User-item fairness tradeoffs in recommendations

In the basic recommendation paradigm, the most (predicted) relevant item is recommended to each user. This may result in some items receiving lower exposure than they "should"; to counter this, several algorithmic approaches have been developed to ensure item fairness. These approaches necessarily degrade recommendations for some users to improve outcomes for items, leading to user fairness concerns. In turn, a recent line of work has focused on developing algorithms for multi-sided fairness, to jointly optimize user fairness, item fairness, and overall recommendation quality. This induces the question: what is the tradeoff between these objectives, and what are the characteristics of (multi-objective) optimal solutions? Theoretically, we develop a model of recommendations with user and item fairness objectives and characterize the solutions of fairness-constrained optimization. We identify two phenomena: (a) when user preferences are diverse, there is "free" item and user fairness; and (b) users whose preferences are misestimated can be especially disadvantaged by item fairness constraints. Empirically, we prototype a recommendation system for preprints on arXiv and implement our framework, measuring the phenomena in practice and showing how these phenomena inform the design of markets with recommendation systems-intermediated matching.

Graph-Sequential Alignment and Uniformity: Toward Enhanced Recommendation Systems

Graph-based and sequential methods are two popular recommendation paradigms, each excelling in its domain but lacking the ability to leverage signals from the other. To address this, we propose a novel method that integrates both approaches for enhanced performance. Our framework uses Graph Neural Network (GNN)-based and sequential recommenders as separate submodules while sharing a unified embedding space optimized jointly. To enable positive knowledge transfer, we design a loss function that enforces alignment and uniformity both within and across submodules. Experiments on three real-world datasets demonstrate that the proposed method significantly outperforms using either approach alone and achieves state-of-the-art results. Our implementations are publicly available at https://github.com/YuweiCao-UIC/GSAU.git.

AL-QASIDA: Analyzing LLM Quality and Accuracy Systematically in Dialectal Arabic

Dialectal Arabic (DA) varieties are under-served by language technologies, particularly large language models (LLMs). This trend threatens to exacerbate existing social inequalities and limits language modeling applications, yet the research community lacks operationalized LLM performance measurements in DA. We present a method that comprehensively evaluates LLM fidelity, understanding, quality, and diglossia in modeling DA. We evaluate nine LLMs in eight DA varieties across these four dimensions and provide best practice recommendations. Our evaluation suggests that LLMs do not produce DA as well as they understand it, but does not suggest deterioration in quality when they do. Further analysis suggests that current post-training can degrade DA capabilities, that few-shot examples can overcome this and other LLM deficiencies, and that otherwise no measurable features of input text correlate well with LLM DA performance.

Pre-train, Align, and Disentangle: Empowering Sequential Recommendation with Large Language Models

Sequential recommendation (SR) aims to model the sequential dependencies in users' historical interactions to better capture their evolving interests. However, existing SR approaches primarily rely on collaborative data, which leads to limitations such as the cold-start problem and sub-optimal performance. Meanwhile, despite the success of large language models (LLMs), their application in industrial recommender systems is hindered by high inference latency, inability to capture all distribution statistics, and catastrophic forgetting. To this end, we propose a novel Pre-train, Align, and Disentangle (PAD) paradigm to empower recommendation models with LLMs. Specifically, we first pre-train both the SR and LLM models to get collaborative and textual embeddings. Next, a characteristic recommendation-anchored alignment loss is proposed using multi-kernel maximum mean discrepancy with Gaussian kernels. Finally, a triple-experts architecture, consisting aligned and modality-specific experts with disentangled embeddings, is fine-tuned in a frequency-aware manner. Experiments conducted on three public datasets demonstrate the effectiveness of PAD, showing significant improvements and compatibility with various SR backbone models, especially on cold items. The implementation code and datasets will be publicly available.

AI4EF: Artificial Intelligence for Energy Efficiency in the Building Sector

AI4EF, Artificial Intelligence for Energy Efficiency, is an advanced, user-centric tool designed to support decision-making in building energy retrofitting and efficiency optimization. Leveraging machine learning (ML) and data-driven insights, AI4EF enables stakeholders such as public sector representatives, energy consultants, and building owners to model, analyze, and predict energy consumption, retrofit costs, and environmental impacts of building upgrades. Featuring a modular framework, AI4EF includes customizable building retrofitting, photovoltaic installation assessment, and predictive modeling tools that allow users to input building parameters and receive tailored recommendations for achieving energy savings and carbon reduction goals. Additionally, the platform incorporates a Training Playground for data scientists to refine ML models used by said framework. Finally, AI4EF provides access to the Enershare Data Space to facilitate seamless data sharing and access within the ecosystem. Its compatibility with open-source identity management, Keycloak, enhances security and accessibility, making it adaptable for various regulatory and organizational contexts. This paper presents an architectural overview of AI4EF, its application in energy efficiency scenarios, and its potential for advancing sustainable energy practices through artificial intelligence (AI).

