Traditional recommender systems primarily rely on overall ratings, which may fail to capture the fine-grained nuances of user preferences. Multi-Criteria Recommender Systems (MCRS) address this limitation by incorporating user feedback across multiple aspects of the items to provide more accurate and personalized suggestions. While the integration of advanced deep learning techniques has significantly improved MCRS performance, the research landscape remains fragmented, lacking a unified theoretical framework that connects modern neural network architectures with classical Multi-Criteria Decision-Making (MCDM) theories. To address this gap, this paper presents a conceptually driven narrative review that introduces a novel architectural taxonomy for MCRS. We systematically classify existing methodologies into two paradigms: Multi-Stage architectures and Single-Stage architectures. Multi-Stage MCRS explicitly integrates MCDM principles, such as multi-attribute utility functions or Pareto dominance, by decomposing the recommendation pipeline into multi-criteria rating prediction and preference aggregation. Conversely, Single-Stage MCRS employs holistic, end-to-end modeling paradigms, such as neighborhood-based methods, deep tensor factorization, or multiview graph neural networks, in order to capture complex and non-linear dimensional interactions simultaneously. Moreover, this review examines the transition from relying on explicit criteria elicitation to mining implicit preferences from user-generated reviews, a shift that significantly broadens the real-world applicability of MCRS. We also discuss ongoing challenges in the field, such as data sparsity, integration with contextual situations, and model explainability. By bridging classical decision science with modern deep learning, this review provides a clear roadmap and theoretical insights for future MCRS research.

Financial risk models provide critical quantitative insights, yet translating complex risk data into actionable narratives remains a challenge. While LLMs offer powerful text generation capabilities, they struggle with numerical reasoning and are prone to hallucinations which is a critical flaw in financial compliance environment. This paper proposes TSRMTGen, a framework that transforms risk model data into accurate, coherent narratives. Our approach integrates expert knowledge through “Hint” injection and utilizes a two-step generation process to ensure factual consistency. We demonstrate the effectiveness of TSRMTGen through a rigorous 3-layer human-in-the-loop evaluation, achieving a 0

In this paper, we introduce a unique task, the assessment of the mutual fund parent companies, in our financial company, where the anomaly events associated with parent companies need to be identified and sent to financial experts to access the impact on related mutual funds. We propose a hybrid framework of anomaly detection to combine data-driven detection and experts-engaged tuning to enhance the identification process. Our experiments have demonstrated its effectiveness through the feedback from financial experts, utilizing a tracking record spanning from May 2022 to June 2023.

With the development of recommender systems (RSs), several promising systems have emerged, such as context-aware RS, multi-criteria RS, and group RS. Multi-criteria recommender systems (MCRSs) are designed to provide personalized recommendations by considering user preferences in multiple attributes or criteria simultaneously. Unlike traditional RSs that typically focus on a single rating, these systems help users make more informed decisions by considering their diverse preferences and needs across various dimensions. In this article, we release the OpenTable data set which was crawled from OpenTable.com. The data set can be considered as a benchmark data set for multi-criteria recommendations.

Financial investments are increasingly encouraged to incorporate environmental, social, and governance (ESG) considerations. For instance, in the context of mutual fund investments, once an investor determines a specific risk preference (e.g., conservative, moderate, or aggressive), ESG-optimized portfolios can be recommended to facilitate investment in ESG-positive mutual funds, such as those associated with lower pollution levels and improved ecosystem health. However, promoting ESG integration presents challenges, particularly in ensuring transparency within investment recommendations, which is crucial for fostering investor confidence and adoption. To address this issue, this paper proposes an interactive visualization system that allows users to navigate multi-objective optimized portfolios, compare their strengths and weaknesses, and understand the impact of ESG considerations on portfolio composition. The system aims to enhance user understanding and support informed investment decision-making. The effectiveness of this platform is validated through a user study, wherein financial experts provided positive feedback regarding its ability to improve decision-making processes. Additionally, by leveraging large language models (LLMs), the system offers on-demand explanations of financial terminology through text highlighting and a chat-based interface, further enhancing transparency and user engagement.

