pwc-archive/papers-with-abstracts huggingface dataset:
STAIR: Manipulating Collaborative and Multimodal Information for E-Commerce Recommendation
Authors: Cong Xu, Yunhang He, Jun Wang, Wei zhang
Date: 2024-12-16 00:00:00
Tasks: Collaborative Filtering, Multimodal Recommendation

While the mining of modalities is the focus of most multimodal recommendation methods, we believe that how to fully utilize both collaborative and multimodal information is pivotal in e-commerce scenarios where, as clarified in this work, the user behaviors are rarely determined entirely by multimodal features. In order to combine the two distinct types of information, some additional challenges are encountered: 1) Modality erasure: Vanilla graph convolution, which proves rather useful in collaborative filtering, however erases multimodal information; 2) Modality forgetting: Multimodal information tends to be gradually forgotten as the recommendation loss essentially facilitates the learning of collaborative information. To this end, we propose a novel approach named STAIR, which employs a novel STepwise grAph convolution to enable a co-existence of collaborative and multimodal Information in e-commerce Recommendation. Besides, it starts with the raw multimodal features as an initialization, and the forgetting problem can be significantly alleviated through constrained embedding updates. As a result, STAIR achieves state-of-the-art recommendation performance on three public e-commerce datasets with minimal computational and memory costs. Our code is available at https://github.com/yhhe2004/STAIR.
Enhancing Healthcare Recommendation Systems with a Multimodal LLMs-based MOE Architecture
Authors: Jingyu Xu, Yang Wang
Date: 2024-12-16 00:00:00
Tasks: Mixture-of-Experts, Recommendation Systems

With the increasing availability of multimodal data, many fields urgently require advanced architectures capable of effectively integrating these diverse data sources to address specific problems. This study proposes a hybrid recommendation model that combines the Mixture of Experts (MOE) framework with large language models to enhance the performance of recommendation systems in the healthcare domain. We built a small dataset for recommending healthy food based on patient descriptions and evaluated the model's performance on several key metrics, including Precision, Recall, NDCG, and MAP@5. The experimental results show that the hybrid model outperforms the baseline models, which use MOE or large language models individually, in terms of both accuracy and personalized recommendation effectiveness. The paper finds image data provided relatively limited improvement in the performance of the personalized recommendation system, particularly in addressing the cold start problem. Then, the issue of reclassification of images also affected the recommendation results, especially when dealing with low-quality images or changes in the appearance of items, leading to suboptimal performance. The findings provide valuable insights into the development of powerful, scalable, and high-performance recommendation systems, advancing the application of personalized recommendation technologies in real-world domains such as healthcare.
Leveraging Large Vision-Language Model as User Intent-aware Encoder for Composed Image Retrieval
Authors: Zelong Sun, Dong Jing, Guoxing Yang, Nanyi Fei, Zhiwu Lu
Date: 2024-12-15 00:00:00
Tasks: Image Retrieval, Instruction Following, Language Modeling, Language Modelling, Retrieval

Composed Image Retrieval (CIR) aims to retrieve target images from candidate set using a hybrid-modality query consisting of a reference image and a relative caption that describes the user intent. Recent studies attempt to utilize Vision-Language Pre-training Models (VLPMs) with various fusion strategies for addressing the task.However, these methods typically fail to simultaneously meet two key requirements of CIR: comprehensively extracting visual information and faithfully following the user intent. In this work, we propose CIR-LVLM, a novel framework that leverages the large vision-language model (LVLM) as the powerful user intent-aware encoder to better meet these requirements. Our motivation is to explore the advanced reasoning and instruction-following capabilities of LVLM for accurately understanding and responding the user intent. Furthermore, we design a novel hybrid intent instruction module to provide explicit intent guidance at two levels: (1) The task prompt clarifies the task requirement and assists the model in discerning user intent at the task level. (2) The instance-specific soft prompt, which is adaptively selected from the learnable prompt pool, enables the model to better comprehend the user intent at the instance level compared to a universal prompt for all instances. CIR-LVLM achieves state-of-the-art performance across three prominent benchmarks with acceptable inference efficiency. We believe this study provides fundamental insights into CIR-related fields.
RecSys Arena: Pair-wise Recommender System Evaluation with Large Language Models
Authors: Zhuo Wu, Qinglin Jia, Chuhan Wu, Zhaocheng Du, Shuai Wang, Zan Wang, Zhenhua Dong
Date: 2024-12-15 00:00:00
Tasks: Chatbot, Recommendation Systems

