Recommender Systems Research Articles

SNOOKER: a dataset generator for helpdesk services

AbstractThe availability of suitable datasets and data generators is crucial for developing intelligent systems, especially in helpdesk services. However, the lack of publicly accessible data generators focused on helpdesk operations, where incidents are often closed without detailing the treatment procedures, poses challenges to implementing intelligent systems such as recommender systems. To address this issue, a dataset generator can be employed to simulate helpdesk incidents. This paper introduces SNOOKER (dataSet geNeratOr fOr helpdesK sERvices), a customizable dataset generator designed to create and treat helpdesk tickets, including domain-specific incidents (e.g., cybersecurity) by orchestrating simulated actions and multiple IT teams. SNOOKER’s output is compared against a real anonymized dataset from S21Sec Cyber Solutions by Thales. The datasets are evaluated using Kolmogorov-Smirnov, Kullback-Leibler Divergence, and Hellinger distance tests, with results indicating similar distributions. For example, the first metric returned a low K-S value and a p-value exceeding 5%, while the second and third measures presented 0.003 and 0.03, respectively. Furthermore, experiments with different team configurations revealed that ticket scheduling highly depends on each team’s operators’ numbers and work shifts, increasing with unbalanced shifts and fewer operators.

Preface to the special issue on conversational recommender systems: theory, models, evaluations, and trends

Preface to the special issue on conversational recommender systems: theory, models, evaluations, and trends

Investigating Characteristics of Media Recommendation Solicitation in r/ifyoulikeblank

Despite the existence of search-based recommender systems like Google, Netflix, and Spotify, online users sometimes may turn to crowdsourced recommendations in places like the r/ifyoulikeblank subreddit. In this exploratory study, we probe why users go to r/ifyoulikeblank, how they look for recommendation, and how the subreddit users respond to recommendation requests. To answer, we collected sample posts from r/ifyoulikeblank and analyzed them using a qualitative approach. Our analysis reveals that users come to this subreddit for various reasons, such as exhausting popular search systems, not knowing what or how to search for an item, and thinking crowd have better knowledge than search systems. Examining users query and their description, we found novel information users provide during recommendation seeking using r/ifyoulikeblank. For example, sometimes they ask for artifacts recommendation based on the tools used to create them. Or, sometimes indicating a recommendation seeker's time constraints can help better suit recommendations to their needs. Finally, recommendation responses and interactions revealed patterns of how requesters and responders refine queries and recommendations. Our work informs future intelligent recommender systems design.

Determining the Effect of Feedback Quality on User Engagement on Online Idea Crowdsourcing Platforms Using an AI model

The success of idea crowdsourcing platforms relies on fostering a collaborative environment that encourages active user participation, measured by the quality and quantity of contributions and interactions. However, understanding the impact of peer interactions on user engagement and the innovation process remains challenging. While previous studies have focused on sentiment analysis, the contextual interpretation of peer feedback and its effects on engagement and innovation have yet to be explored. To address this knowledge gap, we propose a feature-based AI model that categorizes peer feedback based on its quality and polarity. Our model achieves 96% accuracy in identifying feedback quality (constructive vs. non-constructive and toxic vs. non-toxic) and feedback polarity (negative vs. positive). This contextual feedback categorization provides a foundation for quantitatively analyzing the impacts of feedback on user engagement and the innovation process. Our results show that positive and constructive peer feedback has a significantly positive effect, while toxic and negative peer feedback has a significantly negative impact on the process of idea generation, idea evaluation, and idea selection. Our results also indicate that inexperienced users are more susceptible to toxic feedback than experienced users. Based on these findings, we suggest a recommendation and reward system for incentivizing constructive feedback and preventing toxic feedback.

Artificial Intelligence-Powered Recommender Systems for Promoting Healthy Habits and Active Aging: A Systematic Review

Artificial Intelligence-Powered Recommender Systems for Promoting Healthy Habits and Active Aging: A Systematic Review

Deep Learning-Based Freight Recommendation System for Freight Brokerage Platform

Platform-based businesses in the logistics market are evolving under the influence of digital transformation. Transforming the freight market into an environment where various types of freight can be traded across multiple markets and locations. Freight brokerage platforms have revolutionized the trading relationship between freight owners and vehicle owners. However, this type of system has also introduced inefficiencies, such as unestablished contracts, leading to unnecessary costs and delays. To address this issue, a freight recommendation system can assist users in finding what they are looking for while aiming to reduce failed contracts. With current advances in deep learning, complex patterns based on users’ past behaviors and preferences can be learned, enabling more accurate and personalized recommendations. This study proposes a deep learning-based freight recommendation system to provide personalized services and reduce failed contracts on freight brokerage platforms. The system is built by creating a freight transaction dataset, classifying freight categories through natural language processing and text mining techniques, and incorporating externally derived data on transportation distances. The deep learning model is trained using Autoencoder, Word2Vec, and Graph Neural Networks (GNN), with recommendation logic implemented to suggest suitable freight matches for vehicle owners. This system is expected to increase the market efficiency of the freight logistics industry and is a key step toward improving the long-term profit structure.

