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Recommender Systems in E-Commerce: A Deep Dive into AI-Driven Optimization 1

Recommender Systems in E-Commerce: A Deep Dive into AI-Drive...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Mishra, Manas Kumar Ramavath, Shiva Kumar Gupta, Gopal Kumar Desai, Piyush Bipinkumar Somavarapu, Sushira Katyayan, Shashank Shekhar G.L. Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida201306 India University of North Texas DentonTX United States Nagpur Campus Pune440008 India Tamil Nadu Vellore632014 India Louisiana State University Baton RougeLA70802 United States Eli Broad College of Business Michigan State University East LansingMI United States

ISBN: (纸本)9798331518578

In e-commerce, recommender systems are the most widely used systems that satisfy the user with personalized recommendations, and they have a significant influence on e-business success. In the new era of artificial intelligence (AI) and deep learning techniques, modern recommender systems have evolved from the classical models of content-based filtration systems to highly sophisticated and accurate models. In this post, we take a closer look at specific optimization techniques that are applied in the context of AI-based recommender systems in the e-commerce industry. We start with the data needed to train these systems and the different ways to collect and clean the data. Next, we delve into getting recommendations and the methods behind them - collaborative filtering (CF), content-based filtering (CB), etc. Next, we explain how deep learning approaches such as natural language processing and neural networks improve the accuracy of these systems. We look at what sort of problems you face while implementing these systems (cold-start issue, data sparsity issues et al) and how are you working to address them. We also consider ethical concerns regarding recommender systems: bias and privacy. Finally, we summarize the various use cases of AI-based recommender systems in e-commerce, including product recommendations, personalized promotions, and user profiling. This paper intends to present a general survey of the techniques which are being state-of-the-art in e-commerce AI recommender systems and a brief discussion of how they can massively boost user satisfaction in unison with business goals. © 2025 ieee.

关键词： Marketplaces

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Requirements Satisfiability with In-Context Learning

Requirements Satisfiability with In-Context Learning

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ieee international conference on Requirements engineering

作者： Sarah Santos Travis Breaux Thomas Norton Sara Haghighi Sepideh Ghanavati School of Computer Science Carnegie Mellon University Pittsburgh Pennsylvannia United States School of Law Fordham University New York New York United States School of Computing and Information University of Maine Orono Maine United States

ISBN: (数字)9798350395112

ISBN: (纸本)9798350395129

language models that can learn a task at inference time, called in-context learning (ICL), show increasing promise in natural language inference tasks. In ICL, a model user constructs a prompt to describe a task with a natural language instruction and zero or more examples, called demonstrations. The prompt is then input to the language model to generate a completion. In this paper, we apply ICL to the design and evaluation of satisfaction arguments, which describe how a requirement is satisfied by a system specification and associated domain knowledge. The approach builds on three prompt design patterns, including augmented generation, prompt tuning, and chain-of-thought prompting, and is evaluated on a privacy problem to check whether a mobile app scenario and associated design description satisfies eight consent requirements from the EU General Data Protection Regulation (GDPR). The overall results show that GPT-4 can be used to verify requirements satisfaction with 96.7% accuracy and dissatisfaction with 93.2% accuracy. Inverting the requirement improves verification of dissatisfaction to 97.2%. Chain-of-thought prompting improves overall GPT-3.5 performance by 9.0% accuracy. We discuss the trade-offs among templates, models and prompt strategies and provide a detailed analysis of the generated specifications to inform how the approach can be applied in practice.

关键词： Data privacy Analytical models Accuracy natural languages Mobile applications Requirements engineering Task analysis

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Revolutionizing Wireless Modeling and Simulation with Network-Oriented LLMs

Revolutionizing Wireless Modeling and Simulation with Networ...

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ieee international conference on Performance, Computing and Communications (IPCCC)

作者： Jiewen Liu Zhiyuan Peng Dongkuan Xu Yuchen Liu North Carolina State University USA

ISBN: (数字)9798350367942

ISBN: (纸本)9798350367959

The complexity of modern network infrastructure continues to grow, supporting a wide range of interconnected applications. Simulators are indispensable tools in this context, providing cost-effective and risk-free environments for experimentation and development. However, mastering these network simulators demands substantial domain-specific knowledge, even with comprehensive user manuals. Motivated by the capabilities of Large language Models (LLMs), this paper introduces the network-oriented LLM as an intermediary between users and network simulators, aiming to offer an interactive, automated, and script-free simulation paradigm. Using the emerging Sionna simulator as a case study, we adapt the general-purpose LLM into a network-oriented LLM through joint parameter-efficient fine-tuning and retrieval-augmented generation, which then streamlines the complex simulation process through simple natural language queries. Comprehensive experiments with state-of-the-art LLMs demonstrate that the proposed method can effectively adapt LLMs for use with network simulators, significantly enhancing user-level operational efficiency and accessibility. The proposed pipeline can facilitate the broader development of various network-oriented LLMs, potentially automating a range of complex network tasks.

