Semantic understanding of popularity bias is a crucial yet underexplored challenge in recommender systems, where popular items are often favored at the expense of niche content. Most existing debiasing methods treat the semantic understanding of popularity bias as a matter of diversity enhancement or long-tail coverage, neglecting the deeper semantic layer that embodies the causal origins of the bias itself. Consequently, such shallow interpretations limit both their debiasing effectiveness and recommendation accuracy. In this paper, we propose FairLRM, a novel framework that bridges the gap in the semantic understanding of popularity bias with Recommendation via Large Language Model (RecLLM). FairLRM decomposes popularity bias into item-side and user-side components, using structured instruction-based prompts to enhance the model's comprehension of both global item distributions and individual user preferences. Unlike traditional methods that rely on surface-level features such as ''diversity'' or ''debiasing'', FairLRM improves the model's ability to semantically interpret and address the underlying bias. Through empirical evaluation, we demonstrate that FairLRM enhances fairness and recommendation accuracy through a trustworthy, semantically grounded treatment of popularity bias. The source code is shown in https://github.com/LuoRenqiang/FairLRM.

Reconstructing 3D assets from images, known as inverse rendering (IR), remains a challenging task due to its ill-posed nature. 3D Gaussian Splatting (3DGS) has demonstrated impressive capabilities for novel view synthesis (NVS) tasks. Methods apply it to relighting by separating radiance into BRDF parameters and lighting, yet produce inferior relighting quality with artifacts and unnatural indirect illumination due to the limited capability of each Gaussian, which has constant material parameters and normal, alongside the absence of physical constraints for indirect lighting. In this paper, we present a novel framework called Spatially-vayring Gaussian Inverse Rendering (SVG-IR), aimed at enhancing both NVS and relighting quality. To this end, we propose a new representation—Spatially-varying Gaussian (SVG)—that allows per-Gaussian spatially varying parameters. This enhanced representation is complemented by a SVG splatting scheme akin to vertex/fragment shading in traditional graphics pipelines. Furthermore, we integrate a physically-based indirect lighting model, enabling more realistic relighting. The proposed SVG-IR framework significantly improves rendering quality, outperforming state-of-the-art NeRF-based methods by 2.5 dB in peak signal-to-noise ratio (PSNR) and surpassing existing Gaussian-based techniques by 3.5 dB in relighting tasks, all while maintaining a real-time rendering speed. The source code is available at https://github.com/learner-shx/SVG-IR.

Conversational interfaces for the detail-oriented retail fashion domain are more natural, expressive, and user friendly than classical keyword-based search interfaces. In this paper, we introduce the Fashion IQ dataset to support and advance research on interactive fashion image retrieval. Fashion IQ is the first fashion dataset to provide human-generated captions that distinguish similar pairs of garment images together with side-information consisting of real-world product descriptions and derived visual attribute labels for these images. We provide a detailed analysis of the characteristics of the Fashion IQ data, and present a transformer-based user simulator and interactive image retriever that can seamlessly integrate visual attributes with image features, user feedback, and dialog history, leading to improved performance over the state of the art in dialog-based image retrieval. We believe that our dataset will encourage further work on developing more natural and real-world applicable conversational shopping assistants.1

Interactive Fiction (IF) games with real humanwritten natural language texts provide a new natural evaluation for language understanding techniques.In contrast to previous text games with mostly synthetic texts, IF games pose language understanding challenges on the humanwritten textual descriptions of diverse and sophisticated game worlds and language generation challenges on the action command generation from less restricted combinatorial space.We take a novel perspective of IF game solving and re-formulate it as Multi-Passage Reading Comprehension (MPRC) tasks.Our approaches utilize the context-query attention mechanisms and the structured prediction in MPRC to efficiently generate and evaluate action outputs and apply an object-centric historical observation retrieval strategy to mitigate the partial observability of the textual observations.Extensive experiments on the recent IF benchmark (Jericho) demonstrate clear advantages of our approaches achieving high winning rates and low data requirements compared to all previous approaches. 1

Ming Tan, Dakuo Wang, Yupeng Gao, Haoyu Wang, Saloni Potdar, Xiaoxiao Guo, Shiyu Chang, Mo Yu. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Limited access to supervised information may forge scenarios in real-world data mining applications, where training and test data are interconnected by a covariate shift, i.e., having equal class conditional distribution with unequal covariate distribution. Traditional data mining techniques assume that both training and test data represent an identical distribution, therefore suffer in presence of a covariate shift. Kernel Mean Matching (KMM) is a well known approach that addresses covariate shift by weighing training instances appropriately. However, it has time complexity cubic in the size of training data, which is computationally impractical for large or streaming datasets due to limited scalability. In this paper, we present a sampling-based algorithm to address the limited scalability problem of KMM. Moreover, we show that the approach is highly parallelizable, and therefore propose a distributed algorithm for estimating training instance weights efficiently using Spark. Experiment results on benchmark datasets show that the proposed approach achieves competitive estimation accuracy within much lower execution time compared to the KMM algorithm. Moreover, it indicates that larger size of training data results into a higher accuracy with minimal effect on execution time of the proposed approach.

In order to find out the tradeoff between recovery time and resource overhead when a link failure occurs, we propose a software-defined based fast restoration scheme for elastic optical datacenter networks, called precomputation based restoration path (P-RP). By extending the controller functionality and OpenFlow protocol in software defined networking (SDN) technology, we establish a novel elastic optical inter-datacenter network architecture which can quickly and accurately converge the network state information with a global view for failure recovery. Based on this architecture, the P-RP performs path computation and bandwidth resource selection for restoration before a failure occurs. The experimental results demonstrate that the proposed scheme can achieve fast recovery with low blocking probability while maintaining high spectrum efficiency. Compared with existing restoration schemes, average recovery time is improved by up to 28%.

Calibrations for Wi-Fi Indoor Positioning Systems (IPS) are expensive and time-consuming. Recalibrations are necessary periodically to compensate the drifts of radio map. Many crowdsourcing-based approaches have been proposed to calibrate the radio map strategically with user-contributed data. However, these approaches still suffer from several drawbacks. Calibration-on-demand is desirable to reduce the maintenance cost and to enhance the reliability of the Wi-Fi IPS. Wi-Fi IPS degrades only when the radio map changes significantly and lastingly. Such changes are caused by radio map anomalous events. Examples of these events include changes in indoor environment, existence of spurious data from the sensing system and unexpected failure in utility infrastructures. This paper presents a theoretical and experimental study of a crowdsourcing online Wi-Fi radio map anomalous event detection system. Detection of these events will enhance the operation intelligence of Wi-Fi IPS and enable high-level context-aware services. This system is consisted of an outlier detector for abnormal signal identification and an event discriminator for determination of event occurrence. Real world smartphone data and simulated data are used to test the performance of the system. Initial results show that the system is able to detect over 92% of simulated events and 87.5% of real world events with a low false alarm rate.