A Hybrid Intelligent Tourism Recommendation System Using Big Data and AI for Enhanced User-Centric Suggestions

A Hybrid Intelligent Tourism Recommendation System Using Big Data and AI for Enhanced User-Centric Suggestions

Post-Training Attribute Unlearning in Recommender Systems

With the growing privacy concerns in recommender systems, recommendation unlearning is getting increasing attention. Existing studies predominantly use training data, i.e., model inputs, as unlearning target. However, attackers can extract private information from the model even if it has not been explicitly encountered during training. We name this unseen information as attribute and treat it as unlearning target. To protect the sensitive attribute of users, Attribute Unlearning (AU) aims to make target attributes indistinguishable. In this paper, we focus on a strict but practical setting of AU, namely Post-Training Attribute Unlearning (PoT-AU), where unlearning can only be performed after the training of the recommendation model is completed. To address the PoT-AU problem in recommender systems, we propose a two-component loss function. The first component is distinguishability loss, where we design a distribution-based measurement to make attribute labels indistinguishable from attackers. We further extend this measurement to handle multi-class attribute cases with efficient computational overhead. The second component is regularization loss, where we explore a function-space measurement that effectively maintains recommendation performance compared to parameter-space regularization. We use stochastic gradient descent algorithm to optimize our proposed loss. Extensive experiments on four real-world datasets demonstrate the effectiveness of our proposed methods.

UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation

To tackle the challenges of cold start and data sparsity in recommendation systems, an increasing number of researchers are integrating item features, resulting in the emergence of multimodal recommendation systems. Although graph convolutional network-based approaches have achieved significant success, they still face two limitations: (1) Users have different preferences for various types of features, but existing methods often treat these preferences equally or fail to specifically address this issue. (2) They do not effectively distinguish the similarity between different modality item features, overlook the unique characteristics of each type, and fail to fully exploit their complementarity. To solve these issues, we propose the user perception-guided graph convolutional network for multimodal recommendation (UPGCN). This model consists of two main parts: the user perception-guided representation enhancement module (UPEM) and the multimodal two-step enhanced fusion method, which are designed to capture user preferences for different modalities to enhance user representation. At the same time, by distinguishing the similarity between different modalities, the model filters out noise and fully leverages their complementarity to achieve more accurate item representations. We performed comprehensive experiments on the proposed model, and the results indicate that it outperforms other baseline models in recommendation performance, strongly demonstrating its effectiveness.

Fostering AI literacy for future librarians

This study assesses the artificial intelligence (AI) literacy and perceptions among Master of Library Science (MLS) students in India. It aims to identify the most relevant AI knowledge areas for library science, examine students’ attitudes toward AI’s potential impact on library services, and explores the importance of AI literacy for professional practice. A quantitative survey administered to 118 final-year MLS students via WhatsApp groups and personal networks collected data on AI literacy levels, perceptions, and attitudes. The findings reveal that natural language processing (NLP) is considered the most transformative AI technology, with significant interest in other areas like recommender systems and data mining. Students express optimism about AI’s potential to enhance library services but raise concerns about ethical implications and job displacement. The study highlights the need for increased AI literacy training and integration into LIS programs, emphasizing its importance for future career competitiveness. The results provide valuable insights into how AI literacy can be fostered among MLS students to align with evolving library services and user needs.

Optimal design of neural network-based fuzzy predictive control model for recommending educational resources in the context of information technology

Abstract As the information technology develops, educational resource recommendation system has become an indispensable part of the education field. At the same time, people’s demand for personalised educational resources is also growing; therefore, how to accurately predict user demand and provide personalised recommendations has become an important issue. In this study, a fuzzy predictive control model on the grounds of neural network is proposed to optimise the design of educational resource recommendation in the context of information technology. After experimental testing, the model recommendation fit reached 95.16 and 92.91% on the two test datasets, which are significantly higher than the control model. The average F1 values of the proposed model also reached 95.21 and 88.77%, which are higher than the control model. In other control experiments, the proposed model of this study also has a better performance. The relevant outcomes showcase that the predictive performance and recommendation effect of the model can be further enhanced by improving the structure of the neural network and the parameter optimisation method. Meanwhile, the proposed model has high performance in information overload and personalised demand, which offers a useful reference for the optimal design of educational resource recommendation system.