A recommender system is a tool designed to suggest relevant items to users based on their preferences and behaviors. Collaborative filtering, a popular technique within recommender systems, predicts user interests by analyzing patterns in interactions and similarities between users, leveraging past behavior data to make personalized recommendations. Despite its popularity, collaborative filtering faces notable challenges, and one of them is the issue of grey-sheep users who have unusual tastes in the system. Surprisingly, existing research has not extensively explored outlier detection techniques to address the grey-sheep problem. To fill this research gap, this study conducts a comprehensive comparison of 12 outlier detection methods (such as LOF, ABOD, HBOS, etc.) and introduces innovative user representations aimed at improving the identification of outliers within recommender systems. More specifically, we proposed and examined three types of user representations: 1) the distribution statistics of user-user similarities, where similarities were calculated based on users’ rating vectors; 2) the distribution statistics of user-user similarities, but with similarities derived from users represented by latent factors; and 3) latent-factor vector representations. Our experiments on the MovieLens and Yahoo!Movie datasets demonstrate that user representations based on latent-factor vectors consistently facilitate the identification of more grey-sheep users when applying outlier detection methods.

Recommender systems (RSs) are designed to help users navigate through large amounts of information by providing personalized suggestions tailored to their preferences. Multi-criteria recommender systems (MCRSs) extend this concept by utilizing users' ratings on multiple aspects of items (i.e., multi-criteria ratings) to predict their overall preferences. Currently, there are several open-source libraries released for RSs. However, none of these existing libraries can handle multi-criteria recommendations due to the special challenges in MCRSs. In this paper, we introduce a Multi-Criteria Recommendation Kit (MCRecKit) which is a Python-based open-source library for multi-criteria recommendations. MCRecKit fills the gap by providing flexible tools and algorithms specifically designed to address the complexity of MCRSs. The library offers a variety of methods for processing, modeling, and evaluating multi-criteria data, enabling researchers and developers to experiment with novel approaches.

Collaborative filtering is a popular recommendation technique predicting user preferences through the analysis of similar users' historical behaviors, offering personalized recommendations based on shared interests. While collaborative filtering algorithms are widely used, they face well-known challenges like rating sparsity, cold-start problems, and the presence of grey-sheep users. The grey-sheep users are the users with uncommon item preferences, and they can be treated as outliers. It is surprising that the outlier detection technologies were not fully examined to identify the grey-sheep users. In this paper, our study addresses this gap by empirically comparing multiple state-of-the-art outlier detection methods and also introducing novel user representations to enhance the outlier detection process in recommender systems.

With the development of recommender systems (RS), several promising systems have emerged, such as context-aware RS, multi-criteria RS, and group RS. However, the education domain may not benefit from these developments due to missing information, such as contexts and multiple criteria, in educational data sets. In this paper, we announce and release an open data set for educational recommender systems. This data set includes not only traditional rating entries, but also enriched information, e.g., contexts, user preferences in multiple criteria, group compositions and preferences, etc. It provides a testbed and enables more opportunities to develop and examine various educational recommender systems.

Recommender systems (RecSys) have found widespread use in a variety of applications, including e-commerce platforms like Amazon.com and eBay, online streaming services such as YouTube, Netflix, and Spotify, and social media sites like Facebook and Twitter. The success of these applications in improving user experience and decision making by providing personalized recommendations highlights the effectiveness of RecSys. Over the past few decades, RecSys has also made its way into the field of education, which results in the development of educational recommender systems (EdRec). Its applications in this field include personalized learning experiences, recommending appropriate formal or informal learning materials, suggesting learning peers, and adapting learning to context-aware or mobile environments, and so forth. Recently, the development of RecSys has been advanced by a series of interesting and promising topics, such as multi-task learning, multi-objective optimization, multi-stakeholder considerations, concerns of fairness, accountability, and transparency, etc. However, this progress made in the field of recommender systems was not adequately disseminated to the education community or the development of EdRec. In this tutorial, we will introduce the background, motivations, knowledge & skills associated with the current development of EdRec, and discuss a set of emerging topics and open challenges, along with case studies in EdRec.

Multi-criteria recommender systems can improve the quality of recommendations by considering user preferences on multiple criteria. One promising approach proposed recently is multi-criteria ranking, which uses Pareto ranking to assign a ranking score based on the dominance relationship between predicted ratings across criteria. However, applying Pareto ranking to all criteria may result in non-differentiable ranking scores. To alleviate this issue, we proposed a hybrid multi-criteria ranking method by using subsorting. More specifically, we utilize one ranking method as the major sorting approach, while we apply another preference ordering method as subsorting. Our experimental results on the OpenTable and Yahoo!Movies data present the advantages of this hybrid ranking approach. In addition, the experiments also reveal more insights about the sustainability of the multi-criteria ranking for top-N item recommendations.

ChatGPT, an implementation and application of large language models, has gained significant popularity since its initial release. Researchers have been exploring ways to harness the practical benefits of ChatGPT in real-world scenarios. Educational researchers have investigated its potential in various subjects, e.g., programming, mathematics, finance, clinical decision support, etc. However, there has been limited attention given to its application in data science education. This paper aims to bridge that gap by utilizing ChatGPT in a data science course, gathering perspectives from students, and presenting our experiences and feedback on using ChatGPT for teaching and learning in data science education. The findings not only distinguish data science education from other disciplines but also uncover new opportunities and challenges associated with incorporating ChatGPT into the data science curriculum.

