Integrating Response Time and Attention Duration in Bayesian Preference Learning for Multiple Criteria Decision Aiding

We introduce a multiple criteria Bayesian preference learning framework incorporating behavioral cues for decision aiding. The framework integrates pairwise comparisons, response time, and attention duration to deepen insights into decision-making processes. The approach employs an additive value function model and utilizes a Bayesian framework to derive the posterior distribution of potential ranking models by defining the likelihood of observed preference data and specifying a prior on the preference structure. This distribution highlights each model's ability to reconstruct Decision-Makers' holistic pairwise comparisons. By leveraging both response time as a proxy for cognitive effort and alternative discriminability as well as attention duration as an indicator of criterion importance, the proposed model surpasses traditional methods by uncovering richer behavioral patterns. We report the results of a laboratory experiment on mobile phone contract selection involving 30 real subjects using a dedicated application with time-, eye-, and mouse-tracking components. We validate the novel method's ability to reconstruct complete preferences. The detailed ablation studies reveal time- and attention-related behavioral patterns, confirming that integrating comprehensive data leads to developing models that better align with the DM's actual preferences.

AgentA/B: Automated and Scalable Web A/BTesting with Interactive LLM Agents

A/B testing experiment is a widely adopted method for evaluating UI/UX design decisions in modern web applications. Yet, traditional A/B testing remains constrained by its dependence on the large-scale and live traffic of human participants, and the long time of waiting for the testing result. Through formative interviews with six experienced industry practitioners, we identified critical bottlenecks in current A/B testing workflows. In response, we present AgentA/B, a novel system that leverages Large Language Model-based autonomous agents (LLM Agents) to automatically simulate user interaction behaviors with real webpages. AgentA/B enables scalable deployment of LLM agents with diverse personas, each capable of navigating the dynamic webpage and interactively executing multi-step interactions like search, clicking, filtering, and purchasing. In a demonstrative controlled experiment, we employ AgentA/B to simulate a between-subject A/B testing with 1,000 LLM agents Amazon.com, and compare agent behaviors with real human shopping behaviors at a scale. Our findings suggest AgentA/B can emulate human-like behavior patterns.

Deep learning for state estimation of commercial sodium-ion batteries using partial charging profiles: validation with a multi-temperature ageing dataset

Accurately predicting the state of health for sodium-ion batteries is crucial for managing battery modules, playing a vital role in ensuring operational safety. However, highly accurate models available thus far are rare due to a lack of aging data for sodium-ion batteries. In this study, we experimentally collected 53 single cells at four temperatures (0, 25, 35, and 45 {\deg}C), along with two battery modules in the lab. By utilizing the charging profiles, we were able to predict the SOC, capacity, and SOH simultaneously. This was achieved by designing a new framework that integrates the neural ordinary differential equation and 2D convolutional neural networks, using the partial charging profile as input. The charging profile is partitioned into segments, and each segment is fed into the network to output the SOC. For capacity and SOH prediction, we first aggregated the extracted features corresponding to segments from one cycle, after which an embedding block for temperature is concatenated for the final prediction. This novel approach eliminates the issue of multiple outputs for a single target. Our model demonstrated an $R^2$ accuracy of 0.998 for SOC and 0.997 for SOH across single cells at various temperatures. Furthermore, the trained model can be employed to predict single cells at temperatures outside the training set and battery modules with different capacity and current levels. The results presented here highlight the high accuracy of our model and its capability to predict multiple targets simultaneously using a partial charging profile.

Preference Construction: A Bayesian Interactive Preference Elicitation Framework Based on Monte Carlo Tree Search

We present a novel preference learning framework to capture participant preferences efficiently within limited interaction rounds. It involves three main contributions. First, we develop a variational Bayesian approach to infer the participant's preference model by estimating posterior distributions and managing uncertainty from limited information. Second, we propose an adaptive questioning policy that maximizes cumulative uncertainty reduction, formulating questioning as a finite Markov decision process and using Monte Carlo Tree Search to prioritize promising question trajectories. By considering long-term effects and leveraging the efficiency of the Bayesian approach, the policy avoids shortsightedness. Third, we apply the framework to Multiple Criteria Decision Aiding, with pairwise comparison as the preference information and an additive value function as the preference model. We integrate the reparameterization trick to address high-variance issues, enhancing robustness and efficiency. Computational studies on real-world and synthetic datasets demonstrate the framework's practical usability, outperforming baselines in capturing preferences and achieving superior uncertainty reduction within limited interactions.

Fg-T2M++: LLMs-Augmented Fine-Grained Text Driven Human Motion Generation

We address the challenging problem of fine-grained text-driven human motion generation. Existing works generate imprecise motions that fail to accurately capture relationships specified in text due to: (1) lack of effective text parsing for detailed semantic cues regarding body parts, (2) not fully modeling linguistic structures between words to comprehend text comprehensively. To tackle these limitations, we propose a novel fine-grained framework Fg-T2M++ that consists of: (1) an LLMs semantic parsing module to extract body part descriptions and semantics from text, (2) a hyperbolic text representation module to encode relational information between text units by embedding the syntactic dependency graph into hyperbolic space, and (3) a multi-modal fusion module to hierarchically fuse text and motion features. Extensive experiments on HumanML3D and KIT-ML datasets demonstrate that Fg-T2M++ outperforms SOTA methods, validating its ability to accurately generate motions adhering to comprehensive text semantics.

