arXiv - CS - Information Retrieval最新文献_第6页

E-commerce Webpage Recommendation Scheme Base on Semantic Mining and Neural Networks 基于语义挖掘和神经网络的电子商务网页推荐方案

arXiv - CS - Information Retrieval Pub Date : 2024-09-11 DOI: arxiv-2409.07033

Wenchao Zhao, Xiaoyi Liu, Ruilin Xu, Lingxi Xiao, Muqing Li

引用次数: 0

Leveraging User-Generated Reviews for Recommender Systems with Dynamic Headers 利用动态标题为推荐系统提供用户评论

arXiv - CS - Information Retrieval Pub Date : 2024-09-11 DOI: arxiv-2409.07627

Shanu Vashishtha, Abhay Kumar, Lalitesh Morishetti, Kaushiki Nag, Kannan Achan

{"title":"Leveraging User-Generated Reviews for Recommender Systems with Dynamic Headers","authors":"Shanu Vashishtha, Abhay Kumar, Lalitesh Morishetti, Kaushiki Nag, Kannan Achan","doi":"arxiv-2409.07627","DOIUrl":"https://doi.org/arxiv-2409.07627","url":null,"abstract":"E-commerce platforms have a vast catalog of items to cater to their\u0000customers' shopping interests. Most of these platforms assist their customers\u0000in the shopping process by offering optimized recommendation carousels,\u0000designed to help customers quickly locate their desired items. Many models have\u0000been proposed in academic literature to generate and enhance the ranking and\u0000recall set of items in these carousels. Conventionally, the accompanying\u0000carousel title text (header) of these carousels remains static. In most\u0000instances, a generic text such as \"Items similar to your current viewing\" is\u0000utilized. Fixed variations such as the inclusion of specific attributes \"Other\u0000items from a similar seller\" or \"Items from a similar brand\" in addition to\u0000\"frequently bought together\" or \"considered together\" are observed as well.\u0000This work proposes a novel approach to customize the header generation process\u0000of these carousels. Our work leverages user-generated reviews that lay focus on\u0000specific attributes (aspects) of an item that were favorably perceived by users\u0000during their interaction with the given item. We extract these aspects from\u0000reviews and train a graph neural network-based model under the framework of a\u0000conditional ranking task. We refer to our innovative methodology as Dynamic\u0000Text Snippets (DTS) which generates multiple header texts for an anchor item\u0000and its recall set. Our approach demonstrates the potential of utilizing\u0000user-generated reviews and presents a unique paradigm for exploring\u0000increasingly context-aware recommendation systems.","PeriodicalId":501281,"journal":{"name":"arXiv - CS - Information Retrieval","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dot Product is All You Need: Bridging the Gap Between Item Recommendation and Link Prediction 点产品就是你所需要的一切：缩小项目推荐与链接预测之间的差距

arXiv - CS - Information Retrieval Pub Date : 2024-09-11 DOI: arxiv-2409.07433

Daniele Malitesta, Alberto Carlo Maria Mancino, Pasquale Minervini, Tommaso Di Noia

{"title":"Dot Product is All You Need: Bridging the Gap Between Item Recommendation and Link Prediction","authors":"Daniele Malitesta, Alberto Carlo Maria Mancino, Pasquale Minervini, Tommaso Di Noia","doi":"arxiv-2409.07433","DOIUrl":"https://doi.org/arxiv-2409.07433","url":null,"abstract":"Item recommendation (the task of predicting if a user may interact with new\u0000items from the catalogue in a recommendation system) and link prediction (the\u0000task of identifying missing links in a knowledge graph) have long been regarded\u0000as distinct problems. In this work, we show that the item recommendation\u0000problem can be seen as an instance of the link prediction problem, where\u0000entities in the graph represent users and items, and the task consists of\u0000predicting missing instances of the relation type <<interactsWith>>. In a\u0000preliminary attempt to demonstrate the assumption, we decide to test three\u0000popular factorisation-based link prediction models on the item recommendation\u0000task, showing that their predictive accuracy is competitive with ten\u0000state-of-the-art recommendation models. The purpose is to show how the former\u0000may be seamlessly and effectively applied to the recommendation task without\u0000any specific modification to their architectures. Finally, while beginning to\u0000unveil the key reasons behind the recommendation performance of the selected\u0000link prediction models, we explore different settings for their hyper-parameter\u0000values, paving the way for future directions.","PeriodicalId":501281,"journal":{"name":"arXiv - CS - Information Retrieval","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multilingual Prompts in LLM-Based Recommenders: Performance Across Languages 基于 LLM 的推荐器中的多语言提示：跨语言性能