Evaluating the quality of recommender systems is critical for algorithm design and optimization. Most evaluation methods are computed based on offline metrics for quick algorithm evolution, since online experiments are usually risky and time-consuming. However, offline evaluation usually cannot fully reflect users' preference for the outcome of different recommendation algorithms, and the results may not be consistent with online A/B test. Moreover, many offline metrics such as AUC do not offer sufficient information for comparing the subtle differences between two competitive recommender systems in different aspects, which may lead to substantial performance differences in long-term online serving. Fortunately, due to the strong commonsense knowledge and role-play capability of large language models (LLMs), it is possible to obtain simulated user feedback on offline recommendation results. Motivated by the idea of LLM Chatbot Arena, in this paper we present the idea of RecSys Arena, where the recommendation results given by two different recommender systems in each session are evaluated by an LLM judger to obtain fine-grained evaluation feedback. More specifically, for each sample we use LLM to generate a user profile description based on user behavior history or off-the-shelf profile features, which is used to guide LLM to play the role of this user and evaluate the relative preference for two recommendation results generated by different models. Through extensive experiments on two recommendation datasets in different scenarios, we demonstrate that many different LLMs not only provide general evaluation results that are highly consistent with canonical offline metrics, but also provide rich insight in many subjective aspects. Moreover, it can better distinguish different algorithms with comparable performance in terms of AUC and nDCG.
Why Not Together? A Multiple-Round Recommender System for Queries and Items
Authors: Jiarui Jin, Xianyu Chen, Weinan Zhang, Yong Yu, Jun Wang
Date: 2024-12-14 00:00:00
Tasks: Recommendation Systems

A fundamental technique of recommender systems involves modeling user preferences, where queries and items are widely used as symbolic representations of user interests. Queries delineate user needs at an abstract level, providing a high-level description, whereas items operate on a more specific and concrete level, representing the granular facets of user preference. While practical, both query and item recommendations encounter the challenge of sparse user feedback. To this end, we propose a novel approach named Multiple-round Auto Guess-and-Update System (MAGUS) that capitalizes on the synergies between both types, allowing us to leverage both query and item information to form user interests. This integrated system introduces a recursive framework that could be applied to any recommendation method to exploit queries and items in historical interactions and to provide recommendations for both queries and items in each interaction round. Empirical results from testing 12 different recommendation methods demonstrate that integrating queries into item recommendations via MAGUS significantly enhances the efficiency, with which users can identify their preferred items during multiple-round interactions.
Learned Data Compression: Challenges and Opportunities for the Future
Authors: Qiyu Liu, Siyuan Han, Jianwei Liao, Jin Li, Jingshu Peng, Jun Du, Lei Chen
Date: 2024-12-14 00:00:00
Tasks: CPU, Data Compression, Information Retrieval

Compressing integer keys is a fundamental operation among multiple communities, such as database management (DB), information retrieval (IR), and high-performance computing (HPC). Recent advances in \emph{learned indexes} have inspired the development of \emph{learned compressors}, which leverage simple yet compact machine learning (ML) models to compress large-scale sorted keys. The core idea behind learned compressors is to \emph{losslessly} encode sorted keys by approximating them with \emph{error-bounded} ML models (e.g., piecewise linear functions) and using a \emph{residual array} to guarantee accurate key reconstruction. While the concept of learned compressors remains in its early stages of exploration, our benchmark results demonstrate that an SIMD-optimized learned compressor can significantly outperform state-of-the-art CPU-based compressors. Drawing on our preliminary experiments, this vision paper explores the potential of learned data compression to enhance critical areas in DBMS and related domains. Furthermore, we outline the key technical challenges that existing systems must address when integrating this emerging methodology.
Enhancing Event Extraction from Short Stories through Contextualized Prompts
Authors: Chaitanya Kirti, Ayon Chattopadhyay, Ashish Anand, Prithwijit Guha
Date: 2024-12-14 00:00:00
Tasks: Articles, Event Detection, Event Extraction

