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Scaling Laws For Dense Retrieval arXiv.cs.IR Pub Date : 2024-03-27 Yan Fang, Jingtao Zhan, Qingyao Ai, Jiaxin Mao, Weihang Su, Jia Chen, Yiqun Liu
Scaling up neural models has yielded significant advancements in a wide array of tasks, particularly in language generation. Previous studies have found that the performance of neural models frequently adheres to predictable scaling laws, correlated with factors such as training set size and model size. This insight is invaluable, especially as large-scale experiments grow increasingly resource-intensive
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Improving Content Recommendation: Knowledge Graph-Based Semantic Contrastive Learning for Diversity and Cold-Start Users arXiv.cs.IR Pub Date : 2024-03-27 Yejin Kim, Scott Rome, Kevin Foley, Mayur Nankani, Rimon Melamed, Javier Morales, Abhay Yadav, Maria Peifer, Sardar Hamidian, H. Howie Huang
Addressing the challenges related to data sparsity, cold-start problems, and diversity in recommendation systems is both crucial and demanding. Many current solutions leverage knowledge graphs to tackle these issues by combining both item-based and user-item collaborative signals. A common trend in these approaches focuses on improving ranking performance at the cost of escalating model complexity
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To Recommend or Not: Recommendability Identification in Conversations with Pre-trained Language Models arXiv.cs.IR Pub Date : 2024-03-27 Zhefan Wang, Weizhi Ma, Min Zhang
Most current recommender systems primarily focus on what to recommend, assuming users always require personalized recommendations. However, with the widely spread of ChatGPT and other chatbots, a more crucial problem in the context of conversational systems is how to minimize user disruption when we provide recommendation services for users. While previous research has extensively explored different
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Modeling Sustainable City Trips: Integrating CO2 Emissions, Popularity, and Seasonality into Tourism Recommender Systems arXiv.cs.IR Pub Date : 2024-03-27 Ashmi Banerjee, Tunar Mahmudov, Emil Adler, Fitri Nur Aisyah, Wolfgang Wörndl
In an era of information overload and complex decision-making processes, Recommender Systems (RS) have emerged as indispensable tools across diverse domains, particularly travel and tourism. These systems simplify trip planning by offering personalized recommendations that consider individual preferences and address broader challenges like seasonality, travel regulations, and capacity constraints.
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A Novel Behavior-Based Recommendation System for E-commerce arXiv.cs.IR Pub Date : 2024-03-27 Reza Barzegar Nozari, Mahdi Divsalar, Sepehr Akbarzadeh Abkenar, Mohammadreza Fadavi Amiri, Ali Divsalar
The majority of existing recommender systems rely on user ratings, which are limited by the lack of user collaboration and the sparsity problem. To address these issues, this study proposes a behavior-based recommender system that leverages customers' natural behaviors, such as browsing and clicking, on e-commerce platforms. The proposed recommendation system involves clustering active customers, determining
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Enhanced Generative Recommendation via Content and Collaboration Integration arXiv.cs.IR Pub Date : 2024-03-27 Yidan Wang, Zhaochun Ren, Weiwei Sun, Jiyuan Yang, Zhixiang Liang, Xin Chen, Ruobing Xie, Su Yan, Xu Zhang, Pengjie Ren, Zhumin Chen, Xin Xin
Generative recommendation has emerged as a promising paradigm aimed at augmenting recommender systems with recent advancements in generative artificial intelligence. This task has been formulated as a sequence-to-sequence generation process, wherein the input sequence encompasses data pertaining to the user's previously interacted items, and the output sequence denotes the generative identifier for
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Lightweight Embeddings for Graph Collaborative Filtering arXiv.cs.IR Pub Date : 2024-03-27 Xurong Liang, Tong Chen, Lizhen Cui, Yang Wang, Meng Wang, Hongzhi Yin
Graph neural networks (GNNs) are currently one of the most performant collaborative filtering methods. Meanwhile, owing to the use of an embedding table to represent each user/item as a distinct vector, GNN-based recommenders have inherited the long-standing defect of parameter inefficiency. As a common practice for scalable embeddings, parameter sharing enables the use of fewer embedding vectors (i
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Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability arXiv.cs.IR Pub Date : 2024-03-27 Nuo Chen, Jiqun Liu, Hanpei Fang, Yuankai Luo, Tetsuya Sakai, Xiao-Ming Wu
