Accurate conversion rate (CVR) prediction is critical for recommender systems to capture user conversion intent and increase platform revenues. Traditional CVR models commonly suffer from sample selection bias (SSB) and data sparsity (DS), which has led to the adoption of click-through & conversion rate (CTCVR) multi-task learning frameworks to alleviate these issues. However, existing methods implicitly mislabel some unclicked samples with genuine conversion potential as negatives, thereby exacerbating the false negative sample (FNS) problem. To address this, we propose IdeFN, a multi‑task CVR framework that identifies false negatives in the unclicked space to enable CVR prediction across the entire exposure space and leverages CTR as an auxiliary task for shared‑parameter learning. Specifically, IdeFN consists of two main components, i.e., relaxed partial optimal transport (RPOT) module and sample relabeling mechanism (SRM). The former estimates the soft matching strengths between unclicked samples and positive samples under a relaxed partial optimal transport formulation, establishing corresponding relationships between these samples. The latter adaptively re-labels the unclicked samples according to the derived matching strengths, without relying on static or heuristic thresholds, thus enhancing the reliability of the generated pseudo-labels. Experimental results demonstrate that IdeFN effectively mitigates the FNS problem, achieving substantial improvements in CVR prediction accuracy.

Sequential recommendation aims to predict the next user interaction by modeling historical behavior sequences. Recently diffusion models (DMs) have emerged as a promising generative approach due to their robustness and capacity for uncertainty modeling. However, existing diffusion-based recommendation approaches still encounter two major challenges: sample drift during the noise injection process, which compromises the stability of generation; and limited adaptability to noisy data, which hampers the effectiveness of personalized recommendations. To address these issues, we propose GSDiffRec, a novel generative sequential recommendation approach that integrates two core modules: (i) Semantic-Targeted Guidance Module (STG) built upon an enhanced Transformer backbone equipped with shaped attention and convolutional components to improve representational efficiency and modeling capacity; and (ii) Geodesic Diffusion Module (GDM) enforcing manifold constraints through geodesic random walks, thereby preserving geometric consistency and enhancing denoising stability throughout the diffusion process. Extensive experiments on two public Amazon datasets demonstrate that GSDiffRec significantly outperforms a wide range of competitive baselines. Further ablation studies validate the complementary contributions and effectiveness of the GDM and STG modules.

With the widespread adoption of multimedia web APIs (API) in web and mobile applications, a substantial proliferation of these APIs is observed. These APIs have streamlined development processes, reducing both time and costs. Nevertheless, identifying the required APIs from the vast array of options has emerged as a significant challenge. Collaborative filtering (CF)-based recommendation technologies have demonstrated their efficiency in presenting developers with potentially useful APIs. However, these methods often suffer from popularity bias, i.e., popular APIs tend to dominate the recommendation lists. This imbalance in recommendation opportunities among APIs hinders the growth of the multimedia API ecosystem. To mitigate the popularity bias produced by CF-based API recommendation methods, this article introduces a novel debiasing strategy that combines a log postprocessing adjustment (LPA) with determinant point process (DPP). Specifically, the LPA is employed during the prediction phase to yield a more balanced set of candidate APIs. Then, DPP is utilized to generate recommendation lists that are not just relevant but also diverse in terms of API popularity. Experimental results reveal that our proposed method surpasses existing state-of-the-art approaches in multimedia API recommendation, excelling in both accuracy and the capability to mitigate popularity bias effectively.

The explosive growth of the Application Programming Interfaces (APIs) economy in recent years has led to a dramatic increase in available APIs. Mashup development, a dominant approach for creating data-centric applications based on APIs, has experienced a surge in popularity. However, the vast array of choices poses a challenge for mashup developers when selecting appropriate API compositions to meet specific business requirements. Correlation graph-based recommendation approaches have been designed to assist developers in discovering related and compatible API compositions for mashup creation. Unfortunately, these approaches often suffer from popularity bias issues, leading to an inequality in API usage and potential disruptions to the entire API ecosystem. To address these challenges, our research begins with a theoretical analysis of the popularity bias introduced by correlation graph-based API recommendation approaches. Subsequently, we empirically validate the presence of popularity bias in API recommendations through a data-driven study. Finally, we introduce the p opularity b ias aware w eb A PI r ecommendation ( PB-WAR ) approach to mitigate popularity bias in correlation graph-based API recommendations. Experimental results over a real-world dataset demonstrate that PB-WAR offers the optimal tradeoff between accuracy and debiasing performance compared to other competitive methods.

