Tootfinder

Examining the sentiment and emotional differences in product and service reviews: The moderating role of culture
Vinh Truong (RMIT University)
https://arxiv.org/abs/2507.21057 h…

Examining the sentiment and emotional differences in product and service reviews: The moderating role of culture
This study explores how emotions and sentiments differ in customer reviews of products and services on e-commerce platforms. Unlike earlier research that treats all reviews uniformly, this study distinguishes between reviews of products, typically fulfilling basic, functional needs, and services, which often cater to experiential and emotional desires. The findings reveal clear differences in emotional expression and sentiment between the two. Product reviews frequently focus on practicality, s…

Follow the user meaningfully and product growth will follow: A mixed methods case study tying UX Point of View & Growth leading to measurable impact
Neha Raghuvanshi
https://arxiv.org/abs/2506.21195

Follow the user meaningfully and product growth will follow: A mixed methods case study tying UX Point of View & Growth leading to measurable impact
Have you wondered how cross-functional teams balance between maximizing value that users derive and business growth leading to win-win situations? This case study shows how User Experience Research (UXR) and Data Science teams used mixed methods research to strategically influence Product Led Growth (PLG) for a Password Manager used by million+ users, thus allowing our users, internal teams, and business to win. The audience will take away practical lessons/techniques related to leveraging mixe…

AI, AGI, and learning efficiency
An addendum to this: I'm someone who would accurately be called "anti-AI" in the modern age, yet I'm also an "AI researcher" in some ways (have only dabbled in neutral nets).
I don't like:
- AI systems that are the product of labor abuses towards the data workers who curate their training corpora.
- AI systems that use inordinate amounts of water and energy during an intensifying climate catastrophe.
- AI systems that are fundamentally untrustworthy and which reinforce and amplify human biases, *especially* when those systems are exposed in a way that invites harms.
- AI systems which are designed to "save" my attention or brain bandwidth but such my doing so cripple my understating of the things I might use them for when I fact that understanding was the thing I was supposed to be using my time to gain, and where the later lack of such understanding will be costly to me.
- AI systems that are designed by and whose hype fattens the purse of people who materially support genocide and the construction of concentration campus (a.k.a. fascists).
In other words, I do not like and except in very extenuating circumstances I will not use ChatGPT, Claude, Copilot, Gemini, etc.
On the other hand, I do like:
- AI research as an endeavor to discover new technologies.
- Generative AI as a research topic using a spectrum of different methods.
- Speculating about non-human intelligences, including artificial ones, and including how to behave ethically towards them.
- Large language models as a specific technique, and autoencoders and other neural networks, assuming they're used responsibly in terms of both resource costs & presentation to end users.
I write this because I think some people (especially folks without CS backgrounds) may feel that opposing AI for all the harms it's causing runs the risk of opposing technological innovation more broadly, and/or may feel there's a risk that they will be "left behind" as everyone else embraces the hype and these technologies inevitability become ubiquitous and essential (I know I feel this way sometimes). Just know that is entirely possible and logically consistent to both oppose many forms of modern AI while also embracing and even being optimistic about AI research, and that while LLMs are currently all the rage, they're not the endpoint of what AI will look like in the future, and their downsides are not inherent in AI development.

I am triply cautious of this article from @…:
- OpenAI et al use the supposed danger (and thus implied power) of their own product as a marketing ploy (as the article points out)
- When a product vendor funds their own research about the potential dangers of their product, it’s more likely to be good PR than good research
- Society always engages in moral panics about new things causing addiction and psychological damage (including bicycles and novels!)
With those caveats in mind, I do think this is an issue worth watching closely. And that quote in the post? Chef’s kiss.
https://mstdn.ca/@dyckron/114796898620291517

Research on E-Commerce Long-Tail Product Recommendation Mechanism Based on Large-Scale Language Models
Qingyi Lu, Haotian Lyu, Jiayun Zheng, Yang Wang, Li Zhang, Chengrui Zhou
https://arxiv.org/abs/2506.06336

Research on E-Commerce Long-Tail Product Recommendation Mechanism Based on Large-Scale Language Models
As e-commerce platforms expand their product catalogs, accurately recommending long-tail items becomes increasingly important for enhancing both user experience and platform revenue. A key challenge is the long-tail problem, where extreme data sparsity and cold-start issues limit the performance of traditional recommendation methods. To address this, we propose a novel long-tail product recommendation mechanism that integrates product text descriptions and user behavior sequences using a large-…

Case Study for Developing a UXR Point of View for FinOps Product Innovation
Jason Dong, Anna Wu
https://arxiv.org/abs/2506.15314 https://

Case Study for Developing a UXR Point of View for FinOps Product Innovation
In the dynamic landscape of Cloud financial management, we are sharing a case study exploring the development of a User Experience Research (UXR) Point of View (PoV) to drive FinOps product innovation. We demonstrate how qualitative and quantitative research methods working together to navigate the challenges of understanding customer needs, aligning cross-functional teams, and prioritizing limited resources. Through a multi-phased research approach, the research team identifies opportunities, …

A look at DeepSeek's impact on the AI model race and market share landscape, roughly 150 days after DeepSeek R1 shook stock markets and the Western AI world (SemiAnalysis)
https://semianalysis.com/2025/07/03/deepseek-debrief-128-days-later/

DeepSeek Debrief: >128 Days Later 
SemiAnalysis is hiring an analyst in New York City for Core Research, our world class research product for the finance industry. Please apply here It’s been a bit over 150 days since the launc…

One of the comments lodged in my mind from 50-some years ago was that the toxicology studies we were basing product approvals on failed to consider interactions between multiple substances - and apparently not much has been done in that area
https://www.