This study examines the decoy effect's underexplored influence on user search interactions and methods for measuring information retrieval (IR) systems' vulnerability to this effect. It explores how decoy results alter users' interactions on search engine result pages, focusing on metrics like click-through likelihood, browsing time, and perceived document usefulness. By analyzing user interaction
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DELTA: Pre-train a Discriminative Encoder for Legal Case Retrieval via Structural Word Alignment arXiv.cs.IR Pub Date : 2024-03-27 Haitao Li, Qingyao Ai, Xinyan Han, Jia Chen, Qian Dong, Yiqun Liu, Chong Chen, Qi Tian
Recent research demonstrates the effectiveness of using pre-trained language models for legal case retrieval. Most of the existing works focus on improving the representation ability for the contextualized embedding of the [CLS] token and calculate relevance using textual semantic similarity. However, in the legal domain, textual semantic similarity does not always imply that the cases are relevant
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Sequential Recommendation with Latent Relations based on Large Language Model arXiv.cs.IR Pub Date : 2024-03-27 Shenghao Yang, Weizhi Ma, Peijie Sun, Qingyao Ai, Yiqun Liu, Mingchen Cai, Min Zhang
Sequential recommender systems predict items that may interest users by modeling their preferences based on historical interactions. Traditional sequential recommendation methods rely on capturing implicit collaborative filtering signals among items. Recent relation-aware sequential recommendation models have achieved promising performance by explicitly incorporating item relations into the modeling
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Common Sense Enhanced Knowledge-based Recommendation with Large Language Model arXiv.cs.IR Pub Date : 2024-03-27 Shenghao Yang, Weizhi Ma, Peijie Sun, Min Zhang, Qingyao Ai, Yiqun Liu, Mingchen Cai
Knowledge-based recommendation models effectively alleviate the data sparsity issue leveraging the side information in the knowledge graph, and have achieved considerable performance. Nevertheless, the knowledge graphs used in previous work, namely metadata-based knowledge graphs, are usually constructed based on the attributes of items and co-occurring relations (e.g., also buy), in which the former
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A Situation-aware Enhancer for Personalized Recommendation arXiv.cs.IR Pub Date : 2024-03-27 Jiayu Li, Peijie Sun, Chumeng Jiang, Weizhi Ma, Qingyao Ai, Min Zhang
When users interact with Recommender Systems (RecSys), current situations, such as time, location, and environment, significantly influence their preferences. Situations serve as the background for interactions, where relationships between users and items evolve with situation changes. However, existing RecSys treat situations, users, and items on the same level. They can only model the relations between
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A Recommender System for NFT Collectibles with Item Feature arXiv.cs.IR Pub Date : 2024-03-27 Minjoo Choi, Seonmi Kim, Yejin Kim, Youngbin Lee, Joohwan Hong, Yongjae Lee
Recommender systems have been actively studied and applied in various domains to deal with information overload. Although there are numerous studies on recommender systems for movies, music, and e-commerce, comparatively less attention has been paid to the recommender system for NFTs despite the continuous growth of the NFT market. This paper presents a recommender system for NFTs that utilizes a variety
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Improving Out-of-Vocabulary Handling in Recommendation Systems arXiv.cs.IR Pub Date : 2024-03-27 William Shiao, Mingxuan Ju, Zhichun Guo, Xin Chen, Evangelos Papalexakis, Tong Zhao, Neil Shah, Yozen Liu
Recommendation systems (RS) are an increasingly relevant area for both academic and industry researchers, given their widespread impact on the daily online experiences of billions of users. One common issue in real RS is the cold-start problem, where users and items may not contain enough information to produce high-quality recommendations. This work focuses on a complementary problem: recommending
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RankMamba, Benchmarking Mamba's Document Ranking Performance in the Era of Transformers arXiv.cs.IR Pub Date : 2024-03-27 Zhichao Xu
Transformer structure has achieved great success in multiple applied machine learning communities, such as natural language processing (NLP), computer vision (CV) and information retrieval (IR). Transformer architecture's core mechanism -- attention requires $O(n^2)$ time complexity in training and $O(n)$ time complexity in inference. Many works have been proposed to improve the attention mechanism's
