Large-scale "pre-train and prompt learning" paradigms have demonstrated remarkable adaptability, enabling broad applications across diverse domains such as question answering, image recognition, and multimodal retrieval. This approach fully leverages the potential of large-scale pre-trained models, reducing downstream data requirements and computational costs while enhancing model applicability across various tasks. Graphs, as versatile data structures that capture relationships between entities, play pivotal roles in fields such as social network analysis, recommender systems, and biological graphs. Despite the success of pre-train and prompt learning paradigms in Natural Language Processing (NLP) and Computer Vision (CV), their application in graph domains remains nascent. In graph-structured data, not only do the node and edge features often have disparate distributions, but the topological structures also differ significantly. This diversity in graph data can lead to incompatible patterns or gaps between pre-training and fine-tuning on downstream graphs. We aim to bridge this gap by summarizing methods for alleviating these disparities. This includes exploring prompt design methodologies, comparing related techniques, assessing application scenarios and datasets, and identifying unresolved problems and challenges. This survey categorizes over 100 relevant works in this field, summarizing general design principles and the latest applications, including text-attributed graphs, molecules, proteins, and recommendation systems. Through this extensive review, we provide a foundational understanding of graph prompt learning, aiming to impact not only the graph mining community but also the broader Artificial General Intelligence (AGI) community.

Contextual bandits serve as a fundamental algorithmic framework for optimizing recommendation decisions online. Though extensive attention has been paid to tailoring contextual bandits for recommendation applications, the "herding effects" in user feedback have been ignored. These herding effects bias user feedback toward historical ratings, breaking down the assumption of unbiased feedback inherent in contextual bandits. This paper develops a novel variant of the contextual bandit that is tailored to address the feedback bias caused by the herding effects. A user feedback model is formulated to capture this feedback bias. We design the TS-Conf (Thompson Sampling under Conformity) algorithm, which employs posterior sampling to balance the exploration and exploitation tradeoff. We prove an upper bound for the regret of the algorithm, revealing the impact of herding effects on learning speed. Extensive experiments on datasets demonstrate that TS-Conf outperforms four benchmark algorithms. Analysis reveals that TS-Conf effectively mitigates the negative impact of herding effects, resulting in faster learning and improved recommendation accuracy.

Analyzing the sequence of historical interactions between users and items, sequential recommendation models learn user intent and make predictions about the next item of interest. Next to these item interactions, most systems also have interactions with pages not related to specific items, for example navigation pages, account pages, and pages for a specific category, which may provide additional insights into the user's interests. However, while there are several approaches to integrate additional information about items and users, the topic of integrating non-item pages has been less explored. We use the hypotheses testing framework HypTrails to show that there is indeed a relationship between these non-item pages and the items of interest and fill this gap by proposing various approaches of representing non-item pages (e.g, based on their content) to use them as an additional information source for the task of sequential next-item prediction. We create a synthetic dataset with non-item pages highly related to the subsequent item to show that the models are generally capable of learning from these interactions, and subsequently evaluate the improvements gained by including non-item pages in two real-world datasets. We adapt eight popular sequential recommender models, covering CNN-, RNN- and transformer-based architectures, to integrate non-item pages and investigate the capabilities of these models to leverage their information for next item prediction. We also analyze their behavior on noisy data and compare different item representation strategies. Our results show that non-item pages are a valuable source of information, but representing such a page well is the key to successfully leverage them. The inclusion of non-item pages can increase the performance for next-item prediction in all examined model architectures with a varying degree.

It was June 12, Lover's Day in Brazil. Leo Nunes, 24, had spent a few days talking to someone he met on Hornet, a popular gay dating app, before arranging their first encounter in Sao Paulo's middle-class Sacoma neighborhood. A security camera captured the moment that two men on a motorcycle showed up in the alley where he was waiting, grabbed his phone and shot him dead. The Nunes family, who shared details of the investigation with Reuters, said one suspect had been arrested. Sao Paulo police said they are investigating the shooting as a robbery resulting in a homicide, but did not provide further information or confirm if there had been an arrest.

