveness of every component in SCRM.
Empowering ChatGPT-Like Large-Scale Language Models with Local Knowledge Base for Industrial Prognostics and Health Management
Authors: Huan Wang, Yan-Fu Li, Min Xie
Date: 2023-12-06 00:00:00
Tasks: Language Modelling, Management, Prompt Engineering

Prognostics and health management (PHM) is essential for industrial operation and maintenance, focusing on predicting, diagnosing, and managing the health status of industrial systems. The emergence of the ChatGPT-Like large-scale language model (LLM) has begun to lead a new round of innovation in the AI field. It has extensively promoted the level of intelligence in various fields. Therefore, it is also expected further to change the application paradigm in industrial PHM and promote PHM to become intelligent. Although ChatGPT-Like LLMs have rich knowledge reserves and powerful language understanding and generation capabilities, they lack domain-specific expertise, significantly limiting their practicability in PHM applications. To this end, this study explores the ChatGPT-Like LLM empowered by the local knowledge base (LKB) in industrial PHM to solve the above limitations. In addition, we introduce the method and steps of combining the LKB with LLMs, including LKB preparation, LKB vectorization, prompt engineering, etc. Experimental analysis of real cases shows that combining the LKB with ChatGPT-Like LLM can significantly improve its performance and make ChatGPT-Like LLMs more accurate, relevant, and able to provide more insightful information. This can promote the development of ChatGPT-Like LLMs in industrial PHM and promote their efficiency and quality.
Flood Event Extraction from News Media to Support Satellite-Based Flood Insurance
Authors: Tejit Pabari, Beth Tellman, Giannis Karamanolakis, Mitchell Thomas, Max Mauerman, Eugene Wu, Upmanu Lall, Marco Tedesco, Michael S Steckler, Paolo Colosio, Daniel E Osgood, Melody Braun, Jens de Bruijn, Shammun Islam
Date: 2023-12-05 00:00:00
Tasks: Articles, Event Extraction

Floods cause large losses to property, life, and livelihoods across the world every year, hindering sustainable development. Safety nets to help absorb financial shocks in disasters, such as insurance, are often unavailable in regions of the world most vulnerable to floods, like Bangladesh. Index-based insurance has emerged as an affordable solution, which considers weather data or information from satellites to create a "flood index" that should correlate with the damage insured. However, existing flood event databases are often incomplete, and satellite sensors are not reliable under extreme weather conditions (e.g., because of clouds), which limits the spatial and temporal resolution of current approaches for index-based insurance. In this work, we explore a novel approach for supporting satellite-based flood index insurance by extracting high-resolution spatio-temporal information from news media. First, we publish a dataset consisting of 40,000 news articles covering flood events in Bangladesh by 10 prominent news sources, and inundated area estimates for each division in Bangladesh collected from a satellite radar sensor. Second, we show that keyword-based models are not adequate for this novel application, while context-based classifiers cover complex and implicit flood related patterns. Third, we show that time series extracted from news media have substantial correlation Spearman's rho$=0.70 with satellite estimates of inundated area. Our work demonstrates that news media is a promising source for improving the temporal resolution and expanding the spatial coverage of the available flood damage data.
AS-XAI: Self-supervised Automatic Semantic Interpretation for CNN
Authors: Changqi Sun, Hao Xu, Yuntian Chen, Dongxiao Zhang
Date: 2023-12-02 00:00:00
Tasks: Explainable artificial intelligence, Explainable Artificial Intelligence (XAI)

Explainable artificial intelligence (XAI) aims to develop transparent explanatory approaches for "black-box" deep learning models. However,it remains difficult for existing methods to achieve the trade-off of the three key criteria in interpretability, namely, reliability, causality, and usability, which hinder their practical applications. In this paper, we propose a self-supervised automatic semantic interpretable explainable artificial intelligence (AS-XAI) framework, which utilizes transparent orthogonal embedding semantic extraction spaces and row-centered principal component analysis (PCA) for global semantic interpretation of model decisions in the absence of human interference, without additional computational costs. In addition, the invariance of filter feature high-rank decomposition is used to evaluate model sensitivity to different semantic concepts. Extensive experiments demonstrate that robust and orthogonal semantic spaces can be automatically extracted by AS-XAI, providing more effective global interpretability for convolutional neural networks (CNNs) and generating human-comprehensible explanations. The proposed approach offers broad fine-grained extensible practical applications, including shared semantic interpretation under out-of-distribution (OOD) categories, auxiliary explanations for species that are challenging to distinguish, and classification explanations from various perspectives.