Learning to Hash for Recommendation: A Survey

With the explosive growth of users and items, Recommender Systems (RS) are facing unprecedented challenges on both retrieval efficiency and storage cost. Fortunately, Learning to Hash (L2H) techniques have been shown as a promising solution to address the two dilemmas, whose core idea is encoding high-dimensional data into compact hash codes. To this end, L2H for RS (HashRec for short) has recently received widespread attention to support large-scale recommendations. In this survey, we present a comprehensive review of current HashRec algorithms. Specifically, we first introduce the commonly used two-tower models in the recall stage and identify two search strategies frequently employed in L2H. Then, we categorize prior works into two-tier taxonomy based on: (i) the type of loss function and (ii) the optimization strategy. We also introduce some commonly used evaluation metrics to measure the performance of HashRec algorithms. Finally, we shed light on the limitations of the current research and outline the future research directions. Furthermore, the summary of HashRec methods reviewed in this survey can be found at \href{https://github.com/Luo-Fangyuan/HashRec}{https://github.com/Luo-Fangyuan/HashRec}.

Towards Data Governance of Frontier AI Models

Data is essential to train and fine-tune today's frontier artificial intelligence (AI) models and to develop future ones. To date, academic, legal, and regulatory work has primarily addressed how data can directly harm consumers and creators, such as through privacy breaches, copyright infringements, and bias and discrimination. Our work, instead, focuses on the comparatively neglected question of how data can enable new governance capacities for frontier AI models. This approach for "frontier data governance" opens up new avenues for monitoring and mitigating risks from advanced AI models, particularly as they scale and acquire specific dangerous capabilities. Still, frontier data governance faces challenges that stem from the fundamental properties of data itself: data is non-rival, often non-excludable, easily replicable, and increasingly synthesizable. Despite these inherent difficulties, we propose a set of policy mechanisms targeting key actors along the data supply chain, including data producers, aggregators, model developers, and data vendors. We provide a brief overview of 15 governance mechanisms, of which we centrally introduce five, underexplored policy recommendations. These include developing canary tokens to detect unauthorized use for producers; (automated) data filtering to remove malicious content for pre-training and post-training datasets; mandatory dataset reporting requirements for developers and vendors; improved security for datasets and data generation algorithms; and know-your-customer requirements for vendors. By considering data not just as a source of potential harm, but as a critical governance lever, this work aims to equip policymakers with a new tool for the governance and regulation of frontier AI models.

LuxEmbedder: A Cross-Lingual Approach to Enhanced Luxembourgish Sentence Embeddings

Sentence embedding models play a key role in various Natural Language Processing tasks, such as in Topic Modeling, Document Clustering and Recommendation Systems. However, these models rely heavily on parallel data, which can be scarce for many low-resource languages, including Luxembourgish. This scarcity results in suboptimal performance of monolingual and cross-lingual sentence embedding models for these languages. To address this issue, we compile a relatively small but high-quality human-generated cross-lingual parallel dataset to train LuxEmbedder, an enhanced sentence embedding model for Luxembourgish with strong cross-lingual capabilities. Additionally, we present evidence suggesting that including low-resource languages in parallel training datasets can be more advantageous for other low-resource languages than relying solely on high-resource language pairs. Furthermore, recognizing the lack of sentence embedding benchmarks for low-resource languages, we create a paraphrase detection benchmark specifically for Luxembourgish, aiming to partially fill this gap and promote further research.

A Contemporary Overview: Trends and Applications of Large Language Models on Mobile Devices

With the rapid development of large language models (LLMs), which possess powerful natural language processing and generation capabilities, LLMs are poised to provide more natural and personalized user experiences. Their deployment on mobile devices is gradually becoming a significant trend in the field of intelligent devices. LLMs have demonstrated tremendous potential in applications such as voice assistants, real-time translation, and intelligent recommendations. Advancements in hardware technologies (such as neural network accelerators) and network infrastructure (such as 5G) have enabled efficient local inference and low-latency intelligent responses on mobile devices. This reduces reliance on cloud computing while enhancing data privacy and security. Developers can easily integrate LLM functionalities through open APIs and SDKs, enabling the creation of more innovative intelligent applications. The widespread use of LLMs not only enhances the intelligence of mobile devices but also fosters the integrated innovation of fields like augmented reality (AR) and the Internet of Things (IoT). This trend is expected to drive the development of the next generation of mobile intelligent applications.

Recommender Systems for Sustainability: Overview and Research Issues

Sustainability development goals (SDGs) are regarded as a universal call to action with the overall objectives of planet protection, ending of poverty, and ensuring peace and prosperity for all people. In order to achieve these objectives, different AI technologies play a major role. Specifically, recommender systems can provide support for organizations and individuals to achieve the defined goals. Recommender systems integrate AI technologies such as machine learning, explainable AI (XAI), case-based reasoning, and constraint solving in order to find and explain user-relevant alternatives from a potentially large set of options. In this article, we summarize the state of the art in applying recommender systems to support the achievement of sustainability development goals. In this context, we discuss open issues for future research.