Beyond Classification: Latent User Interests Profiling from Visual Contents Analysis

User preference profiling is an important task in modern online social networks (OSN). With the proliferation of image-centric social platforms, such as Pinterest, visual contents have become one of the most informative data streams for understanding user preferences. Traditional approaches usually treat visual content analysis as a general classification problem where one or more labels are assigned to each image. Although such an approach simplifies the process of image analysis, it misses the rich context and visual cues that play an important role in people's perception of images. In this paper, we explore the possibilities of learning a user's latent visual preferences directly from image contents. We propose a distance metric learning method based on Deep Convolutional Neural Networks (CNN) to directly extract similarity information from visual contents and use the derived distance metric to mine individual users' fine-grained visual preferences. Through our preliminary experiments using data from 5,790 Pinterest users, we show that even for the images within the same category, each user possesses distinct and individually-identifiable visual preferences that are consistent over their lifetime. Our results underscore the untapped potential of finer-grained visual preference profiling in understanding users' preferences.Comment: 2015 IEEE 15th International Conference on Data Mining Workshop

Evaluation of the green development efficiency of marine fish culture in China

Green development efficiency (GDE) is an important criterion for measuring the level of green development. GDE considers not only economic development efficiency but also environmental costs. In China, marine fish culture, as one of the pillar industries of mariculture, promotes green development and industrial transformation and upgradation. Based on data from the field surveys of marine fish farmers (2017–2019) and the China Fishery Statistical Yearbook (2018–2020), this study establishes an evaluation index system and uses the super-slack-based measure model (Super-SBM) to evaluate the GDE of marine fish culture. The results show that the average GDE of marine fish culture in China was 0.9529, which was in an inefficient state. As for culture species, golden pompano (Trachinotus ovatus) and cobia (Rachycentron canadum) were the two species farmed in an efficient state, with a GDE of 1.2107 and 1.0659, respectively. Regarding culture modes, green modes (offshore cage aquaculture, industrial recirculating aquaculture, and engineering pond aquaculture) were in an efficient state, with a GDE of 1.2310, 1.0827, and 1.0401, respectively. Traditional modes (industrial flow-through aquaculture, ordinary cage aquaculture, and ordinary pond aquaculture) were in an inefficient state, with their GDE being 0.9884, 0.8746, and 0.8248, respectively. Green modes have higher GDE than traditional modes. In contrast, the production and culture areas of green modes were less than those of traditional modes because the profits of the same species in green modes were lower than those in traditional modes. The results of this study present an objective assessment of the GDE of marine fish culture in China and provide valuable insights for analyzing the mechanisms to improve the GDE of marine fish culture

S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking in the Era of LLMs

The traditional Dialogue State Tracking (DST) problem aims to track user preferences and intents in user-agent conversations. While sufficient for task-oriented dialogue systems supporting narrow domain applications, the advent of Large Language Model (LLM)-based chat systems has introduced many real-world intricacies in open-domain dialogues. These intricacies manifest in the form of increased complexity in contextual interactions, extended dialogue sessions encompassing a diverse array of topics, and more frequent contextual shifts. To handle these intricacies arising from evolving LLM-based chat systems, we propose joint dialogue segmentation and state tracking per segment in open-domain dialogue systems. Assuming a zero-shot setting appropriate to a true open-domain dialogue system, we propose S3-DST, a structured prompting technique that harnesses Pre-Analytical Recollection, a novel grounding mechanism we designed for improving long context tracking. To demonstrate the efficacy of our proposed approach in joint segmentation and state tracking, we evaluate S3-DST on a proprietary anonymized open-domain dialogue dataset, as well as publicly available DST and segmentation datasets. Across all datasets and settings, S3-DST consistently outperforms the state-of-the-art, demonstrating its potency and robustness the next generation of LLM-based chat systems

PEARL: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrievers

Powerful large language models have facilitated the development of writing assistants that promise to significantly improve the quality and efficiency of composition and communication. However, a barrier to effective assistance is the lack of personalization in LLM outputs to the author's communication style and specialized knowledge. In this paper, we address this challenge by proposing PEARL, a retrieval-augmented LLM writing assistant personalized with a generation-calibrated retriever. Our retriever is trained to select historic user-authored documents for prompt augmentation, such that they are likely to best personalize LLM generations for a user request. We propose two key novelties for training our retriever: 1) A training data selection method that identifies user requests likely to benefit from personalization and documents that provide that benefit; and 2) A scale-calibrating KL-divergence objective that ensures that our retriever closely tracks the benefit of a document for personalized generation. We demonstrate the effectiveness of PEARL in generating personalized workplace social media posts and Reddit comments. Finally, we showcase the potential of a generation-calibrated retriever to double as a performance predictor and further improve low-quality generations via LLM chaining.Comment: Pre-print, work in progres