On globally solving nonconvex trust region subproblem via projected gradient method

The trust region subproblem (TRS) is to minimize a possibly nonconvex quadratic function over a Euclidean ball. There are typically two cases for (TRS), the so-called ``easy case'' and ``hard case''. Even in the ``easy case'', the sequence generated by the classical projected gradient method (PG) may converge to a saddle point at a sublinear local rate, when the initial point is arbitrarily selected from a nonzero measure feasible set. To our surprise, when applying (PG) to solve a cheap and possibly nonconvex reformulation of (TRS), the generated sequence initialized with {\it any} feasible point almost always converges to its global minimizer. The local convergence rate is at least linear for the ``easy case'', without assuming that we have possessed the information that the ``easy case'' holds. We also consider how to use (PG) to globally solve equality-constrained (TRS).Comment: 19 pages, 3 figure

Using Platform-Generated Content to Stimulate User-Generated Content

This work intends to study the implication of an editorial review program where a review platform starts to supplement the user-generated reviews on its website with editorial review articles that are written by the platform. Our research question is whether platform-generated content (i.e., editorial reviews) influence subsequent user- generated content (i.e., online reviews) both in terms of the quantity and quality of those reviews. We obtain the dataset through a partnership with a restaurant review platform in Asia. Our preliminary analysis suggests that platform-generated content has a positive net effect on subsequent user-generated content. Specifically, users post more reviews for restaurants that have editorial reviews and these reviews tend to be longer on average

When Reward Meets Donation: A Paradoxical Dilemma

Reward-based crowdfunding platforms are increasingly incorporating donation options, allowing backers to financially support campaigns without receiving any tangible rewards in return. Although this option seems to create a novel fundraising channel, our quasi-natural experimental study highlights the potential negative impacts of individual donation occurrences, which ultimately lead to reduced total raised funds, as substantiated by robust empirical evidence. We explore two primary mechanisms responsible for the adverse effect. First, the bystander effect, where prior donations discourage potential backers from supporting the campaign, causing them to either forgo reward purchases or decrease their contribution amounts. Second, the social conformity effect, in which prior donations shape backers\u27 perceptions of social norms and consequently lower their support levels. By offering a comprehensive understanding of behavioral dynamics in crowdfunding, our study enriches the literature on the design and management of crowdfunding platforms and provides valuable insights for industry practitioners

Optimizing Two Sided Promotion for IS Enabled Transportation Network: A Conditional Bayesian Learning Model

This paper investigates whether taxi apps provide attribute value for taxi driver, and how two-sided sales promotion interacted with consumer learning about attribute value to influence taxi drivers’ decision of adoption of taxi app. We propose a conditional Bayesian learning model to allow learning about multiple attributes. We find the evidence of taxi driver’s learning about attribute of app, transaction successful rate and the probability of earning cash back from app provider. We also find measurable evidence that sales promotion during product introduction has indirect effect through learning

ChatGPT Is A User-Generated Knowledge-Sharing Killer

Large Language Models (LLMs), e.g., ChatGPT, is expected to reshape a broad spectrum of domains. This study examines the impact of ChatGPT on question aksing in Q&A communitits via the natural experiment. Safe-guided by supporting evidence of parallel trends, a difference-in-difference (DID) analysis suggests the launching trigger an average 2.6% reduction of question-asking on Stack Overflow, confirming a lower-search-cost-enabled substitution. Our further analysis suggests that, this substitution effect has resulted in more longer, less readable and less cognitive and hence more sophisticated questions on average. Finally, the insignificant change in the score given by viewers per question suggests no improvement in the question quality and decreased platform-wide engagement. Our moderation analysis further ascertain the types of individuals who are more susceptible to ChatGPT. Taken together, our paper suggests LLMs may threaten the survival of user-generated knowledge-sharing communities, which may further threaten the sustainable learning and long-run improvement of LLMs

Ensembled CTR Prediction via Knowledge Distillation

Recently, deep learning-based models have been widely studied for click-through rate (CTR) prediction and lead to improved prediction accuracy in many industrial applications. However, current research focuses primarily on building complex network architectures to better capture sophisticated feature interactions and dynamic user behaviors. The increased model complexity may slow down online inference and hinder its adoption in real-time applications. Instead, our work targets at a new model training strategy based on knowledge distillation (KD). KD is a teacher-student learning framework to transfer knowledge learned from a teacher model to a student model. The KD strategy not only allows us to simplify the student model as a vanilla DNN model but also achieves significant accuracy improvements over the state-of-the-art teacher models. The benefits thus motivate us to further explore the use of a powerful ensemble of teachers for more accurate student model training. We also propose some novel techniques to facilitate ensembled CTR prediction, including teacher gating and early stopping by distillation loss. We conduct comprehensive experiments against 12 existing models and across three industrial datasets. Both offline and online A/B testing results show the effectiveness of our KD-based training strategy.Comment: Published in CIKM'202