Search Results for « Online Shaping »

Q-shaping is an extension of Q-value initialization and serves as an alternative to reward shaping for incorporating domain knowledge to accelerate agent training, thereby improving sample efficiency by directly shaping Q-values. This approach is both general and robust across diverse tasks, allowing for immediate impact assessment while guaranteeing optimality. We evaluated Q-shaping across 20 different environments using a large language model (LLM) as the heuristic provider. The results demonstrate that Q-shaping significantly enhances sample efficiency, achieving a \textbf{16.87\%} improvement over the best baseline in each environment and a \textbf{253.80\%} improvement compared to LLM-based reward shaping methods. These findings establish Q-shaping as a superior and unbiased alternative to conventional reward shaping in reinforcement learning.

This chapter explores the construction of authenticity by social media influencers. Understood to include attributes and practices such as sincerity, trustworthiness, accuracy, originality, spontaneity, and visibility, influencer authenticity is complex. We argue that the perceived authenticity of influencers can create virtual communities through shared identities and experiences, has the power to impact followers’ actions such as purchases and beliefs, and is often contradictory as influencers navigate their opposing roles as both proponents for consumption and facilitators of social connection. Further, we discuss the ways in which ideas about authenticity are central to the digital labor that influencers perform, and that authenticity, as it is enacted, possesses a complex ethics. We conclude with a discussion of how social media authenticity is further complicated in the wake of advancements in generative artificial intelligence that produce cultural content whose veracity is difficult to determine.

Wikipedia and many User-Generated Content (UGC) communities are known for producing reliable, quality content, but also for being vulnerable to false or misleading information. Previous work has shown that many hoaxes on Wikipedia go undetected for extended periods of time. But little is known about the creation of intentionally false or misleading information online. Does collective attention toward a topic increase the likelihood it will spawn disinformation? Here, we measure the relationship between allocation of attention and the production of hoax articles on the English Wikipedia. Analysis of traffic logs reveals that, compared to legitimate articles created on the same day, hoaxes tend to be more associated with traffic spikes preceding their creation. This is consistent with the idea that the supply of false or misleading information on a topic is driven by the attention it receives. These findings improve our comprehension of the determinants of disinformation in UGC communities and could help promote the integrity of knowledge on Wikipedia.

A classical problem in power systems is to allocate in-coming (elastic or inelastic) demands without violating the operating constraints of electric networks in an online fashion. Although online decision problems have been well-studied in the literature, a unique challenge arising in power systems is the presence of non-linear constraints, a departure from the traditional settings. A particular example is the capacity constraint of apparent power, which gives rise to a quadratic constraint, rather than typical linear constraints. In this paper, we present a competitive randomized online algorithm for deciding whether a sequence of inelastic demands can be allocated for the requested intervals, subject to the total satisfiable apparent power within a time-varying capacity constraint. We also consider an alternative setting with nodal voltage constraint, using a variant of the online algorithm. Finally, simulation studies are provided to evaluate the algorithms empirically.

Decentralized Online Social Networks (DOSNs) represent a growing trend in the social media landscape, as opposed to the well-known centralized peers, which are often in the spotlight due to privacy concerns and a vision typically focused on monetization through user relationships. By exploiting open-source software, DOSNs allow users to create their own servers, or instances, thus favoring the proliferation of platforms that are independent yet interconnected with each other in a transparent way. Nonetheless, the resulting cooperation model, commonly known as the Fediverse, still represents a world to be fully discovered, since existing studies have mainly focused on a limited number of structural aspects of interest in DOSNs. In this work, we aim to fill a lack of study on user relations and roles in DOSNs, by taking two main actions: understanding the impact of decentralization on how users relate to each other within their membership instance and/or across different instances, and unveiling user roles that can explain two interrelated axes of social behavioral phenomena, namely information consumption and boundary spanning. To this purpose, we build our analysis on user networks from Mastodon, since it represents the most widely used DOSN platform. We believe that the findings drawn from our study on Mastodon users' roles and information flow can pave a way for further development of fascinating research on DOSNs.

Consumer purchases are largely shaped by their assessment of a product's benefits, drawbacks, and emotional aspects. Psychological and marketing research suggests that emotions influence customers at various points in the buying process. This research seeks to identify the factors that may affect consumer emotions when buying luxury cosmetics. To examine the different facets of customer emotions, a qualitative study was carried out through in-depth semi-structured interviews with 23 users of high-end cosmetics and health products in Telegram groups. This study revealed various emotional dimensions and identified factors that trigger emotions in the target market. The subsequent phase involved determining the variables that influence customer emotions, based on collective agreement. This phase included a panel of 15 experts in marketing, psychology, luxury cosmetics import businesses on digital platforms, and managers of online luxury cosmetics and hygiene groups. Using a three-step consensus approach, the experts identified and evaluated 36 factors influencing customer emotions based on their importance and perceived impact. These factors were then grouped into three categories: personal variables, group and product variables, and contextual variables.

What if a successful company starts to receive a torrent of low-valued (one or two stars) recommendations in its mobile apps from multiple users within a short (say one month) period of time? Is it legitimate evidence that the apps have lost in quality, or an intentional plan (via lockstep behavior) to steal market share through defamation? In the case of a systematic attack to one's reputation, it might not be possible to manually discern between legitimate and fraudulent interaction within the huge universe of possibilities of user-product recommendation. Previous works have focused on this issue, but none of them took into account the context, modeling, and scale that we consider in this paper. Here, we propose the novel method Online-Recommendation Fraud ExcLuder (ORFEL) to detect defamation and/or illegitimate promotion of online products by using vertex-centric asynchronous parallel processing of bipartite (users-products) graphs. With an innovative algorithm, our results demonstrate both efficacy and efficiency -- over 95% of potential attacks were detected, and ORFEL was at least two orders of magnitude faster than the state-of-the-art. Over a novel methodology, our main contributions are: (1) a new algorithmic solution; (2) one scalable approach; and (3) a novel context and modeling of the problem, which now addresses both defamation and illegitimate promotion. Our work deals with relevant issues of the Web 2.0, potentially augmenting the credibility of online recommendation to prevent losses to both customers and vendors.

Purpose The purpose of this paper is to provide practitioners of management and information technology a sense of how the automobile industry uses the internet to market its products. Design/methodology/approach A review of the applied literature on practices and actual examples of companies' practices (obtained through personal interviews and basic empirical analysis of managers located in Pittsburgh, PA) was conducted. An analysis of data derived from web‐enabled and highly educated professionals from the metropolitan area of Pittsburgh, PA, resulted in 60 interviews from an initial sample of over 155 professionals from three area firms. Techniques reviewed include online advertising, data mining from web sites, other conventional advertising of the company web site and positioning their vehicles among the various search engines. Findings The statistical findings lead to the general conclusion that people are using the internet with greater regularity to gain information about vehicle purchases. However, the vast majority of those surveyed still preferred to ultimately complete the purchase in person. Practical implications The findings show that while dealers are forced to be more competitive in terms of financing and pricing, dealerships are not in danger of being cut from the vehicle purchase model, at least not in the short‐term. Originality/value The internet has proven to be an invaluable tool for consumers who are either considering the purchase of a new car or actually in the process of doing so. In today's market, automotive manufacturers must have a significant market presence to compete and, in many cases, just to survive in a business environment where several of the major automotive manufacturers must find new markets overseas.