Summary In this article, we offer an in‐depth exploration of the history, advancements, and innovations in the field of animation. From the roots of traditional hand‐drawn animation to the contemporary realm of computer‐generated imagery, the article outlines the progression of techniques and technologies that have revolutionized the industry. This study meticulously examines the transformative impact of emerging trends such as virtual reality, augmented reality, and artificial intelligence on the development and application of animation. A significant focus is placed on the societal implications of animation, investigating its pervasive influence in fields as diverse as education, advertising, and entertainment. The use of animation as a pedagogical tool, a marketing strategy, and a medium for storytelling has not only redefined these sectors but has also reshaped audience expectations and experiences. In forecasting the future of animation, the article identifies and analyzes potential technologies and trends poised to push the boundaries of the field. This includes examining challenges such as the ethical considerations of AI and the technical constraints of VR and AR. By providing a comprehensive overview of past developments and future possibilities, this review contributes to the ongoing discourse on the continually evolving landscape of animation.

In many fields, such as social networks and recommendation systems with high time requirements, fake news and false information are often released in real time, impacting on people’s daily life. Entity alignment (EA) in temporal knowledge graph (TKG) can fuse the information contained in entities by finding equivalent entities, thus helping to determine the regular pattern of disinformation under time change. The existing methods either ignore the use of temporal attributes’ information and structural information or the modeling of that is insufficient, which has become a major obstacle to the further and wider application of TKG EA. In this article, we put forward a new idea of training for the processing of time attributes and relational structure information, to further enhance the ability in the EA process of TKGs. By forming box embedding matrix and name embedding matrix, and adaptively fusing the above information, we propose a new TKG EA solution. We carry out comparative experiments on standard news media and social media datasets collected from the real world, which validates the effectiveness of our proposal.

In geophysics, full waveform inversion rely on an efficient forward modeling method. However, a direct solver is computationally expensive and requires significant in-core memory. Furthermore, the iterative solver is hampered by the problem of the convergence. In this work, a compressive sensing (CS) solver is proposed for the frequency-domain acoustic wave modeling. Critical factors for the CS solver are the construction of a sparse transform matrix and an efficient recovery algorithm. Because of the characteristics of seismic wavefields in the frequency domain, we introduce the K-singular value decomposition (K-SVD) algorithm to construct the sparse transform matrix. Once a sparse representation is achieved, the size of the impedance matrix is strongly reduced. Numerical results obtained in homogeneous media, the Marmousi II model, and the Society of Exploration Geophysicists (SEG)/European Association of Geoscientists and Engineers (EAGE) salt model reveal that the proposed CS solver significantly reduces computation cost compared with the direct solver and the iterative solver. In addition, the comparison with the iterative solver shows that the CS solver may speed up the convergence.

The ever-increasing popularity of the recommender system provides a convenient way for users to find their interesting items among plenty of candidate services. However, on account of the enhancement of user privacy protection consciousness in recent years, users tend to conceal their evaluation information from the public. Thus, a large number of users with little explicit rating information are generated (i.e., cold-start users), which makes it challenging to implement high-quality recommendations. It has become a serious barrier to further and broader applications of the recommender system. In response to this issue, we take social network information into account and first propose TeCF (Trust-enhanced Collaborative Filtering). Our proposal integrates user-based, item-based, and trust-based collaborative filtering methods harmoniously and achieves a good trade-off between privacy preservation and service recommendation accuracy. A case study is conducted to validate the feasibility and comprehensiveness of our research.