Sequential LLM Framework for Fashion Recommendation

The fashion industry is one of the leading domains in the global e-commerce sector, prompting major online retailers to employ recommendation systems for product suggestions and customer convenience. While recommendation systems have been widely studied, most are designed for general e-commerce problems and struggle with the unique challenges of the fashion domain. To address these issues, we propose a sequential fashion recommendation framework that leverages a pre-trained large language model (LLM) enhanced with recommendation-specific prompts. Our framework employs parameter-efficient fine-tuning with extensive fashion data and introduces a novel mix-up-based retrieval technique for translating text into relevant product suggestions. Extensive experiments show our proposed framework significantly enhances fashion recommendation performance.

G2T: A Simple but Effective Framework for Topic Modeling based on Pretrained Language Model and Community Detection

It has been reported that clustering-based topic models, which cluster high-quality sentence embeddings with an appropriate word selection method, can generate better topics than generative probabilistic topic models. However, these approaches suffer from the inability to select appropriate parameters and incomplete models that overlook the quantitative relation between words with topics and topics with text. To solve these issues, we propose graph to topic (G2T), a simple but effective framework for topic modelling. The framework is composed of four modules. First, document representation is acquired using pretrained language models. Second, a semantic graph is constructed according to the similarity between document representations. Third, communities in document semantic graphs are identified, and the relationship between topics and documents is quantified accordingly. Fourth, the word--topic distribution is computed based on a variant of TFIDF. Automatic evaluation suggests that G2T achieved state-of-the-art performance on both English and Chinese documents with different lengths. Human judgements demonstrate that G2T can produce topics with better interpretability and coverage than baselines. In addition, G2T can not only determine the topic number automatically but also give the probabilistic distribution of words in topics and topics in documents. Finally, G2T is publicly available, and the distillation experiments provide instruction on how it works.

Cross-Lingual Document Retrieval with Smooth Learning

Cross-lingual document search is an information retrieval task in which the queries' language differs from the documents' language. In this paper, we study the instability of neural document search models and propose a novel end-to-end robust framework that achieves improved performance in cross-lingual search with different documents' languages. This framework includes a novel measure of the relevance, smooth cosine similarity, between queries and documents, and a novel loss function, Smooth Ordinal Search Loss, as the objective. We further provide theoretical guarantee on the generalization error bound for the proposed framework. We conduct experiments to compare our approach with other document search models, and observe significant gains under commonly used ranking metrics on the cross-lingual document retrieval task in a variety of languages.

Explainable Ordinal Factorization Model: Deciphering the Effects of Attributes by Piece-wise Linear Approximation

Ordinal regression predicts the objects' labels that exhibit a natural ordering, which is important to many managerial problems such as credit scoring and clinical diagnosis. In these problems, the ability to explain how the attributes affect the prediction is critical to users. However, most, if not all, existing ordinal regression models simplify such explanation in the form of constant coefficients for the main and interaction effects of individual attributes. Such explanation cannot characterize the contributions of attributes at different value scales. To address this challenge, we propose a new explainable ordinal regression model, namely, the Explainable Ordinal Factorization Model (XOFM). XOFM uses the piece-wise linear functions to approximate the actual contributions of individual attributes and their interactions. Moreover, XOFM introduces a novel ordinal transformation process to assign each object the probabilities of belonging to multiple relevant classes, instead of fixing boundaries to differentiate classes. XOFM is based on the Factorization Machines to handle the potential sparsity problem as a result of discretizing the attribute scales. Comprehensive experiments with benchmark datasets and baseline models demonstrate that the proposed XOFM exhibits superior explainability and leads to state-of-the-art prediction accuracy.

A preference learning framework for multiple criteria sorting with diverse additive value models and valued assignment examples

We present a preference learning framework for multiple criteria sorting. We consider sorting procedures applying an additive value model with diverse types of marginal value functions (including linear, piecewise-linear, splined, and general monotone ones) under a unified analytical framework. Differently from the existing sorting methods that infer a preference model from crisp decision examples, where each reference alternative is assigned to a unique class, our framework allows to consider valued assignment examples in which a reference alternative can be classified into multiple classes with respective credibility degrees. We propose an optimization model for constructing a preference model from such valued examples by maximizing the credible consistency among reference alternatives. To improve the predictive ability of the constructed model on new instances, we employ the regularization techniques. Moreover, to enhance the capability of addressing large-scale datasets, we introduce a state-of-the-art algorithm that is widely used in the machine learning community to solve the proposed optimization model in a computationally efficient way. Using the constructed additive value model, we determine both crisp and valued assignments for non-reference alternatives. Moreover, we allow the Decision Maker to prioritize importance of classes and give the method a flexibility to adjust classification performance across classes according to the specified priorities. The practical usefulness of the analytical framework is demonstrated on a real-world dataset by comparing it to several existing sorting methods.