One of the comments lodged in my mind from 50-some years ago was that the toxicology studies we were basing product approvals on failed to consider interactions between multiple substances - and apparently not much has been done in that area
https://www.

The USA domestic sugar production is already short, with about 25% of the sugar consumed being imported. If Coke actually does change from HFCS to sugar, doesn't that mean replacing a domestic product with new imports?
source: https://www.ers.usda.gov/topics/crops/suga

One way to ensure I don't buy an item is to market it "as seen on Shark Tank" -- but that's probably just me. Another way to ensure I don't buy an item is to market it as having AI features (??), and apparently that's widespread.
https://futurism.com/customers-see-ai-…

Something Hilarious Happens When Potential Customers See That a Product Has AI Features
New research explores how customers react to products that are marketed as having AI — with striking results.

UXR Point of View on Product Feature Prioritization Prior To Multi-Million Engineering Commitments
Jonas Lau, Annie Tran
https://arxiv.org/abs/2506.15294 h…

UXR Point of View on Product Feature Prioritization Prior To Multi-Million Engineering Commitments
This paper discusses a popular UX research activity, feature prioritization, using the User Experience Research Point of View (UXR PoV) Playbook framework. We describe an application of multinomial logistic regression, frequently marketed as MaxDiff, for prioritizing product features in consumer product development. It addresses challenges of traditional surveying techniques. We propose a solution using MaxDiff to generate a reliable preference list with a reasonable sample size. We also adapt …

Computations Meet Experiments to Advance the Enzymatic Depolymerization of Plastics One Atom at a Time
Francesco Colizzi, Paula Bl\'azquez-S\'anchez, Giovanni Bussi, Isabelle Andr\'e, Federico Ballabio, Thomas Bayer, Federica Bertocchini, Erik Butensch\"on, Emanuele Carosati, Alessia De Piero, Ania Di Pede-Mattatelli, Leonardo Faggian, Lorenzo Favaro, Pedro Alexandrino Fernandes, Alfonso Ferretti, Peter Fojan, Andreas Gagsteiger, Eva Garc\'ia-Ruiz, Lucia Gardossi, …

Computations Meet Experiments to Advance the Enzymatic Depolymerization of Plastics One Atom at a Time
Plastics are essential to modern life, yet poor disposal practices contribute to low recycling rates and environmental accumulation-biological degradation and by-product reuse offer a path to mitigate this global threat. This report highlights key insights, future challenges, and research priorities identified during the CECAM workshop "Computations Meet Experiments to Advance the Enzymatic Depolymerization of Plastics One Atom at a Time", held in Trieste from May 6-8, 2025. The workshop brough…

This https://arxiv.org/abs/2506.04929 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT
Neural Machine Translation (NMT) has improved translation by using Transformer-based models, but it still struggles with word ambiguity and context. This problem is especially important in domain-specific applications, which often have problems with unclear sentences or poor data quality. Our research explores how adding information to models can improve translations in the context of e-commerce data. To this end we create ConECT -- a new Czech-to-Polish e-commerce product translation dataset c…

Liquidity provision with $\tau$-reset strategies: a dynamic historical liquidity approach
Andrey Urusov, Rostislav Berezovskiy, Anatoly Krestenko, Andrei Kornilov
https://arxiv.org/abs/2505.15338

Liquidity provision with $τ$-reset strategies: a dynamic historical liquidity approach
Since the launch of Uniswap and other AMM protocols, the DeFi industry has evolved from simple constant product functions with uniform liquidity distribution across the entire price axis to more advanced mechanisms that allow Liquidity Providers (LPs) to concentrate capital within selected price ranges. This evolution has introduced new research challenges focused on optimizing capital allocation in Decentralized Exchanges (DEXs) under dynamic market conditions. In this paper, we present a meth…

This https://arxiv.org/abs/2506.05376 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

A Red Teaming Roadmap Towards System-Level Safety
Large Language Model (LLM) safeguards, which implement request refusals, have become a widely adopted mitigation strategy against misuse. At the intersection of adversarial machine learning and AI safety, safeguard red teaming has effectively identified critical vulnerabilities in state-of-the-art refusal-trained LLMs. However, in our view the many conference submissions on LLM red teaming do not, in aggregate, prioritize the right research problems. First, testing against clear product safety …

Digital Twin-based Smart Manufacturing: Dynamic Line Reconfiguration for Disturbance Handling
Bo Fu, Mingjie Bi, Shota Umeda, Takahiro Nakano, Youichi Nonaka, Quan Zhou, Takaharu Matsui, Dawn M. Tilbury, Kira Barton
https://arxiv.org/abs/2506.07332

Digital Twin-based Smart Manufacturing: Dynamic Line Reconfiguration for Disturbance Handling
The increasing complexity of modern manufacturing, coupled with demand fluctuation, supply chain uncertainties, and product customization, underscores the need for manufacturing systems that can flexibly update their configurations and swiftly adapt to disturbances. However, current research falls short in providing a holistic reconfigurable manufacturing framework that seamlessly monitors system disturbances, optimizes alternative line configurations based on machine capabilities, and automate…

DeepShop: A Benchmark for Deep Research Shopping Agents
Yougang Lyu, Xiaoyu Zhang, Lingyong Yan, Maarten de Rijke, Zhaochun Ren, Xiuying Chen
https://arxiv.org/abs/2506.02839

DeepShop: A Benchmark for Deep Research Shopping Agents
Web agents for online shopping have shown great promise in automating user interactions across e-commerce platforms. Benchmarks for assessing such agents do not reflect the complexity of real-world shopping scenarios, as they often consist of overly simple queries with deterministic paths, such as "Find iPhone 15." Real shopping scenarios are inherently more layered, involving multi-dimensional product attributes, search filters, and user-specific sorting preferences. To address this gap, we in…