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One Backpropagation in Two Tower Recommendation Models arXiv.cs.IR Pub Date : 2024-03-27 Erjia Chen, Bang Wang
Recent years have witnessed extensive researches on developing two tower recommendation models for relieving information overload. Four building modules can be identified in such models, namely, user-item encoding, negative sampling, loss computing and back-propagation updating. To the best of our knowledge, existing algorithms have researched only on the first three modules, yet neglecting the backpropagation
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Antitrust, Amazon, and Algorithmic Auditing arXiv.cs.IR Pub Date : 2024-03-27 Abhisek Dash, Abhijnan Chakraborty, Saptarshi Ghosh, Animesh Mukherjee, Jens Frankenreiter, Stefan Bechtold, Krishna P. Gummadi
In digital markets, antitrust law and special regulations aim to ensure that markets remain competitive despite the dominating role that digital platforms play today in everyone's life. Unlike traditional markets, market participant behavior is easily observable in these markets. We present a series of empirical investigations into the extent to which Amazon engages in practices that are typically
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Can AI Models Appreciate Document Aesthetics? An Exploration of Legibility and Layout Quality in Relation to Prediction Confidence arXiv.cs.IR Pub Date : 2024-03-27 Hsiu-Wei Yang, Abhinav Agrawal, Pavlos Fragkogiannis, Shubham Nitin Mulay
A well-designed document communicates not only through its words but also through its visual eloquence. Authors utilize aesthetic elements such as colors, fonts, graphics, and layouts to shape the perception of information. Thoughtful document design, informed by psychological insights, enhances both the visual appeal and the comprehension of the content. While state-of-the-art document AI models demonstrate
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LLMs in HCI Data Work: Bridging the Gap Between Information Retrieval and Responsible Research Practices arXiv.cs.IR Pub Date : 2024-03-27 Neda Taghizadeh Serajeh, Iman Mohammadi, Vittorio Fuccella, Mattia De Rosa
Efficient and accurate information extraction from scientific papers is significant in the rapidly developing human-computer interaction research in the literature review process. Our paper introduces and analyses a new information retrieval system using state-of-the-art Large Language Models (LLMs) in combination with structured text analysis techniques to extract experimental data from HCI literature
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Cognitively Biased Users Interacting with Algorithmically Biased Results in Whole-Session Search on Controversial Topics arXiv.cs.IR Pub Date : 2024-03-26 Ben Wang, Jiqun Liu
When interacting with information retrieval (IR) systems, users, affected by confirmation biases, tend to select search results that confirm their existing beliefs on socially significant contentious issues. To understand the judgments and attitude changes of users searching online, our study examined how cognitively biased users interact with algorithmically biased search engine result pages (SERPs)
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Search and Society: Reimagining Information Access for Radical Futures arXiv.cs.IR Pub Date : 2024-03-26 Bhaskar Mitra
Information retrieval (IR) technologies and research are undergoing transformative changes. It is our perspective that the community should accept this opportunity to re-center our research agendas on societal needs while dismantling the artificial separation between the work on fairness, accountability, transparency, and ethics in IR and the rest of IR research. Instead of adopting a reactionary strategy
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MIND Your Language: A Multilingual Dataset for Cross-lingual News Recommendation arXiv.cs.IR Pub Date : 2024-03-26 Andreea Iana, Goran Glavaš, Heiko Paulheim
Digital news platforms use news recommenders as the main instrument to cater to the individual information needs of readers. Despite an increasingly language-diverse online community, in which many Internet users consume news in multiple languages, the majority of news recommendation focuses on major, resource-rich languages, and English in particular. Moreover, nearly all news recommendation efforts
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CaseLink: Inductive Graph Learning for Legal Case Retrieval arXiv.cs.IR Pub Date : 2024-03-26 Yanran Tang, Ruihong Qiu, Hongzhi Yin, Xue Li, Zi Huang
