Exploring Fine-tuning ChatGPT for News Recommendation

News recommendation systems (RS) play a pivotal role in the current digital age, shaping how individuals access and engage with information. The fusion of natural language processing (NLP) and RS, spurred by the rise of large language models such as the GPT and T5 series, blurs the boundaries between these domains, making a tendency to treat RS as a language task. ChatGPT, renowned for its user-friendly interface and increasing popularity, has become a prominent choice for a wide range of NLP tasks. While previous studies have explored ChatGPT on recommendation tasks, this study breaks new ground by investigating its fine-tuning capability, particularly within the news domain. In this study, we design two distinct prompts: one designed to treat news RS as the ranking task and another tailored for the rating task. We evaluate ChatGPT's performance in news recommendation by eliciting direct responses through the formulation of these two tasks. More importantly, we unravel the pivotal role of fine-tuning data quality in enhancing ChatGPT's personalized recommendation capabilities, and illustrates its potential in addressing the longstanding challenge of the "cold item" problem in RS. Our experiments, conducted using the Microsoft News dataset (MIND), reveal significant improvements achieved by ChatGPT after fine-tuning, especially in scenarios where a user's topic interests remain consistent, treating news RS as a ranking task. This study illuminates the transformative potential of fine-tuning ChatGPT as a means to advance news RS, offering more effective news consumption experiences

Identifying new product ideas : waiting for the wisdom of the crowd or screening ideas in real time

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Dynamics between social media engagement, firm-generated content, and live and time-shifted TV viewing

Purpose The purpose of this paper is to study consumer engagement as a dynamic, iterative process in the context of TV shows. A theoretical framework involving the central constructs of brand actions, customer engagement behaviors (CEBs), and consumption is proposed. Brand actions of TV shows include advertising and firm-generated content (FGC) on social media. CEBs include volume, sentiment, and richness of user-generated content (UGC) on social media. Consumption comprises live and time-shifted TV viewing. Design/methodology/approach The authors study 31 new TV shows introduced in 2015. Consistent with the ecosystem framework, a simultaneous system of equations approach is adopted to analyze data from a US Cable TV provider, Kantar Media, and Twitter. Findings The findings show that advertising efforts initiated by the TV show have a positive effect on time-shifted viewing, but a negative effect on live viewing; tweets posted by the TV show (FGC) have a negative effect on time-shifted viewing, but no effect on live viewing; and negative sentiment from tweets posted by viewers (UGC) reduces time-shifted viewing, but increases live viewing. Originality/value Content creators and TV networks are faced with the daunting challenge of retaining their audiences in a media-fragmented world. Whereas most studies on engagement have focused on static firm-customer relationships, this study examines engagement from a dynamic, multi-agent perspective by studying interrelationships among brand actions, CEBs, and consumption over time. Accordingly, this study can help brands to quantify the effectiveness of their engagement efforts in terms of encouraging CEBs and eliciting specific TV consumption behaviors

Making smart recommendations for perishable and stockout products

Food waste and stockouts are widely recognized as an important global challenge. While inventory management aims to address these challenges, the tools available to inventory managers are often limited and the usefulness of their decisions is dependent on demand realizations, which are not within their control. Recommender systems (RS) can influence and direct customer demand, e.g., by sending personalized emails with promotions for different items. We propose a novel approach that combines the opportunities provided by RS with inventory management considerations. Under the assumption that there is a known set of customers to receive a promotion consisting of items, we use mixed-integer programming (MIP) to allocate recommended items across customers taking both individual preferences and the current state of inventory with uncertainties into account. Our approach can solve problems with both stochastic supply (inventory and perishability) and demand. We propose heuristics to improve scalability and compare their performance with the optimal solution using data from an online grocery retailer. The goal is to target the right set of customers who are likely to purchase an item, while simultaneously considering which items are prone to expire or be out-of-stock soon. We show that creating recommendation lists exclusively considering user preferences can be counterproductive to users due to possible excessive stockouts. Similarly, focusing only on the retailer can be counterproductive to retailer sales due to the number of expired products that can be considered lost income. We thus avoid the loss of customer goodwill due to stockouts and reduce waste by selling inventory before it expires

Visioning a hospitality-oriented patient experience (HOPE) framework in health care

PurposeThis paper considers the question: what would happen if healthcare providers, like their counterparts in the hospitality industry, adopted the principles of customer experience management (CEM) in order to facilitate a more holistic and personalized patient experience? It proposes an alternative vision of the patient experience by adding to an emerging hospitality–healthcare literature base, this time focusing upon CEM. A hospitality-oriented patient experience (HOPE) framework is introduced, designed to enhance the patient experience across all the touchpoints of the healthcare journey.Design/methodology/approachThis is a conceptual paper that draws upon three distinct literatures: hospitality literature; healthcare literature; and CEM literature. It utilizes this literature to develop a framework, the HOPE framework, designed to offer an alternative lens to understanding the patient experience. The paper utilizes descriptions of three unique patient experiences, one linked to chronic pain, a second to gastro issues and a third to orthopedic issues, to illustrate how adopting the principles of hospitality management, within a healthcare context, could promote an enhanced patient experience.FindingsThe main theoretical contribution is the development of the HOPE framework that brings together research on CEM with research on cocreative customer practices in health care. By selecting and connecting key ingredients of two separate research streams, this vision and paradigm provide an alternative lens into ways of addressing the key challenges in the implementation of person-centered care in healthcare services. The HOPE framework offers an actionable roadmap for healthcare organizations to realize greater understanding and to operationalize new ways of improving the patient experience.Originality/valueThis paper applies the principles of hospitality and CEM to the domain of health care. In so doing it adds value to a hospitality literature primarily focused upon extensive employee–customer relationships. To a healthcare literature seeking to more fully understand a person-centered care model typically delivered by a care team consisting of professionals and family/friends. And to a CEM literature in hospitality, which seeks to facilitate favorable employee–customer interactions. Connecting these separate literature streams enables an original conceptual framework, a HOPE framework, to be introduced.</jats:sec