Event extraction is an important natural language processing (NLP) task of identifying events in an unstructured text. Although a plethora of works deal with event extraction from new articles, clinical text etc., only a few works focus on event extraction from literary content. Detecting events in short stories presents several challenges to current systems, encompassing a different distribution of events as compared to other domains and the portrayal of diverse emotional conditions. This paper presents \texttt{Vrittanta-EN}, a collection of 1000 English short stories annotated for real events. Exploring this field could result in the creation of techniques and resources that support literary scholars in improving their effectiveness. This could simultaneously influence the field of Natural Language Processing. Our objective is to clarify the intricate idea of events in the context of short stories. Towards the objective, we collected 1,000 short stories written mostly for children in the Indian context. Further, we present fresh guidelines for annotating event mentions and their categories, organized into \textit{seven distinct classes}. The classes are {\tt{COGNITIVE-MENTAL-STATE(CMS), COMMUNICATION(COM), CONFLICT(CON), GENERAL-ACTIVITY(GA), LIFE-EVENT(LE), MOVEMENT(MOV), and OTHERS(OTH)}}. Subsequently, we apply these guidelines to annotate the short story dataset. Later, we apply the baseline methods for automatically detecting and categorizing events. We also propose a prompt-based method for event detection and classification. The proposed method outperforms the baselines, while having significant improvement of more than 4\% for the class \texttt{CONFLICT} in event classification task.
Sentiment and Hashtag-aware Attentive Deep Neural Network for Multimodal Post Popularity Prediction
Authors: Shubhi Bansal, Mohit Kumar, Chandravardhan Singh Raghaw, Nagendra Kumar
Date: 2024-12-14 00:00:00
Tasks: Sentiment Analysis

Social media users articulate their opinions on a broad spectrum of subjects and share their experiences through posts comprising multiple modes of expression, leading to a notable surge in such multimodal content on social media platforms. Nonetheless, accurately forecasting the popularity of these posts presents a considerable challenge. Prevailing methodologies primarily center on the content itself, thereby overlooking the wealth of information encapsulated within alternative modalities such as visual demographics, sentiments conveyed through hashtags and adequately modeling the intricate relationships among hashtags, texts, and accompanying images. This oversight limits the ability to capture emotional connection and audience relevance, significantly influencing post popularity. To address these limitations, we propose a seNtiment and hAshtag-aware attentive deep neuRal netwoRk for multimodAl posT pOpularity pRediction, herein referred to as NARRATOR that extracts visual demographics from faces appearing in images and discerns sentiment from hashtag usage, providing a more comprehensive understanding of the factors influencing post popularity Moreover, we introduce a hashtag-guided attention mechanism that leverages hashtags as navigational cues, guiding the models focus toward the most pertinent features of textual and visual modalities, thus aligning with target audience interests and broader social media context. Experimental results demonstrate that NARRATOR outperforms existing methods by a significant margin on two real-world datasets. Furthermore, ablation studies underscore the efficacy of integrating visual demographics, sentiment analysis of hashtags, and hashtag-guided attention mechanisms in enhancing the performance of post popularity prediction, thereby facilitating increased audience relevance, emotional engagement, and aesthetic appeal.
Movie Recommendation using Web Crawling
Authors: Pronit Raj, Chandrashekhar Kumar, Harshit Shekhar, Amit Kumar, Kritibas Paul, Debasish Jana
Date: 2024-12-14 00:00:00
Tasks: Collaborative Filtering, Movie Recommendation

