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The ability of Large Language Models (LLMs) to generate high-quality text and code has fuelled their rise in popularity. In this paper, we aim to demonstrate the potential of LLMs within the realm of optimization algorithms by integrating them into ...

Search Trajectory Networks (STNs) serve as a tool for visualizing algorithm behavior within the realm of optimization problems. Despite their user-friendly nature, challenges arise in obtaining interpretable plots, for example, in the case of ...

Applying large Language Models (LM) to unlock natural language based interactions with game NPCs is an emerging frontier. But the LMs of today are prohibitively expensive to run locally, especially on mobile platforms due to limited runtime resources, ...

VolumeMatic is an Apple Vision Pro natural language text to interactive volume app creator app, utilizing the “chat to create" motif. The user can easily create 3D using various AI3D creation methods, such as text to 3D and image to 3D from different ...

Vibrotactile interaction is an essential source of HCI and should be on a large scale, such as room scale, whereas in the past many devices have been small. However, there is a practical difficulties for implementing vibrotactile actuators and sensors ...

Emerging short-video platforms like TikTok, Instagram Reels, and ShareChat present unique challenges for recommender systems, primarily originating from a continuous stream of new content. ShareChat alone receives approximately 2 million pieces of fresh ...

Embedding-based retrieval in graph-based recommendation has shown great improvements over traditional graph walk retrieval methods, and has been adopted in large-scale industry applications such as friend recommendations [16]. However, it is not without ...

Misinformation is a global challenge, but successful mitigations must come from the communities affected and not be imposed by external entities. Co·Insights is a multi-year NSF-funded initiative building capacity to respond to misinformation and harmful ...

Recent advances in generative artificial intelligence have revolutionized information retrieval and content generation, opening up new opportunities for the e-commerce industry. In particular, text-to-image generation models offer a novel approach to ...

Information diffusion prediction is fundamental to understand the structure and organization of the online social networks, and plays a crucial role to blocking rumor spread, influence maximization, political propaganda, etc. So far, most existing ...

Click-through rate (CTR) prediction plays an important role in online advertising platforms. Most existing methods use data from the advertising platform itself for CTR prediction. As user behaviors also exist on many other platforms, e.g., media ...

Knowledge Bases (KBs) find applications in many knowledge-intensive tasks and, most notably, in information retrieval. Wikidata is one of the largest public general-purpose KBs. Yet, its collaborative nature has led to a convoluted schema and taxonomy. ...

Recommender systems often grapple with noisy implicit feedback. Most studies alleviate the noise issues from data cleaning perspective such as data resampling and reweighting, but they are constrained by heuristic assumptions. Another denoising avenue is ...

In online advertising, the sample selection bias problem is a major cause of inaccurate conversion rate estimates. Current mainstream solutions only perform causality-based optimization in the click space since the conversion labels in the non-click ...

Information access systems are getting complex, and our understanding of user behavior during information seeking processes is mainly drawn from qualitative methods, such as observational studies or surveys. Leveraging the advances in sensing ...

Text-to-image generation systems have emerged as revolutionary tools in the realm of artistic creation, offering unprecedented ease in transforming textual prompts into visual art. However, the efficacy of these systems is intricately linked to the ...

Cross-domain recommendation (CDR) is a widely used methodology in recommender systems to combat data sparsity. It leverages user data across different domains or platforms for providing personalized recommendations. Traditional CDR assumes user ...

E-commerce platforms usually present an ordered list, mixed with several organic items and an advertisement, in response to each user's page view request. This list, the outcome of ad auction and allocation processes, directly impacts the platform's ad ...

As an emerging privacy-preserving approach to leveraging cross-platform user interactions, vertical federated learning (VFL) has been increasingly applied in recommender systems. However, vanilla VFL is only applicable to overlapped users, ignoring ...

Diaspora communities are disproportionately impacted by off-the-radar misinformation and often neglected by mainstream fact-checking efforts, creating a critical need to scale-up efforts of nascent fact-checking initiatives. In this paper we present ...