In case law, the precedents are the relevant cases that are used to support the decisions made by the judges and the opinions of lawyers towards a given case. This relevance is referred to as the case-to-case reference relation. To efficiently find relevant cases from a large case pool, retrieval tools are widely used by legal practitioners. Existing legal case retrieval models mainly work by comparing
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TWOLAR: a TWO-step LLM-Augmented distillation method for passage Reranking arXiv.cs.IR Pub Date : 2024-03-26 Davide Baldelli, Junfeng Jiang, Akiko Aizawa, Paolo Torroni
In this paper, we present TWOLAR: a two-stage pipeline for passage reranking based on the distillation of knowledge from Large Language Models (LLM). TWOLAR introduces a new scoring strategy and a distillation process consisting in the creation of a novel and diverse training dataset. The dataset consists of 20K queries, each associated with a set of documents retrieved via four distinct retrieval
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Large Language Models Enhanced Collaborative Filtering arXiv.cs.IR Pub Date : 2024-03-26 Zhongxiang Sun, Zihua Si, Xiaoxue Zang, Kai Zheng, Yang Song, Xiao Zhang, Jun Xu
Recent advancements in Large Language Models (LLMs) have attracted considerable interest among researchers to leverage these models to enhance Recommender Systems (RSs). Existing work predominantly utilizes LLMs to generate knowledge-rich texts or utilizes LLM-derived embeddings as features to improve RSs. Al- though the extensive world knowledge embedded in LLMs generally benefits RSs, the application
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END4Rec: Efficient Noise-Decoupling for Multi-Behavior Sequential Recommendation arXiv.cs.IR Pub Date : 2024-03-26 Yongqiang Han, Hao Wang, Kefan Wang, Likang Wu, Zhi Li, Wei Guo, Yong Liu, Defu Lian, Enhong Chen
In recommendation systems, users frequently engage in multiple types of behaviors, such as clicking, adding to a cart, and purchasing. However, with diversified behavior data, user behavior sequences will become very long in the short term, which brings challenges to the efficiency of the sequence recommendation model. Meanwhile, some behavior data will also bring inevitable noise to the modeling of
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Document Set Expansion with Positive-Unlabelled Learning Using Intractable Density Estimation arXiv.cs.IR Pub Date : 2024-03-26 Haiyang Zhang, Qiuyi Chen, Yuanjie Zou, Yushan Pan, Jia Wang, Mark Stevenson
The Document Set Expansion (DSE) task involves identifying relevant documents from large collections based on a limited set of example documents. Previous research has highlighted Positive and Unlabeled (PU) learning as a promising approach for this task. However, most PU methods rely on the unrealistic assumption of knowing the class prior for positive samples in the collection. To address this limitation
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Touch the Core: Exploring Task Dependence Among Hybrid Targets for Recommendation arXiv.cs.IR Pub Date : 2024-03-26 Xing Tang, Yang Qiao, Fuyuan Lyu, Dugang Liu, Xiuqiang He
As user behaviors become complicated on business platforms, online recommendations focus more on how to touch the core conversions, which are highly related to the interests of platforms. These core conversions are usually continuous targets, such as \textit{watch time}, \textit{revenue}, and so on, whose predictions can be enhanced by previous discrete conversion actions. Therefore, multi-task learning
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AFDGCF: Adaptive Feature De-correlation Graph Collaborative Filtering for Recommendations arXiv.cs.IR Pub Date : 2024-03-26 Wei Wu, Chao Wang, Dazhong Shen, Chuan Qin, Liyi Chen, Hui Xiong
Collaborative filtering methods based on graph neural networks (GNNs) have witnessed significant success in recommender systems (RS), capitalizing on their ability to capture collaborative signals within intricate user-item relationships via message-passing mechanisms. However, these GNN-based RS inadvertently introduce excess linear correlation between user and item embeddings, contradicting the goal
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Multi-Domain Recommendation to Attract Users via Domain Preference Modeling arXiv.cs.IR Pub Date : 2024-03-26 Hyuunjun Ju, SeongKu Kang, Dongha Lee, Junyoung Hwang, Sanghwan Jang, Hwanjo Yu
Recently, web platforms have been operating various service domains simultaneously. Targeting a platform that operates multiple service domains, we introduce a new task, Multi-Domain Recommendation to Attract Users (MDRAU), which recommends items from multiple ``unseen'' domains with which each user has not interacted yet, by using knowledge from the user's ``seen'' domains. In this paper, we point