In today's digital world, streaming platforms offer a vast array of movies, making it hard for users to find content matching their preferences. This paper explores integrating real time data from popular movie websites using advanced HTML scraping techniques and APIs. It also incorporates a recommendation system trained on a static Kaggle dataset, enhancing the relevance and freshness of suggestions. By combining content based filtering, collaborative filtering, and a hybrid model, we create a system that utilizes both historical and real time data for more personalized suggestions. Our methodology shows that incorporating dynamic data not only boosts user satisfaction but also aligns recommendations with current viewing trends.
Beyond Quantile Methods: Improved Top-K Threshold Estimation for Traditional and Learned Sparse Indexes
Authors: Jinrui Gou, Yifan Liu, Minghao Shao, Torsten Suel
Date: 2024-12-14 00:00:00

Top-k threshold estimation is the problem of estimating the score of the k-th highest ranking result of a search query. A good estimate can be used to speed up many common top-k query processing algorithms, and thus a number of researchers have recently studied the problem. Among the various approaches that have been proposed, quantile methods appear to give the best estimates overall at modest computational costs, followed by sampling-based methods in certain cases. In this paper, we make two main contributions. First, we study how to get even better estimates than the state of the art. Starting from quantile-based methods, we propose a series of enhancements that give improved estimates in terms of the commonly used mean under-prediction fraction (MUF). Second, we study the threshold estimation problem on recently proposed learned sparse index structures, showing that our methods also work well for these cases. Our best methods substantially narrow the gap between the state of the art and the ideal MUF of 1.0, at some additional cost in time and space.
USM: Unbiased Survey Modeling for Limiting Negative User Experiences in Recommendation Systems
Authors: Chenghui Yu, Peiyi Li, Haoze Wu, Bingfeng Deng, Hongyu Xiong
Date: 2024-12-14 00:00:00
Tasks: Recommendation Systems

Negative feedback signals are crucial to guardrail content recommendations and improve user experience. When these signals are effectively integrated into recommendation systems, they play a vital role in preventing the promotion of harmful or undesirable content, thereby contributing to a healthier online environment. However, the challenges associated with negative signals are noteworthy. Due to the limited visibility of options for users to express negative feedback, these signals are often sparse compared to positive signals. This imbalance can lead to a skewed understanding of user preferences, resulting in recommendations that prioritize short-term engagement over long-term satisfaction. Moreover, an over-reliance on positive signals can create a filter bubble, where users are continuously exposed to content that aligns with their immediate preferences but may not be beneficial in the long run. This scenario can ultimately lead to user attrition as audiences become disillusioned with the quality of the content provided. Additionally, existing user signals frequently fail to meet specific customized requirements, such as understanding the underlying reasons for a user's likes or dislikes regarding a video. This lack of granularity hinders our ability to tailor content recommendations effectively, as we cannot identify the particular attributes of content that resonate with individual users.
Recommendation and Temptation
Authors: Md Sanzeed Anwar, Paramveer S. Dhillon, Grant Schoenebeck
Date: 2024-12-13 00:00:00
Tasks: Recommendation Systems

Traditional recommender systems based on utility maximization and revealed preferences often fail to capture users' dual-self nature, where consumption choices are driven by both long-term benefits (enrichment) and desire for instant gratification (temptation). Consequently, these systems may generate recommendations that fail to provide long-lasting satisfaction to users. To address this issue, we propose a novel user model that accounts for this dual-self behavior and develop an optimal recommendation strategy to maximize enrichment from consumption. We highlight the limitations of historical consumption data in implementing this strategy and present an estimation framework that makes minimal assumptions and leverages explicit user feedback and implicit choice data to overcome these constraints. We evaluate our approach through both synthetic simulations and simulations based on real-world data from the MovieLens dataset. Results demonstrate that our proposed recommender can deliver superior enrichment compared to several competitive baseline algorithms that assume a single utility type and rely solely on revealed preferences. Our work emphasizes the critical importance of optimizing for enrichment in recommender systems, particularly in temptation-laden consumption contexts. Our findings have significant implications for content platforms, user experience design, and the development of responsible AI systems, paving the way for more nuanced and user-centric recommendation approaches.
Agro-STAY : Collecte de données et analyse des informations en agriculture alternative issues de YouTube
Authors: Laura Maxim, Julien Rabatel, Jean-Marc Douguet, Natalia Grabar, Roberto Interdonato, Sébastien Loustau, Mathieu Roche, Maguelonne Teisseire
Date: 2024-12-13 00:00:00