关键词： knowledge engineering Wireless communication Codes Large language models Retrieval augmented generation Pipelines natural languages Manuals Complex networks Complexity theory

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Compensatory Debiasing For Gender Imbalances In language Models

Compensatory Debiasing For Gender Imbalances In Language Mod...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Tae-Jin Woo Woo-Jeoung Nam Yeong-Joon Ju Seong-Whan Lee Department of Artificial Intelligence Korea University South Korea School of Computer Science and Engineering Kyungpook National University South Korea

Pre-trained language models (PLMs) learn gender bias from imbalances in human-written corpora. This bias leads to critical social issues when deploying PLMs in real-world scenarios. However, minimizing bias is limited by the trade-off due to the degradation of language modeling performance. It is particularly challenging to detach and remove biased representations in the embedding space because the learned linguistic knowledge entails bias. To address this problem, we propose a compensatory debiasing strategy to reduce gender bias while preserving linguistic knowledge. This strategy utilizes two types of sentences to distinguish biased knowledge: stereotype and non-stereotype sentences. We assign small angles and distances to pairs of representations of the two gender groups to mitigate bias for the stereotype sentences. At the same time, we maximize the agreement for the representations of the debiasing model and the original model to maintain linguistic knowledge for the non-stereotype sentences. To validate our approach, we measure the performance of the debiased model using the following evaluation metrics: SEAT, StereoSet, CrowS-Pairs, and GLUE. Our experimental results demonstrate that the model fine-tuned by our strategy has the lowest level of bias while retaining knowledge of PLMs.

关键词： Measurement Degradation Linguistics Signal processing Acoustics Speech processing

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Multilingual Approaches to Named Entity Recognition

Multilingual Approaches to Named Entity Recognition

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Delhi Section conference (DELCON), ieee

作者： Padmaja Savaram Suhana Tabassum Sasidhar Bandu Lakshitha B Dept. of Computer Science College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University Al-kharj KSA Dept. of Computer Science and Engineering University College of Engineering Osmania University Hyderabad India Dept. of English Prince Sattam Bin Abdulaziz University Al-kharj KSA Dept. of Computer Science and Engineering VNRVJIET JNTU Hyderabad India

ISBN: (数字)9798331518592

ISBN: (纸本)9798331518608

Named Entity Recognition (NER) represents a fundamental operation within natural language processing (NLP), focused on the extraction and classification of specific entities embedded in textual data. Given the rising linguistic diversity present in digital communications, there is an escalating need for NER systems to be proficient in identifying and categorizing entities across a spectrum of languages. However, developing NER models for resource-poor languages presents significant challenges due to limited labeled data and linguistic resources. This paper examines methodologies for enhancing the ability of NLP models to perform NER across diverse languages by transferring knowledge from high-resource languages to low-resource languages. We delve into advanced approaches such as cross-lingual transfer learning, multilingual embeddings, and cross-lingual model adaptation. Cross-lingual transfer learning utilizes pre-trained models from high-resource languages to initialize NER systems for low-resource languages, thereby facilitating the effective transfer of linguistic knowledge across language boundaries. Multilingual embeddings provide a shared representation space for words across languages, facilitating the transfer of linguistic knowledge. Additionally, cross-lingual model adaptation techniques aim to adapt existing NER models to new languages through fine-tuning or domain adaptation. By enhancing the generalizability of NER models through cross-lingual knowledge transfer, we enable these models to perform effectively across diverse linguistic contexts, including both resource-rich and resource-poor languages. These advancements contribute to broader accessibility and applicability of NER technology across languages and cultures, facilitating more inclusive and comprehensive language processing applications.

关键词： Adaptation models Error analysis Transfer learning Collaboration Training data Named entity recognition Data models Multilingual Trajectory Context modeling

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Exploring Object-Centered External knowledge for Fine-Grained Video Paragraph Captioning

Exploring Object-Centered External Knowledge for Fine-Graine...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Guorui Yu Yimin Hu Yiqian Xu Yuejie Zhang Rui Feng Tao Zhang Shang Gao School of Computer Science Shanghai Key Lab of Intelligent Information Processing Fudan University School of Information Management and Engineering Shanghai University of Finance and Economics School of Information Technology Deakin University

Video paragraph captioning task aims to generate a detailed, fluent and relevant paragraph for a given video. Prior studies often focus on isolating visual objects (potential main components in a sentence) from the overall video content. They rarely explore the latent semantic relations between objects and high-level video concepts, resulting in dull or even incorrect descriptions. To create fine-grained and contextually relevant paragraph captions, we propose a novel framework that constructs a concept graph from a commonsense knowledge base and infers richer semantic meaning from the visual objects. Moreover, we employ a Vision-Guided Concept Selection Network that incorporates an under-sentence supervision mechanism to align the external knowledge with the visual information. Through extensive experiments on ActivityNet captions and YouCook2, the effectiveness of our method is demonstrated compared to state-of-the-art methods.

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Implicit Business Competitor Inference Using Heterogeneous knowledge Graph 12

Implicit Business Competitor Inference Using Heterogeneous K...

