With the advent of many movie content platforms, users face a flood of content and consequent difficulties in selecting appropriate movie titles. Although much research has been conducted in developing effective recommender systems to provide personalized recommendations based on customers’ past preferences and behaviors, not much attention has been paid to leveraging users’ sentiments and emotions together. In this study, we built a new graph-based movie recommender system that utilized sentiment and emotion information along with user ratings, and evaluated its performance in comparison to well known conventional models and state-of-the-art graph-based models. The sentiment and emotion information were extracted using fine-tuned BERT. We used a Kaggle dataset created by crawling movies’ meta-data and review data from the Rotten Tomatoes website and Amazon product data. The study results show that the proposed IGMC-based models coupled with emotion and sentiment are superior over the compared models. The findings highlight the significance of using sentiment and emotion information in relation to movie recommendation.

The internet of things (IoT) requires that more data are collected and processed by devices with faster response, but lower power consumption. In order to achieve these unprecedented requirements, a high-speed DRAM with low-power dissipation at standby and during low-frequency operation in a small footprint is necessary. In addition, a higher degree of reliability is expected for some applications: such as transportation and healthcare. One promising solution, on-chip error correction coding (ECC), has gained traction among memory companies [1] and is now required by the LPDDR4 standard for the first time [2]. In this paper, a 2Gb LPDDR4 that dissipates 75% less refresh current than a conventional LPDDR4 is presented. The proposed LPDDR4 exploits ECC to reduce refresh current, periodically activated voltage regulators to reduce standby power, and dual CA buffers to save the buffer current during low frequency operation. In addition, circuit designs for cell screen of DRAM with on-chip ECC, and for high-speed wafer test for known good die (KGD) - essential for small form factor devices using system-in-package (SiP) - are presented.

The application of language models in essay scoring has gained significant attention in recent years, typically on the basis of evaluating a single model across multiple prompts. However, in a multi-prompt setup, it is crucial to understand the varying aspects of different prompts. In such settings, there exist notable variations even in a trait with the same name across prompts. This semantic variation on the same traits underscores the need to treat them differently at a fine-grained level according to each prompt. In this study, we propose a multi-level disentanglement framework for multi-prompt essay scoring, designed to achieve fine-grained disentanglement of semantic differences across such traits. Our method not only improves the quality of the essay scoring, but also reduces memory usage and latency. Experimental results highlight that our framework surpasses seven state-of-the-art essay scoring methods and large language model(LLM)-based zero-shot and few-shot approaches, achieving the highest agreement with human essay ratings.