arXiv - CS - Information Retrieval Pub Date : 2024-09-11 DOI: arxiv-2409.07604

Makbule Gulcin Ozsoy

{"title":"Multilingual Prompts in LLM-Based Recommenders: Performance Across Languages","authors":"Makbule Gulcin Ozsoy","doi":"arxiv-2409.07604","DOIUrl":"https://doi.org/arxiv-2409.07604","url":null,"abstract":"Large language models (LLMs) are increasingly used in natural language\u0000processing tasks. Recommender systems traditionally use methods such as\u0000collaborative filtering and matrix factorization, as well as advanced\u0000techniques like deep learning and reinforcement learning. Although language\u0000models have been applied in recommendation, the recent trend have focused on\u0000leveraging the generative capabilities of LLMs for more personalized\u0000suggestions. While current research focuses on English due to its resource\u0000richness, this work explores the impact of non-English prompts on\u0000recommendation performance. Using OpenP5, a platform for developing and\u0000evaluating LLM-based recommendations, we expanded its English prompt templates\u0000to include Spanish and Turkish. Evaluation on three real-world datasets, namely\u0000ML1M, LastFM, and Amazon-Beauty, showed that usage of non-English prompts\u0000generally reduce performance, especially in less-resourced languages like\u0000Turkish. We also retrained an LLM-based recommender model with multilingual\u0000prompts to analyze performance variations. Retraining with multilingual prompts\u0000resulted in more balanced performance across languages, but slightly reduced\u0000English performance. This work highlights the need for diverse language support\u0000in LLM-based recommenders and suggests future research on creating evaluation\u0000datasets, using newer models and additional languages.","PeriodicalId":501281,"journal":{"name":"arXiv - CS - Information Retrieval","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205297","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

STORE: Streamlining Semantic Tokenization and Generative Recommendation with A Single LLM STORE：利用单一 LLM 精简语义标记化和生成式推荐

arXiv - CS - Information Retrieval Pub Date : 2024-09-11 DOI: arxiv-2409.07276

Qijiong Liu, Jieming Zhu, Lu Fan, Zhou Zhao, Xiao-Ming Wu

{"title":"STORE: Streamlining Semantic Tokenization and Generative Recommendation with A Single LLM","authors":"Qijiong Liu, Jieming Zhu, Lu Fan, Zhou Zhao, Xiao-Ming Wu","doi":"arxiv-2409.07276","DOIUrl":"https://doi.org/arxiv-2409.07276","url":null,"abstract":"Traditional recommendation models often rely on unique item identifiers (IDs)\u0000to distinguish between items, which can hinder their ability to effectively\u0000leverage item content information and generalize to long-tail or cold-start\u0000items. Recently, semantic tokenization has been proposed as a promising\u0000solution that aims to tokenize each item's semantic representation into a\u0000sequence of discrete tokens. In this way, it preserves the item's semantics\u0000within these tokens and ensures that semantically similar items are represented\u0000by similar tokens. These semantic tokens have become fundamental in training\u0000generative recommendation models. However, existing generative recommendation\u0000methods typically involve multiple sub-models for embedding, quantization, and\u0000recommendation, leading to an overly complex system. In this paper, we propose\u0000to streamline the semantic tokenization and generative recommendation process\u0000with a unified framework, dubbed STORE, which leverages a single large language\u0000model (LLM) for both tasks. Specifically, we formulate semantic tokenization as\u0000a text-to-token task and generative recommendation as a token-to-token task,\u0000supplemented by a token-to-text reconstruction task and a text-to-token\u0000auxiliary task. All these tasks are framed in a generative manner and trained\u0000using a single LLM backbone. Extensive experiments have been conducted to\u0000validate the effectiveness of our STORE framework across various recommendation\u0000tasks and datasets. We will release the source code and configurations for\u0000reproducible research.","PeriodicalId":501281,"journal":{"name":"arXiv - CS - Information Retrieval","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Diff-VPS: Video Polyp Segmentation via a Multi-task Diffusion Network with Adversarial Temporal Reasoning Diff-VPS：通过多任务扩散网络与对抗性时态推理进行视频息肉分割