引用

12th ieee international conference on Big knowledge, ICBK 2021

作者： Qin, Wei Luo, Xiangfeng Wang, Hao Shanghai University School of Computer Engineering and Science Shanghai China

ISBN: (纸本)9781665438582

Competitor inference is the task of identifying current or potential competitors given their primary markets and Business Scope. Previous methods have achieved remarkable success on explicit competitor inference using state-of-the-art natural language processing (NLP) techniques, mainly relying on comparative expressions. However, those methods lack interpretability and cannot identify implicit competitors without the explicit mentions of competitive relationships in the text. To remedy these problems, in this paper, we propose a probabilistic graphical model which leverages heterogeneous enterprise knowledge graph containing both structured information, e.g., Product Analysis, Sales Territory, and unstructured information, e.g., Business Scope. The model is defined with first-order logic rules using the declarative language of Probabilistic Soft Logic (PSL). As a result, our model enables predicting implicit competitors while provides pieces of interpretable evidence. Experimental results show that our approach is significantly superior to previous methods. © 2021 ieee.

关键词： knowledge graph

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Software Service engineering in the Era of Large language Models

Software Service Engineering in the Era of Large Language Mo...

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Software Services engineering (SSE) ieee international conference on

作者： Xin Xia Zhi Jin Marco Aiello Dongmei Zhang Guangtai Liang Xing Hu Huawei Technologies Peking University University of Stuttgart Microsoft Huawei Cloud Zhejiang University

ISBN: (数字)9798350368512

ISBN: (纸本)9798350368529

Large language Models (LLMs) such as GPT-4, trained on massive amounts of natural language and source code data, have exhibited remarkable proficiency in automating many aspects of software development and maintenance. As a result, these models have been extensively applied to various Software Service engineering (SSE) tasks, including software requirement analysis, software coding, software testing, and Artificial Intelligence for IT Operations (AIOps). Despite their widespread adoption, numerous challenges persist in fully utilizing LLMs for SSE, such as the need for integrating domain-specific knowledge to generate project-level code or patches effectively. Furthermore, there remains a lack of clarity on how traditional SSE practices can adapt to support the full lifecycle of LLMs, from the initial training and fine-tuning with domain-specific data to the ongoing inference, application, and maintenance (i.e., LLMOps). Effective LLMOps require new methodologies and tools to manage the unique demands of LLMs, including data handling, model updates, performance monitoring, and scalability. These challenges underscore the need for innovative approaches to manage the integration of LLM capabilities within established SSE frameworks.

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An Optimization of Conversational System Based Conceptual natural language Understanding 4

An Optimization of Conversational System Based Conceptual Na...

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4th international conference on Emerging Research in Electronics, Computer Science and Technology, ICERECT 2022

作者： Sudhamsu, Gadug Kumar, Anil Jain University Department of Computer Science and Engineering Bangalore India Teerthanker Mahaveer University Department of Computing Science and IT Moradabad India

ISBN: (纸本)9781665456357

A method for dialogue comprehension based on rules-based in-depth parsing and semantic analysis is presented in the abstract of this paper. A semantic evaluation carried out as part of the EVALDA/MEDIA initiative yielded positive findings. We were able to achieve a score comparable to that of statistical systems without the use of a manually annotated corpus, despite the fact that the conditions of the evaluation made it difficult to use a symbolic processing-based method. Our methodology, which depends on a LTAG parser and two ontologies, gives very strong discourse understanding and offices for communicating with an exchange supervisor and the actual application, and we accept it tends to be utilized in genuine discourse frameworks, despite the fact that the engineering has been intended for this mission, which is solely committed to spoken discourse understanding. © 2022 ieee.

关键词： Semantics

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Multi-Topic Categorization in a Low-Resource Ewe language: A Modern Transformer Approach 7

Multi-Topic Categorization in a Low-Resource Ewe Language: A...

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7th international conference on Computer and Communication Systems, ICCCS 2022

作者： Agbesi, Victor Kwaku Wenyu, Chen Kuadey, Noble Arden Maale, Gerald Tietaa University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China

ISBN: (数字)9781665450607

ISBN: (纸本)9781665450607

The evolution of natural language processing (NLP) recently, paved the way for text categorization. With this mechanism, allocating a large volume of textual data to a category is much easier. This task is more challenging in dealing with multi-topic categorizations in a low-resource language. Transformer-based mechanisms have shown much strength in NLP tasks. However, low-resourced, low-data settings and a lack of benchmark datasets make it difficult to perform any NLP-related task in these extremely low-resource languages with data-points and dataset constraints. In this work, the authors focus on creating a new benchmark dataset for a low-resourced language and performed a multi-topic categorization using this dataset. We further propose an EweBERT model, which is built on the pre-trained transformer model known as Bidirectional Encoder Representations from Transformers (BERT) for multi topic categorization. The EweBERT is used to tokenize and represent the input articles as the initial stage in this system. The output of the EweBERT is then sent into a densely connected neural network, which classifies the articles according to six (6) diverse predefined topics. Experimental results prove that our proposed EweBERT-model records 86.2% accuracy, 85.6% F1-score micro, 85.4% F1-score macro, and F1-score mass of 85.7% compared with 3 benchmarked models. © 2022 ieee.

关键词： Text processing

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