Session-Based Recommendation by Exploiting Substitutable and Complementary Relationships from Multi-behavior Data

Session-based recommendation (SR) aims to dynamically recommend items to a user based on a sequence of the most recent user-item interactions. Most existing studies on SR adopt advanced deep learning methods. However, the majority only consider a special behavior type (e.g., click), while those few considering multi-typed behaviors ignore to take full advantage of the relationships between products (items). In this case, the paper proposes a novel approach, called Substitutable and Complementary Relationships from Multi-behavior Data (denoted as SCRM) to better explore the relationships between products for effective recommendation. Specifically, we firstly construct substitutable and complementary graphs based on a user's sequential behaviors in every session by jointly considering `click' and `purchase' behaviors. We then design a denoising network to remove false relationships, and further consider constraints on the two relationships via a particularly designed loss function. Extensive experiments on two e-commerce datasets demonstrate the superiority of our model over state-of-the-art methods, and the effectiveness of every component in SCRM.Comment: 31 pages,11 figures, accepted by Data Mining and Knowledge Discovery(2023

Dense-Localizing Audio-Visual Events in Untrimmed Videos: A Large-Scale Benchmark and Baseline

Existing audio-visual event localization (AVE) handles manually trimmed videos with only a single instance in each of them. However, this setting is unrealistic as natural videos often contain numerous audio-visual events with different categories. To better adapt to real-life applications, in this paper we focus on the task of dense-localizing audio-visual events, which aims to jointly localize and recognize all audio-visual events occurring in an untrimmed video. The problem is challenging as it requires fine-grained audio-visual scene and context understanding. To tackle this problem, we introduce the first Untrimmed Audio-Visual (UnAV-100) dataset, which contains 10K untrimmed videos with over 30K audio-visual events. Each video has 2.8 audio-visual events on average, and the events are usually related to each other and might co-occur as in real-life scenes. Next, we formulate the task using a new learning-based framework, which is capable of fully integrating audio and visual modalities to localize audio-visual events with various lengths and capture dependencies between them in a single pass. Extensive experiments demonstrate the effectiveness of our method as well as the significance of multi-scale cross-modal perception and dependency modeling for this task.Comment: Accepted by CVPR202

Improving Adversarial Energy-Based Model via Diffusion Process

Generative models have shown strong generation ability while efficient likelihood estimation is less explored. Energy-based models~(EBMs) define a flexible energy function to parameterize unnormalized densities efficiently but are notorious for being difficult to train. Adversarial EBMs introduce a generator to form a minimax training game to avoid expensive MCMC sampling used in traditional EBMs, but a noticeable gap between adversarial EBMs and other strong generative models still exists. Inspired by diffusion-based models, we embedded EBMs into each denoising step to split a long-generated process into several smaller steps. Besides, we employ a symmetric Jeffrey divergence and introduce a variational posterior distribution for the generator's training to address the main challenges that exist in adversarial EBMs. Our experiments show significant improvement in generation compared to existing adversarial EBMs, while also providing a useful energy function for efficient density estimation

Application of ultrasound-guided anterior quadratus lumborum block at the lateral supra-arcuate ligament in bariatric surgery

Objective To compare the analgesic effect of ultrasound-guided anterior quadratus lumborum block at the lateral supra-arcuate ligament (QLB-LSAL) and transversus abdominis plane block (TAPB) in laparoscopic sleeve gastrectomy (LSG). Methods From January 2023 to January 2024, 90 patients underwent LSG in Suqian First People's Hospital were randomly divided into two groups: QLB-LSAL group and TAPB group, 45 cases in each group. Bilateral nerve block was performed before induction of general anesthesia, and 0.375% ropivacaine 20 mL was injected into each side of both groups. Both groups of patients received the same general anesthesia and postoperative patient-controlled intravenous analgesia (PCIA) regimen. The number of block dermatomes after block, mean arterial pressure (MAP), heart rate (HR), visual analogue scale (VAS) score were measured in different time. The intraoperative consumption of sufentanil and remifentanil, the interval time from the end of operation to the first pressing of the analgesia pump, the consumption of analgesics within 48 h after operation, the requirement for rescue analgesia, and the incidence of adverse reactions were recorded. Results The MAP and HR at 1 min and 5 min after skin incision, the intraoperative consumption of remifentanil, the VAS score at 2,6,12,24 h after operation, the consumption of analgesics within 48 h after operation, and the incidence of nausea and vomiting in QLB-LSAL group were significantly lower than those in TAPB group (P＜0.05). The number of block dermatomes at 5 min, 10 min, 6 h, 24 h after block, and the interval time from the end of operation to the first pressing of the analgesia pump in QLB-LSAL group were significantly higher than those in TAPB group (P＜0.05). There was no significant difference in the intraoperative consumption of sufentanil, the requirement for rescue analgesia, and the incidence of respiratory depression between the two groups (P＞0.05). Conclusion Ultrasound-guided QLB-LSAL combined with general anesthesia can stabilize hemodynamics, reduce the consumption of intraoperative opioids, and provide effective postoperative analgesia in patients received LSG