If you wish you could remotely control your electronics but don't want to replace them with smart variants just yet, you have another option: get yourself a few smart plugs. Right now, you can get this 3-pack of TP-Link Tapo smart plugs for just 23 on Amazon, more than half off its usual price. These smart plugs are easy to set up (with Bluetooth onboarding) and they support advanced timers so you can schedule your lamps, fans, Christmas lights, and other electronics to turn on and off. Compatible with the Matter smart home standard, these smart plugs offer seamless integration across all certified smart home platforms, including Apple Home, Amazon Alexa, Google Home, and Samsung SmartThings. Whatever system you use -- or plan to use -- in your home, you'll be able to control your devices with a single app.

No disrespect to Amazon Alexa or Google Assistant, but voice control still has its quirks and sometimes it's easier to control your various smart home devices using your PC instead. But the degree to which you can adjust lighting, view security camera feeds, or set your thermostat will depend on which particular devices and ecosystems you're tied to. Let's walk through some of the different ways you can control smart home systems from a Windows PC, without having to grab your phone or yell out to your nearest voice assistant. Although it doesn't show up in Microsoft Store search results and has seemingly been deprecated, you can still install Amazon's Alexa app for Windows 10 and above if you have the direct store link. Once installed, you can control Alexa-enabled smart devices through the Smart Home tab.

Around 1 in 4 married people in Japan under the age of 40 said they met their partners on a dating app, according to a recent survey on attitudes toward marriage by the children and families agency. The nationwide questionnaire conducted online last month targeted 20,000 people (18,000 unmarried and 2,000 married) between the ages of 15 and 39, and showed that 25.1% of married respondents met their partner on a dating app, beating out those who met their partner through work or part time jobs (20.5%) and at school (9.9%). Among unmarried respondents, 56.3% thought getting married was not important while 42.2% thought having a child was not necessarily important. However, around 60% of unmarried respondents still said they wanted to tie the knot someday, leaving only 20% saying they did not want to get married.

Due to the rapid growth of scientific publications, identifying all related reference articles in the literature has become increasingly challenging yet highly demanding. Existing methods primarily assess candidate publications from a static perspective, focusing on the content of articles and their structural information, such as citation relationships. There is a lack of research regarding how to account for the evolving impact among papers on their embeddings. Toward this goal, this paper introduces a temporal dimension to paper recommendation strategies. The core idea is to continuously update a paper's embedding when new citation relationships appear, enhancing its relevance for future recommendations. Whenever a citation relationship is added to the literature upon the publication of a paper, the embeddings of the two related papers are updated through a Temporal Graph Neural Network (TGN). A learnable memory update module based on a Recurrent Neural Network (RNN) is utilized to study the evolution of the embedding of a paper in order to predict its reference impact in a future timestamp. Such a TGN-based model learns a pattern of how people's views of the paper may evolve, aiming to guide paper recommendations more precisely. Extensive experiments on an open citation network dataset, including 313,278 articles from https://paperswithcode.com/about PaperWithCode, have demonstrated the effectiveness of the proposed approach.

Current artificial intelligence (AI) models often focus on enhancing performance through meticulous parameter tuning and optimization techniques. However, the fundamental design principles behind these models receive comparatively less attention, which can limit our understanding of their potential and constraints. This comprehensive review explores the diverse design inspirations that have shaped modern AI models, i.e., brain-inspired artificial intelligence (BIAI). We present a classification framework that categorizes BIAI approaches into physical structure-inspired and human behavior-inspired models. We also examine the real-world applications where different BIAI models excel, highlighting their practical benefits and deployment challenges. By delving into these areas, we provide new insights and propose future research directions to drive innovation and address current gaps in the field. This review offers researchers and practitioners a comprehensive overview of the BIAI landscape, helping them harness its potential and expedite advancements in AI development.

This work introduces MultiTRON, an approach that adapts Pareto front approximation techniques to multi-objective session-based recommender systems using a transformer neural network. Our approach optimizes trade-offs between key metrics such as click-through and conversion rates by training on sampled preference vectors. A significant advantage is that after training, a single model can access the entire Pareto front, allowing it to be tailored to meet the specific requirements of different stakeholders by adjusting an additional input vector that weights the objectives. We validate the model's performance through extensive offline and online evaluation. For broader application and research, the source code is made available at https://github.com/otto-de/MultiTRON. The results confirm the model's ability to manage multiple recommendation objectives effectively, offering a flexible tool for diverse business needs.