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An Empirical Study of Training ID-Agnostic Multi-modal Sequential Recommenders arXiv.cs.IR Pub Date : 2024-03-26 Youhua Li, Hanwen Du, Yongxin Ni, Yuanqi He, Junchen Fu, Xiangyan Liu, Qi Guo
Sequential Recommendation (SR) aims to predict future user-item interactions based on historical interactions. While many SR approaches concentrate on user IDs and item IDs, the human perception of the world through multi-modal signals, like text and images, has inspired researchers to delve into constructing SR from multi-modal information without using IDs. However, the complexity of multi-modal
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ArabicaQA: A Comprehensive Dataset for Arabic Question Answering arXiv.cs.IR Pub Date : 2024-03-26 Abdelrahman Abdallah, Mahmoud Kasem, Mahmoud Abdalla, Mohamed Mahmoud, Mohamed Elkasaby, Yasser Elbendary, Adam Jatowt
In this paper, we address the significant gap in Arabic natural language processing (NLP) resources by introducing ArabicaQA, the first large-scale dataset for machine reading comprehension and open-domain question answering in Arabic. This comprehensive dataset, consisting of 89,095 answerable and 3,701 unanswerable questions created by crowdworkers to look similar to answerable ones, along with additional
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S+t-SNE - Bringing dimensionality reduction to data streams arXiv.cs.IR Pub Date : 2024-03-26 Pedro C. Vieira, João P. Montrezol, João T. Vieira, João Gama
We present S+t-SNE, an adaptation of the t-SNE algorithm designed to handle infinite data streams. The core idea behind S+t-SNE is to update the t-SNE embedding incrementally as new data arrives, ensuring scalability and adaptability to handle streaming scenarios. By selecting the most important points at each step, the algorithm ensures scalability while keeping informative visualisations. Employing
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EXPLORA: A teacher-apprentice methodology for eliciting natural child-computer interactions arXiv.cs.IR Pub Date : 2024-03-25 Vanessa Figueiredo, Catherine Ann Cameron
Investigating child-computer interactions within their contexts is vital for designing technology that caters to children's needs. However, determining what aspects of context are relevant for designing child-centric technology remains a challenge. We introduce EXPLORA, a multimodal, multistage online methodology comprising three pivotal stages: (1) building a teacher-apprentice relationship,(2) learning
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Reinforcement Learning-based Recommender Systems with Large Language Models for State Reward and Action Modeling arXiv.cs.IR Pub Date : 2024-03-25 Jie Wang, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M. Jose
Reinforcement Learning (RL)-based recommender systems have demonstrated promising performance in meeting user expectations by learning to make accurate next-item recommendations from historical user-item interactions. However, existing offline RL-based sequential recommendation methods face the challenge of obtaining effective user feedback from the environment. Effectively modeling the user state
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GloSIS: The Global Soil Information System Web Ontology arXiv.cs.IR Pub Date : 2024-03-25 Raul Palma, Bogusz Janiak, Luís Moreira de Sousa, Kathi Schleidt, Tomáš Řezník, Fenny van Egmond, Johan Leenaars, Dimitrios Moshou, Abdul Mouazen, Peter Wilson, David Medyckyj-Scott, Alistair Ritchie, Yusuf Yigini, Ronald Vargas
Established in 2012 by members of the Food and Agriculture Organisation (FAO), the Global Soil Partnership (GSP) is a global network of stakeholders promoting sound land and soil management practices towards a sustainable world food system. However, soil survey largely remains a local or regional activity, bound to heterogeneous methods and conventions. Recognising the relevance of global and trans-national
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Play to Your Strengths: Collaborative Intelligence of Conventional Recommender Models and Large Language Models arXiv.cs.IR Pub Date : 2024-03-25 Yunjia Xi, Weiwen Liu, Jianghao Lin, Chuhan Wu, Bo Chen, Ruiming Tang, Weinan Zhang, Yong Yu
The rise of large language models (LLMs) has opened new opportunities in Recommender Systems (RSs) by enhancing user behavior modeling and content understanding. However, current approaches that integrate LLMs into RSs solely utilize either LLM or conventional recommender model (CRM) to generate final recommendations, without considering which data segments LLM or CRM excel in. To fill in this gap
