Large Language Model (LLM) has transformative potential in various domains, including recommender systems (RS). There have been a handful of research that focuses on empowering the RS by LLM. However, previous efforts mainly focus on LLM as RS, which may face the challenge of intolerant inference costs by LLM. Recently, the integration of LLM into RS, known as LLM-Enhanced Recommender Systems (LLMERS), has garnered significant interest due to its potential to address latency and memory constraints in real-world applications. This paper presents a comprehensive survey of the latest research efforts aimed at leveraging LLM to enhance RS capabilities. We identify a critical shift in the field with the move towards incorporating LLM into the online system, notably by avoiding their use during inference. Our survey categorizes the existing LLMERS approaches into three primary types based on the component of the RS model being augmented: Knowledge Enhancement, Interaction Enhancement, and Model Enhancement. We provide an in-depth analysis of each category, discussing the methodologies, challenges, and contributions of recent studies. Furthermore, we highlight several promising research directions that could further advance the field of LLMERS.

If one of your New Year's Resolutions was to get back on the dating scene, today is the day to finally bite the bullet. January 5 is'Dating Sunday' - the first Sunday in January, which is annually recognised as the busiest day for dating apps. On this day, apps including Hinge, Tinder, and Bumble see substantial increases in activity, with users more actively seeking and initiating conversations. 'As we move into the first days of the new year, many people find themselves reflecting on the past year and visioning for the year ahead,' said Moe Ari Brown, Hinge's Love and Connection expert. 'The new year marks a shift from one chapter to the next and offers us the opportunity for a reboot.

As Integrated Development Environments (IDEs) increasingly integrate Artificial Intelligence, Software Engineering faces both benefits like productivity gains and challenges like mismatched user preferences. We propose Hyper-Dimensional (HD) vector spaces to model Human-Computer Interaction, focusing on user actions, stylistic preferences, and project context. These contributions aim to inspire further research on applying HD computing in IDE design.

Good mental health enables individuals to cope with the normal stresses of life. In Germany, approximately one-quarter of the adult population is affected by mental illnesses. Teletherapy and digital health applications are available to bridge gaps in care and relieve healthcare professionals. The acceptance of these tools is a strongly influencing factor for their effectiveness, which also needs to be evaluated for AI-based conversational agents (CAs) (e. g. ChatGPT, Siri) to assess the risks and potential for integration into therapeutic practice. This study investigates the perspectives of both the general population and healthcare professionals with the following questions: 1. How frequently are CAs used for mental health? 2. How high is the acceptance of CAs in the field of mental health? 3. To what extent is the use of CAs in counselling, diagnosis, and treatment acceptable? To address these questions, two quantitative online surveys were conducted with 444 participants from the general population and 351 healthcare professionals. Statistical analyses show that 27 % of the surveyed population already confide their concerns to CAs. Not only experience with this technology but also experience with telemedicine shows a higher acceptance among both groups for using CAs for mental health. Additionally, participants from the general population were more likely to support CAs as companions controlled by healthcare professionals rather than as additional experts for the professionals. CAs have the potential to support mental health, particularly in counselling. Future research should examine the influence of different communication media and further possibilities of augmented intelligence. With the right balance between technology and human care, integration into patient-professional interaction can be achieved.

We present HiRMed (Hierarchical RAG-enhanced Medical Test Recommendation), a novel tree-structured recommendation system that leverages Retrieval-Augmented Generation (RAG) for intelligent medical test recommendations. Unlike traditional vector similarity-based approaches, our system performs medical reasoning at each tree node through a specialized RAG process. Starting from the root node with initial symptoms, the system conducts step-wise medical analysis to identify potential underlying conditions and their corresponding diagnostic requirements. At each level, instead of simple matching, our RAG-enhanced nodes analyze retrieved medical knowledge to understand symptom-disease relationships and determine the most appropriate diagnostic path. The system dynamically adjusts its recommendation strategy based on medical reasoning results, considering factors such as urgency levels and diagnostic uncertainty. Experimental results demonstrate that our approach achieves superior performance in terms of coverage rate, accuracy, and miss rate compared to conventional retrieval-based methods. This work represents a significant advance in medical test recommendation by introducing medical reasoning capabilities into the traditional tree-based retrieval structure.

Micro-video recommendation is attracting global attention and becoming a popular daily service for people of all ages. Recently, Graph Neural Networks-based micro-video recommendation has displayed performance improvement for many kinds of recommendation tasks. However, the existing works fail to fully consider the characteristics of micro-videos, such as the high timeliness of news nature micro-video recommendation and sequential interactions of frequently changed interests. In this paper, a novel Multi-aggregator Time-warping Heterogeneous Graph Neural Network (MTHGNN) is proposed for personalized news nature micro-video recommendation based on sequential sessions, where characteristics of micro-videos are comprehensively studied, users' preference is mined via multi-aggregator, the temporal and dynamic changes of users' preference are captured, and timeliness is considered. Through the comparison with the state-of-the-arts, the experimental results validate the superiority of our MTHGNN model.

It may be a new year, but the hacks, scams, and dangerous people lurking online haven't gone anywhere. Just a day before the ball dropped, the United States Treasury Department said it had been hacked. Officials believe the attackers are an as-yet-unidentified Advanced Persistent Threat group linked to China's government that exploited flaws in remote tech support software made by BeyondTrust to carry out what the Treasury Department described as a "major" breach. The company told the Treasury on December 8 that the attackers stole an authentication key, which ultimately allowed them to access department computers. While the Treasury says the attackers were only able to steal "certain unclassified documents," new details have already begun to emerge, which we'll get into more below.

Back in the day, couples typically met at bars, with those flirty glances eventually progressing into blossoming romances. Others might have been set-up by friends playing Cupid. Nowadays, however, singletons hit the love jackpot by swiping through a conveyor belt of strangers' faces on dating apps. A mesmerising chart today shows how the likes of Tinder and Hinge have killed off the traditional ways lovers used to meet. In the early 1960s, more than a third of couples originally met through friends.

Apple has agreed to pay 95m in cash to settle a proposed class-action lawsuit claiming that its voice-activated assistant Siri violated users' privacy, listening to them without their consent. A preliminary settlement was filed on Tuesday night in the Oakland, California, federal court, and requires approval by US district judge Jeffrey White. Voice assistants typically react when people use "hot words" such as "Hey, Siri". Two plaintiffs said their mentions of Air Jordan sneakers and Olive Garden restaurants triggered ads for those products. Another said he was served ads for a brand name surgical treatment after discussing it, he thought privately, with his doctor.

TL;DR: At SmashLab, we're creating an intelligent assistant that uses the sensors in a smartwatch to support physical tasks such as cooking and DIY. This blog post explores how we use less intrusive scene understanding--compared to cameras--to enable helpful, context-aware interactions for task execution in their daily lives. Every day, we perform many tasks, including cooking, crafting, and medical self-care (like the COVID-19 self-test kit), which involve a series of discrete steps. Accurately executing all the steps can be difficult; when we try a new recipe, for example, we might have questions at any step and might make mistakes by skipping important steps or doing them in the wrong order. This project, Procedural Interaction from Sensing Module (PrISM), aims to support users in executing these kinds of tasks through dialogue-based interactions.