AI Enabled E-commerce Platform

An overview of AI-based e-commerce plat forms and their effects on the online retail sector is given in this survey. Businesses are starting to use artificial intelligence (AI) to improve customer experience, streamline processes, and boost revenue development in the e-commerce industry as a result of the technology’s rapid advancement. This research offers a thorough examination of the state of AI-based e-commerce platforms today, stressing their salient characteristics, advantages, difficulties, and potential. The first part of the poll introduced artificial intelligence (AI) and how it relates to e-commerce. It looks at how artificial intelligence (AI) technologies like computer vision, machine learning, natural language processing, and recommendation systems are being incorporated into e-commerce platforms to provide intelligent customer support, enhance product search and discovery, and offer personalized shopping experiences. Additionally, it explores the role of AI in enhancing customer engagement through chatbots, virtual assistants, and personalized marketing campaigns

Characterizing and Understanding HGNN Training on GPUs

Owing to their remarkable representation capabilities for heterogeneous graph data, Heterogeneous Graph Neural Networks (HGNNs) have been widely adopted in many critical real-world domains such as recommendation systems and medical analysis. Prior to their practical application, identifying the optimal HGNN model parameters tailored to specific tasks through extensive training is a time-consuming and costly process. To enhance the efficiency of HGNN training, it is essential to characterize and analyze the execution semantics and patterns within the training process to identify performance bottlenecks. In this study, we conduct a comprehensive quantification and in-depth analysis of two mainstream HGNN training scenarios, including single-GPU and multi-GPU distributed training. Based on the characterization results, we reveal the performance bottlenecks and their underlying causes in different HGNN training scenarios and propose optimization guidelines from both software and hardware perspectives.

AdaGIN: Adaptive Graph Interaction Network for Click-Through Rate Prediction

The goal of click-through rate (CTR) prediction in recommender systems is to effectively work with input features. However, existing CTR prediction models face three main issues. First, many models use a basic approach for feature combinations, leading to noise and reduced accuracy. Second, there is no consideration for the varying importance of features in different interaction orders, affecting model performance. Third, current model architectures struggle to capture different interaction signals from various semantic spaces, leading to sub-optimal performance. To address these issues, we propose the Adaptive Graph Interaction Network (AdaGIN) with the Graph Neural Networks-based Feature Interaction Module (GFIM), the Multi-semantic Feature Interaction Module (MFIM), and the Negative Feedback-based Search (NFS) algorithm. GFIM explicitly aggregates information between features and assesses their importance, while MFIM captures information from different semantic spaces. NFS uses negative feedback to optimize model complexity. Experimental results show AdaGIN outperforms existing models on large-scale public benchmark datasets.

Natural Language Processing and Machine Learning-Based Solution of Cold Start Problem Using Collaborative Filtering Approach

In today’s digital era, the abundance of online services presents users with a daunting array of choices, spanning from streaming platforms to e-commerce websites, leading to decision fatigue. Recommendation algorithms play a pivotal role in aiding users in navigating this plethora of options, among which collaborative filtering (CF) stands out as a prevalent technique. However, CF encounters several challenges, including scalability issues, privacy implications, and the well-known cold start problem. This study endeavors to mitigate the cold start problem by harnessing the capabilities of natural language processing (NLP) applied to user-generated reviews. A unique methodology is introduced, integrating both supervised and unsupervised NLP approaches facilitated by sci-kit learn, utilizing benchmark datasets across diverse domains. This study offers scientific contributions through its novel approach, ensuring rigor, precision, scalability, and real-world relevance. It tackles the cold start problem in recommendation systems by combining natural language processing (NLP) with machine learning and collaborative filtering techniques, addressing data sparsity effectively. This study emphasizes reproducibility and accuracy while proposing an advanced solution that improves personalization in recommendation models. The proposed NLP-based strategy enhances the quality of user-generated content, consequently refining the accuracy of Collaborative Filtering-Based Recommender Systems (CFBRSs). The authors conducted experiments to test the performance of the proposed approach on benchmark datasets like MovieLens, Jester, Book-Crossing, Last.fm, Amazon Product Reviews, Yelp, Netflix Prize, Goodreads, IMDb (Internet movie Database) Data, CiteULike, Epinions, and Etsy to measure global accuracy, global loss, F-1 Score, and AUC (area under curve) values. Assessment through various techniques such as random forest, Naïve Bayes, and Logistic Regression on heterogeneous benchmark datasets indicates that random forest is the most effective method, achieving an accuracy rate exceeding 90%. Further, the proposed approach received a global accuracy above 95%, a global loss of 1.50%, an F-1 Score of 0.78, and an AUC value of 92%. Furthermore, the experiments conducted on distributed and global differential privacy (GDP) further optimize the system’s efficacy.