To address the current crises (climatic, social, economic), the self-sufficiency -- a set of practices that combine energy sobriety, self-production of food and energy, and self-construction - arouses an increasing interest. The CNRS STAY project (Savoirs Techniques pour l'Auto-suffisance, sur YouTube) explores this topic by analyzing techniques shared on YouTube. We present Agro-STAY, a platform designed for the collection, processing, and visualization of data from YouTube videos and their comments. We use Natural Language Processing (NLP) techniques and language models, which enable a fine-grained analysis of alternative agricultural practice described online. -- Face aux crises actuelles (climatiques, sociales, \'economiques), l'auto-suffisance -- ensemble de pratiques combinant sobri\'et\'e \'energ\'etique, autoproduction alimentaire et \'energ\'etique et autoconstruction - suscite un int\'er\^et croissant. Le projet CNRS STAY (Savoirs Techniques pour l'Auto-suffisance, sur YouTube) s'inscrit dans ce domaine en analysant les savoirs techniques diffus\'es sur YouTube. Nous pr\'esentons Agro-STAY, une plateforme d\'edi\'ee \`a la collecte, au traitement et \`a la visualisation de donn\'ees issues de vid\'eos YouTube et de leurs commentaires. En mobilisant des techniques de traitement automatique des langues (TAL) et des mod\`eles de langues, ce travail permet une analyse fine des pratiques agricoles alternatives d\'ecrites en ligne.
CRS Arena: Crowdsourced Benchmarking of Conversational Recommender Systems
Authors: Nolwenn Bernard, Hideaki Joko, Faegheh Hasibi, Krisztian Balog
Date: 2024-12-13 00:00:00
Tasks: Benchmarking, Recommendation Systems

We introduce CRS Arena, a research platform for scalable benchmarking of Conversational Recommender Systems (CRS) based on human feedback. The platform displays pairwise battles between anonymous conversational recommender systems, where users interact with the systems one after the other before declaring either a winner or a draw. CRS Arena collects conversations and user feedback, providing a foundation for reliable evaluation and ranking of CRSs. We conduct experiments with CRS Arena on both open and closed crowdsourcing platforms, confirming that both setups produce highly correlated rankings of CRSs and conversations with similar characteristics. We release CRSArena-Dial, a dataset of 474 conversations and their corresponding user feedback, along with a preliminary ranking of the systems based on the Elo rating system. The platform is accessible at https://iai-group-crsarena.hf.space/.
The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks
Authors: Ziqian Zhong, Ziming Liu, Max Tegmark, Jacob Andreas
Date: 2023-06-30 00:00:00
Proceeding: NeurIPS 2023 11
Tasks: Diversity

Do neural networks, trained on well-understood algorithmic tasks, reliably rediscover known algorithms for solving those tasks? Several recent studies, on tasks ranging from group arithmetic to in-context linear regression, have suggested that the answer is yes. Using modular addition as a prototypical problem, we show that algorithm discovery in neural networks is sometimes more complex. Small changes to model hyperparameters and initializations can induce the discovery of qualitatively different algorithms from a fixed training set, and even parallel implementations of multiple such algorithms. Some networks trained to perform modular addition implement a familiar Clock algorithm; others implement a previously undescribed, less intuitive, but comprehensible procedure which we term the Pizza algorithm, or a variety of even more complex procedures. Our results show that even simple learning problems can admit a surprising diversity of solutions, motivating the development of new tools for characterizing the behavior of neural networks across their algorithmic phase space.