arXiv - CS - Information Retrieval Pub Date : 2024-09-11 DOI: arxiv-2409.07238

Yingling Lu, Yijun Yang, Zhaohu Xing, Qiong Wang, Lei Zhu

{"title":"Diff-VPS: Video Polyp Segmentation via a Multi-task Diffusion Network with Adversarial Temporal Reasoning","authors":"Yingling Lu, Yijun Yang, Zhaohu Xing, Qiong Wang, Lei Zhu","doi":"arxiv-2409.07238","DOIUrl":"https://doi.org/arxiv-2409.07238","url":null,"abstract":"Diffusion Probabilistic Models have recently attracted significant attention\u0000in the community of computer vision due to their outstanding performance.\u0000However, while a substantial amount of diffusion-based research has focused on\u0000generative tasks, no work introduces diffusion models to advance the results of\u0000polyp segmentation in videos, which is frequently challenged by polyps' high\u0000camouflage and redundant temporal cues.In this paper, we present a novel\u0000diffusion-based network for video polyp segmentation task, dubbed as Diff-VPS.\u0000We incorporate multi-task supervision into diffusion models to promote the\u0000discrimination of diffusion models on pixel-by-pixel segmentation. This\u0000integrates the contextual high-level information achieved by the joint\u0000classification and detection tasks. To explore the temporal dependency,\u0000Temporal Reasoning Module (TRM) is devised via reasoning and reconstructing the\u0000target frame from the previous frames. We further equip TRM with a generative\u0000adversarial self-supervised strategy to produce more realistic frames and thus\u0000capture better dynamic cues. Extensive experiments are conducted on SUN-SEG,\u0000and the results indicate that our proposed Diff-VPS significantly achieves\u0000state-of-the-art performance. Code is available at\u0000https://github.com/lydia-yllu/Diff-VPS.","PeriodicalId":501281,"journal":{"name":"arXiv - CS - Information Retrieval","volume":"67 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205321","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Negative Sampling in Recommendation: A Survey and Future Directions 建议书中的负抽样：调查与未来方向

arXiv - CS - Information Retrieval Pub Date : 2024-09-11 DOI: arxiv-2409.07237

Haokai Ma, Ruobing Xie, Lei Meng, Fuli Feng, Xiaoyu Du, Xingwu Sun, Zhanhui Kang, Xiangxu Meng

{"title":"Negative Sampling in Recommendation: A Survey and Future Directions","authors":"Haokai Ma, Ruobing Xie, Lei Meng, Fuli Feng, Xiaoyu Du, Xingwu Sun, Zhanhui Kang, Xiangxu Meng","doi":"arxiv-2409.07237","DOIUrl":"https://doi.org/arxiv-2409.07237","url":null,"abstract":"Recommender systems aim to capture users' personalized preferences from the\u0000cast amount of user behaviors, making them pivotal in the era of information\u0000explosion. However, the presence of the dynamic preference, the \"information\u0000cocoons\", and the inherent feedback loops in recommendation make users interact\u0000with a limited number of items. Conventional recommendation algorithms\u0000typically focus on the positive historical behaviors, while neglecting the\u0000essential role of negative feedback in user interest understanding. As a\u0000promising but easy-to-ignored area, negative sampling is proficients in\u0000revealing the genuine negative aspect inherent in user behaviors, emerging as\u0000an inescapable procedure in recommendation. In this survey, we first discuss\u0000the role of negative sampling in recommendation and thoroughly analyze\u0000challenges that consistently impede its progress. Then, we conduct an extensive\u0000literature review on the existing negative sampling strategies in\u0000recommendation and classify them into five categories with their discrepant\u0000techniques. Finally, we detail the insights of the tailored negative sampling\u0000strategies in diverse recommendation scenarios and outline an overview of the\u0000prospective research directions toward which the community may engage and\u0000benefit.","PeriodicalId":501281,"journal":{"name":"arXiv - CS - Information Retrieval","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation 用于时空抽象的列表式推荐的分层强化学习