Survey on Multimodal Emotion Detection

As we know, many people suffer from unhappiness in today’s world, so there is a requirement for a system that recommends music based on human emotions such as anger, sadness, happiness, etc. In this paper, we propose a system named Multimodal Emotion Detection which helps people by suggesting music and movies based on their emotions. We implemented this system using the FER-2013 dataset, which contains 35,887 images of different emotions such as sadness, happiness, and anger. We used Python’s TensorFlow framework and Haar Cascade algorithms to recommend movies and music based on human facial emotions. The main concern in existing recommendation systems is manual sorting. To avoid this, we propose this model, which automatically plays music and movies without requiring much browsing time

Research on Personalized Cultural Learning Platform Based on Collaborative Filtering and Popularity Recommendation

With the rapid progress of information technology, it has become the demand of the times to use the power of the Internet to inherit and develop Chinese traditional culture. Given the challenge of users searching for learning content in massive online resources, personalized recommendation systems have become the key to improving user experience. This paper focuses on building a personalized Chinese traditional culture learning platform based on collaborative filtering and popularity recommendation, aiming to stimulate users’ interest in learning traditional culture through intelligent algorithms. In terms of technical implementation, this study relies on the Django framework and MySQL database to build a feature-rich and user-friendly traditional cultural learning platform, supporting users’ diverse learning needs, and providing flexible learning paths and abundant learning resources. Experimental evaluation shows that the hybrid algorithm of collaborative filtering and popularity recommendation proposed in this paper has improved recommendation performance compared to existing methods, effectively enhancing user satisfaction and platform activity. This platform not only promotes the digital dissemination of traditional culture, but also provides users with a highly autonomous and personalized learning experience, meeting the contemporary society’s pursuit of diversified and personalized cultural learning.

Cloud Platform Business Scenario Modeling Based on Multi-Model Fusion Recommendation Algorithm

In the era of cloud computing, businesses are increasingly relying on cloud platforms to streamline their operations and deliver services efficiently. Recommendation systems play a pivotal role in suggesting suitable services and resources to enhance user experience and optimize resource allocation. This paper presents a novel approach, the Multi-Model Fusion Recommendation Algorithm (MMFRA), which integrates multiple recommendation models using advanced fusion techniques to enhance the accuracy of recommendations in cloud platform business scenarios. The implementation process of MMFRA involves combining diverse recommendation models, such as collaborative filtering, content-based filtering, and matrix factorization, into a unified framework that leverages their strengths while mitigating individual limitations. This fusion process is designed to achieve higher precision in service recommendations by considering various aspects of user behavior and preferences. Through a comprehensive evaluation in a simulated cloud platform environment, MMFRA demonstrates superior performance in terms of recommendation accuracy and user satisfaction. The proposed algorithm offers significant potential for enhancing the effectiveness of cloud platform services, ultimately benefiting both service providers and users.

AI AND MACHINE LEARNING IN BUSINESS PROCESS AUTOMATION: INNOVATING WAYS AI CAN ENHANCE OPERATIONAL EFFICIENCIES OR CUSTOMER EXPERIENCES IN U.S. ENTERPRISES

This study presents a comprehensive review of the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing business processes across various industries. By examining a total of 75 peer-reviewed articles, the review highlights key areas where AI and ML have demonstrated significant impact, including operational efficiency, customer engagement, and strategic decision-making. Findings indicate that AI-driven process optimizations, particularly through predictive maintenance and resource management, have led to substantial cost savings and improved productivity by minimizing downtime and enhancing workflow efficiencies. Additionally, AI’s ability to support personalized customer experiences—through recommendation systems, dynamic pricing, and chatbots—has proven instrumental in driving customer satisfaction, retention, and engagement, making it a critical tool in customer relationship management. Furthermore, the strategic adoption of AI and ML has enabled data-driven decision-making, allowing companies to respond more effectively to market changes and forecast business outcomes with greater accuracy. The study also explores the transition from Robotic Process Automation (RPA) to AI as a foundational step, illustrating how RPA provides a structured entry point for advanced AI applications, creating an automation-ready environment. However, challenges such as technical limitations, ethical concerns, and organizational resistance persist, underscoring the need for careful implementation strategies. Addressing these challenges will be vital for organizations aiming to maximize the benefits of AI-driven automation in an increasingly competitive digital landscape. This review underscores the significant potential of AI and ML in reshaping business models and emphasizes the importance of ongoing research and development in these fields to support sustainable and scalable innovations.

Advancing legal recommendation system with enhanced Bayesian network machine learning

Advancing legal recommendation system with enhanced Bayesian network machine learning