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Uncovering Selective State Space Model's Capabilities in Lifelong Sequential Recommendation arXiv.cs.IR Pub Date : 2024-03-25 Jiyuan Yang, Yuanzi Li, Jingyu Zhao, Hanbing Wang, Muyang Ma, Jun Ma, Zhaochun Ren, Mengqi Zhang, Xin Xin, Zhumin Chen, Pengjie Ren
Sequential Recommenders have been widely applied in various online services, aiming to model users' dynamic interests from their sequential interactions. With users increasingly engaging with online platforms, vast amounts of lifelong user behavioral sequences have been generated. However, existing sequential recommender models often struggle to handle such lifelong sequences. The primary challenges
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RankingSHAP -- Listwise Feature Attribution Explanations for Ranking Models arXiv.cs.IR Pub Date : 2024-03-24 Maria Heuss, Maarten de Rijke, Avishek Anand
Feature attributions are a commonly used explanation type, when we want to posthoc explain the prediction of a trained model. Yet, they are not very well explored in IR. Importantly, feature attribution has rarely been rigorously defined, beyond attributing the most important feature the highest value. What it means for a feature to be more important than others is often left vague. Consequently, most
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Knowledge-aware Dual-side Attribute-enhanced Recommendation arXiv.cs.IR Pub Date : 2024-03-24 Taotian Pang, Xingyu Lou, Fei Zhao, Zhen Wu, Kuiyao Dong, Qiuying Peng, Yue Qi, Xinyu Dai
\textit{Knowledge-aware} recommendation methods (KGR) based on \textit{graph neural networks} (GNNs) and \textit{contrastive learning} (CL) have achieved promising performance. However, they fall short in modeling fine-grained user preferences and further fail to leverage the \textit{preference-attribute connection} to make predictions, leading to sub-optimal performance. To address the issue, we propose
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QueryExplorer: An Interactive Query Generation Assistant for Search and Exploration arXiv.cs.IR Pub Date : 2024-03-23 Kaustubh D. Dhole, Shivam Bajaj, Ramraj Chandradevan, Eugene Agichtein
Formulating effective search queries remains a challenging task, particularly when users lack expertise in a specific domain or are not proficient in the language of the content. Providing example documents of interest might be easier for a user. However, such query-by-example scenarios are prone to concept drift, and the retrieval effectiveness is highly sensitive to the query generation method, without
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ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search arXiv.cs.IR Pub Date : 2024-03-25 Zehan Li, Jianfei Zhang, Chuantao Yin, Yuanxin Ouyang, Wenge Rong
Retrieval-based code question answering seeks to match user queries in natural language to relevant code snippets. Previous approaches typically rely on pretraining models using crafted bi-modal and uni-modal datasets to align text and code representations. In this paper, we introduce ProCQA, a large-scale programming question answering dataset extracted from the StackOverflow community, offering naturally
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Graph Augmentation for Recommendation arXiv.cs.IR Pub Date : 2024-03-25 Qianru Zhang, Lianghao Xia, Xuheng Cai, Siuming Yiu, Chao Huang, Christian S. Jensen
Graph augmentation with contrastive learning has gained significant attention in the field of recommendation systems due to its ability to learn expressive user representations, even when labeled data is limited. However, directly applying existing GCL models to real-world recommendation environments poses challenges. There are two primary issues to address. Firstly, the lack of consideration for data
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LARA: Linguistic-Adaptive Retrieval-Augmented LLMs for Multi-Turn Intent Classification arXiv.cs.IR Pub Date : 2024-03-25 Liu Junhua, Tan Yong Keat, Fu Bin
Following the significant achievements of large language models (LLMs), researchers have employed in-context learning for text classification tasks. However, these studies focused on monolingual, single-turn classification tasks. In this paper, we introduce LARA (Linguistic-Adaptive Retrieval-Augmented Language Models), designed to enhance accuracy in multi-turn classification tasks across six languages
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InstUPR : Instruction-based Unsupervised Passage Reranking with Large Language Models arXiv.cs.IR Pub Date : 2024-03-25 Chao-Wei Huang, Yun-Nung Chen
This paper introduces InstUPR, an unsupervised passage reranking method based on large language models (LLMs). Different from existing approaches that rely on extensive training with query-document pairs or retrieval-specific instructions, our method leverages the instruction-following capabilities of instruction-tuned LLMs for passage reranking without any additional fine-tuning. To achieve this,