The influence of personality traits on educational outcomes has been widely recognized and studied. Research has explored its effects on factors such as student satisfaction, academic anxiety, and dishonesty, particularly during the COVID-19 pandemic. However, there has been a lack of studies comparing the learning behaviors and performance of students with different personality traits during the pre, during, and post-COVID-19 lockdown periods. This study fills this gap by analyzing the differences in academic metrics, such as class grades and assignment submissions, among students with varying personality traits during different lockdown periods. Our research, based on a dataset of 282 graduate students in the USA, identified correlations and patterns between lockdown periods, personality traits, and academic metrics. For example, the class grades and the rate of late submissions were affected by different lockdown periods. Students with lower degree in extraversion and agreeableness made less attempts in assignment submissions. These findings can assist educators in identifying impacted students and developing effective teaching strategies at early stage in future incidents.

The process of financial portfolio optimization involves choosing the most suitable mix of assets to meet a particular investment goal. Conventional portfolio optimization primarily focuses on maximizing returns and minimizing risks while overlooking the importance of social responsibility or sustainability in financial investments. In this paper, we present a Python-based multi-objective portfolio optimization library for sustainable investments (MOPO-LSI). MOPO-LSI is able to take Environmental, Social and Governance (ESG) factors into consideration in financial portfolio, where investors’ assets can be well allocated to mutual funds towards the ESG optimization along with their financial goals in the investment. MOPO-LSI is easy to be configured and used, and it is capable of production solutions in two scenarios – when client preferences are known or unknown. The developers can also easily customize the library to adapt it to their own financial objectives.

Multi-criteria decision making (MCDM) is a popular branch of decision making, where the decision makers need to make a choice based on a number of decision criteria. This process is applicable in various domains of our daily life. For example, a person who is booking a hotel may need to take into account several factors such as location, safety, budget, and room cleanliness. Similarly, a consumer who is placing an order online may consider factors such as budget, quality, functionality, and delivery time. Recommender systems have become a popular solution to assist users in decision making, and these systems can also be developed with multiple criteria and user preferences in mind. This tutorial provides a comprehensive review of MCDM schemes and the development of multi-criteria recommender systems (MCRS). It explores various MCDM solutions and discusses how the current development of MCRS can be linked to these MCDM theories and solutions. The tutorial also highlights the gap between MCDM and the practice of MCRS, which could inspire future development of MCRS.

Multi-criteria recommender systems are capable of enhancing recommendation quality by taking into account user preferences across multiple criteria. A promising approach that has recently emerged is multi-criteria ranking, which employs Pareto ranking to determine a ranking score based on the dominance relation of predicted multi-criteria ratings. While this technique can be integrated with existing MCRS models, the issue of dimensionality remains a challenge. To tackle similar problems, other preference ordering methods have been proposed in the field of multi-objective optimization. This study presents a comparative analysis of preference ordering methods for multicriteria ranking, along with insights obtained from experiments conducted on four real-world datasets.

Financial portfolio optimization is a process of selecting the optimal combination of assets to achieve a specific investment objective. Traditional portfolio optimization may only maximize returns and minimize risks, and ignore social responsibility or sustainability in financial investments. In this paper, we release MOPO-LSI which is a multi-objective portfolio optimization library for sustainable investments. More specifically, MOPO-LSI additionally considers Environmental, Social and Governance (ESG) factors as objectives to be optimized in financial portfolio, where investors' assets can be well allocated to mutual funds towards the improvements in sustainable development and practices. No matter client preferences on ESG factors are unknown or not, MOPO-LSI provides solutions to portfolio optimization in these scenarios. Moreover, MOPO-LSI is easy to be configured and used, and users can also extend its definition of multi-objective problems and adapt the solutions to customized requirements or applications.

Multi-criteria recommender systems can improve the quality of recommendations by considering user preferences on multiple criteria. One promising approach proposed recently is multi-criteria ranking, which uses Pareto ranking to assign a ranking score based on the dominance relationship between predicted ratings across criteria. However, applying Pareto ranking to all criteria may result in non-differentiable ranking scores. To alleviate this issue, we conducted a study on three relaxed Pareto ranking methods for multi-criteria ranking. We evaluated these methods on three real-world datasets and found that the k-dominance ranking approach, which is one of the relaxed Pareto ranking methods, was able to further enhance the ranking performance.