arXiv - CS - Information Retrieval Pub Date : 2024-09-11 DOI: arxiv-2409.07416

Luo Ji, Gao Liu, Mingyang Yin, Hongxia Yang, Jingren Zhou

{"title":"Hierarchical Reinforcement Learning for Temporal Abstraction of Listwise Recommendation","authors":"Luo Ji, Gao Liu, Mingyang Yin, Hongxia Yang, Jingren Zhou","doi":"arxiv-2409.07416","DOIUrl":"https://doi.org/arxiv-2409.07416","url":null,"abstract":"Modern listwise recommendation systems need to consider both long-term user\u0000perceptions and short-term interest shifts. Reinforcement learning can be\u0000applied on recommendation to study such a problem but is also subject to large\u0000search space, sparse user feedback and long interactive latency. Motivated by\u0000recent progress in hierarchical reinforcement learning, we propose a novel\u0000framework called mccHRL to provide different levels of temporal abstraction on\u0000listwise recommendation. Within the hierarchical framework, the high-level\u0000agent studies the evolution of user perception, while the low-level agent\u0000produces the item selection policy by modeling the process as a sequential\u0000decision-making problem. We argue that such framework has a well-defined\u0000decomposition of the outra-session context and the intra-session context, which\u0000are encoded by the high-level and low-level agents, respectively. To verify\u0000this argument, we implement both a simulator-based environment and an\u0000industrial dataset-based experiment. Results observe significant performance\u0000improvement by our method, compared with several well-known baselines. Data and\u0000codes have been made public.","PeriodicalId":501281,"journal":{"name":"arXiv - CS - Information Retrieval","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive Learning 利用负反馈信息对比学习改进序列音乐推荐

arXiv - CS - Information Retrieval Pub Date : 2024-09-11 DOI: arxiv-2409.07367

Pavan Seshadri, Shahrzad Shashaani, Peter Knees

{"title":"Enhancing Sequential Music Recommendation with Negative Feedback-informed Contrastive Learning","authors":"Pavan Seshadri, Shahrzad Shashaani, Peter Knees","doi":"arxiv-2409.07367","DOIUrl":"https://doi.org/arxiv-2409.07367","url":null,"abstract":"Modern music streaming services are heavily based on recommendation engines\u0000to serve content to users. Sequential recommendation -- continuously providing\u0000new items within a single session in a contextually coherent manner -- has been\u0000an emerging topic in current literature. User feedback -- a positive or\u0000negative response to the item presented -- is used to drive content\u0000recommendations by learning user preferences. We extend this idea to\u0000session-based recommendation to provide context-coherent music recommendations\u0000by modelling negative user feedback, i.e., skips, in the loss function. We\u0000propose a sequence-aware contrastive sub-task to structure item embeddings in\u0000session-based music recommendation, such that true next-positive items\u0000(ignoring skipped items) are structured closer in the session embedding space,\u0000while skipped tracks are structured farther away from all items in the session.\u0000This directly affects item rankings using a K-nearest-neighbors search for\u0000next-item recommendations, while also promoting the rank of the true next item.\u0000Experiments incorporating this task into SoTA methods for sequential item\u0000recommendation show consistent performance gains in terms of next-item hit\u0000rate, item ranking, and skip down-ranking on three music recommendation\u0000datasets, strongly benefiting from the increasing presence of user feedback.","PeriodicalId":501281,"journal":{"name":"arXiv - CS - Information Retrieval","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adversarial Attacks to Multi-Modal Models 多模态模型的对抗性攻击

arXiv - CS - Information Retrieval Pub Date : 2024-09-10 DOI: arxiv-2409.06793

Zhihao Dou, Xin Hu, Haibo Yang, Zhuqing Liu, Minghong Fang

{"title":"Adversarial Attacks to Multi-Modal Models","authors":"Zhihao Dou, Xin Hu, Haibo Yang, Zhuqing Liu, Minghong Fang","doi":"arxiv-2409.06793","DOIUrl":"https://doi.org/arxiv-2409.06793","url":null,"abstract":"Multi-modal models have gained significant attention due to their powerful\u0000capabilities. These models effectively align embeddings across diverse data\u0000modalities, showcasing superior performance in downstream tasks compared to\u0000their unimodal counterparts. Recent study showed that the attacker can\u0000manipulate an image or audio file by altering it in such a way that its\u0000embedding matches that of an attacker-chosen targeted input, thereby deceiving\u0000downstream models. However, this method often underperforms due to inherent\u0000disparities in data from different modalities. In this paper, we introduce\u0000CrossFire, an innovative approach to attack multi-modal models. CrossFire\u0000begins by transforming the targeted input chosen by the attacker into a format\u0000that matches the modality of the original image or audio file. We then\u0000formulate our attack as an optimization problem, aiming to minimize the angular\u0000deviation between the embeddings of the transformed input and the modified\u0000image or audio file. Solving this problem determines the perturbations to be\u0000added to the original media. Our extensive experiments on six real-world\u0000benchmark datasets reveal that CrossFire can significantly manipulate\u0000downstream tasks, surpassing existing attacks. Additionally, we evaluate six\u0000defensive strategies against CrossFire, finding that current defenses are\u0000insufficient to counteract our CrossFire.","PeriodicalId":501281,"journal":{"name":"arXiv - CS - Information Retrieval","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142205323","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0