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An Experiment with the Use of ChatGPT for LCSH Subject Assignment on Electronic Theses and Dissertations arXiv.cs.IR Pub Date : 2024-03-25 Eric H. C. Chow, TJ Kao, Xiaoli Li
This study delves into the potential use of Large Language Models (LLMs) for generating Library of Congress Subject Headings (LCSH). The authors employed ChatGPT to generate subject headings for electronic theses and dissertations (ETDs) based on their titles and summaries. The results revealed that although some generated subject headings were valid, there were issues regarding specificity and exhaustiveness
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Enhanced Facet Generation with LLM Editing arXiv.cs.IR Pub Date : 2024-03-25 Joosung Lee, Jinhong Kim
In information retrieval, facet identification of a user query is an important task. If a search service can recognize the facets of a user's query, it has the potential to offer users a much broader range of search results. Previous studies can enhance facet prediction by leveraging retrieved documents and related queries obtained through a search engine. However, there are challenges in extending
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Ultra Low-Cost Two-Stage Multimodal System for Non-Normative Behavior Detection arXiv.cs.IR Pub Date : 2024-03-24 Albert Lu, Stephen Cranefield
The online community has increasingly been inundated by a toxic wave of harmful comments. In response to this growing challenge, we introduce a two-stage ultra-low-cost multimodal harmful behavior detection method designed to identify harmful comments and images with high precision and recall rates. We first utilize the CLIP-ViT model to transform tweets and images into embeddings, effectively capturing
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Model, Analyze, and Comprehend User Interactions and Various Attributes within a Social Media Platform arXiv.cs.IR Pub Date : 2024-03-23 Md Kaykobad Reza, S M Maksudul Alam, Yiran Luo, Youzhe Liu
How can we effectively model, analyze, and comprehend user interactions and various attributes within a social media platform based on post-comment relationship? In this study, we propose a novel graph-based approach to model and analyze user interactions within a social media platform based on post-comment relationship. We construct a user interaction graph from social media data and analyze it to
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Towards Human-Like Machine Comprehension: Few-Shot Relational Learning in Visually-Rich Documents arXiv.cs.IR Pub Date : 2024-03-23 Hao Wang, Tang Li, Chenhui Chu, Nengjun Zhu, Rui Wang, Pinpin Zhu
Key-value relations are prevalent in Visually-Rich Documents (VRDs), often depicted in distinct spatial regions accompanied by specific color and font styles. These non-textual cues serve as important indicators that greatly enhance human comprehension and acquisition of such relation triplets. However, current document AI approaches often fail to consider this valuable prior information related to
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Spectral Initialization for High-Dimensional Phase Retrieval with Biased Spatial Directions arXiv.cs.IR Pub Date : 2024-03-22 Pierre Bousseyroux, Marc Potters
We explore a spectral initialization method that plays a central role in contemporary research on signal estimation in nonconvex scenarios. In a noiseless phase retrieval framework, we precisely analyze the method's performance in the high-dimensional limit when sensing vectors follow a multivariate Gaussian distribution for two rotationally invariant models of the covariance matrix C. In the first
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Bilateral Unsymmetrical Graph Contrastive Learning for Recommendation arXiv.cs.IR Pub Date : 2024-03-22 Jiaheng Yu, Jing Li, Yue He, Kai Zhu, Shuyi Zhang, Wen Hu
Recent methods utilize graph contrastive Learning within graph-structured user-item interaction data for collaborative filtering and have demonstrated their efficacy in recommendation tasks. However, they ignore that the difference relation density of nodes between the user- and item-side causes the adaptability of graphs on bilateral nodes to be different after multi-hop graph interaction calculation
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Fundus: A Simple-to-Use News Scraper Optimized for High Quality Extractions arXiv.cs.IR Pub Date : 2024-03-22 Max Dallabetta, Conrad Dobberstein, Adrian Breiding, Alan Akbik
This paper introduces Fundus, a user-friendly news scraper that enables users to obtain millions of high-quality news articles with just a few lines of code. Unlike existing news scrapers, we use manually crafted, bespoke content extractors that are specifically tailored to the formatting guidelines of each supported online newspaper. This allows us to optimize our scraping for quality such that retrieved
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Understanding the Ranking Loss for Recommendation with Sparse User Feedback arXiv.cs.IR Pub Date : 2024-03-21 Zhutian Lin, Junwei Pan, Shangyu Zhang, Ximei Wang, Xi Xiao, Shudong Huang, Lei Xiao, Jie Jiang
Click-through rate (CTR) prediction holds significant importance in the realm of online advertising. While many existing approaches treat it as a binary classification problem and utilize binary cross entropy (BCE) as the optimization objective, recent advancements have indicated that combining BCE loss with ranking loss yields substantial performance improvements. However, the full efficacy of this
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FIT-RAG: Black-Box RAG with Factual Information and Token Reduction arXiv.cs.IR Pub Date : 2024-03-21 Yuren Mao, Xuemei Dong, Wenyi Xu, Yunjun Gao, Bin Wei, Ying Zhang
Due to the extraordinarily large number of parameters, fine-tuning Large Language Models (LLMs) to update long-tail or out-of-date knowledge is impractical in lots of applications. To avoid fine-tuning, we can alternatively treat a LLM as a black-box (i.e., freeze the parameters of the LLM) and augment it with a Retrieval-Augmented Generation (RAG) system, namely black-box RAG. Recently, black-box
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A Large Language Model Enhanced Sequential Recommender for Joint Video and Comment Recommendation arXiv.cs.IR Pub Date : 2024-03-20 Bowen Zheng, Zihan Lin, Enze Liu, Chen Yang, Enyang Bai, Cheng Ling, Wayne Xin Zhao, Ji-Rong Wen
In online video platforms, reading or writing comments on interesting videos has become an essential part of the video watching experience. However, existing video recommender systems mainly model users' interaction behaviors with videos, lacking consideration of comments in user behavior modeling. In this paper, we propose a novel recommendation approach called LSVCR by leveraging user interaction
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DESIRE-ME: Domain-Enhanced Supervised Information REtrieval using Mixture-of-Experts arXiv.cs.IR Pub Date : 2024-03-20 Pranav Kasela, Gabriella Pasi, Raffaele Perego, Nicola Tonellotto
Open-domain question answering requires retrieval systems able to cope with the diverse and varied nature of questions, providing accurate answers across a broad spectrum of query types and topics. To deal with such topic heterogeneity through a unique model, we propose DESIRE-ME, a neural information retrieval model that leverages the Mixture-of-Experts framework to combine multiple specialized neural
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Harnessing Large Language Models for Text-Rich Sequential Recommendation arXiv.cs.IR Pub Date : 2024-03-20 Zhi Zheng, Wenshuo Chao, Zhaopeng Qiu, Hengshu Zhu, Hui Xiong
Recent advances in Large Language Models (LLMs) have been changing the paradigm of Recommender Systems (RS). However, when items in the recommendation scenarios contain rich textual information, such as product descriptions in online shopping or news headlines on social media, LLMs require longer texts to comprehensively depict the historical user behavior sequence. This poses significant challenges
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Flickr30K-CFQ: A Compact and Fragmented Query Dataset for Text-image Retrieval arXiv.cs.IR Pub Date : 2024-03-20 Haoyu Liu, Yaoxian Song, Xuwu Wang, Zhu Xiangru, Zhixu Li, Wei Song, Tiefeng Li
With the explosive growth of multi-modal information on the Internet, unimodal search cannot satisfy the requirement of Internet applications. Text-image retrieval research is needed to realize high-quality and efficient retrieval between different modalities. Existing text-image retrieval research is mostly based on general vision-language datasets (e.g. MS-COCO, Flickr30K), in which the query utterance
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An Analysis on Matching Mechanisms and Token Pruning for Late-interaction Models arXiv.cs.IR Pub Date : 2024-03-20 Qi Liu, Gang Guo, Jiaxin Mao, Zhicheng Dou, Ji-Rong Wen, Hao Jiang, Xinyu Zhang, Zhao Cao
With the development of pre-trained language models, the dense retrieval models have become promising alternatives to the traditional retrieval models that rely on exact match and sparse bag-of-words representations. Different from most dense retrieval models using a bi-encoder to encode each query or document into a dense vector, the recently proposed late-interaction multi-vector models (i.e., ColBERT