Tootfinder

Forbes reshuffles its leadership team and forms an AI & Strategic Platforms Group to identify future revenue streams and grow its brand across social platforms (Chris Roush/Talking Biz News)
https://talkingbiznews.com/media-news/forbes-shuffles-le…

Forbes shuffles leadership, aiming for more growth - Talking Biz News
Forbes announced changes to its leadership on Monday, looking to boost its future growth and artificial intelligence opportunities. Nina Gould was promoted to chief innovation officer, overseeing Forbes’ technology and product businesses. She will oversee Forbes’ technology, product and product design, data and business intelligence, audience growth and development, accolades and eCommerce businesses and brand […]

Sources: Meta plans its fourth AI restructuring in six months, dividing Superintelligence Labs into TBD Lab, a product team, an infrastructure team, and FAIR (Kalley Huang/The Information)
https://www.theinformation.com/articles/meta-plans-fourth…

Meta Plans Fourth Restructuring of AI Efforts in Six Months
Meta Platforms is planning the fourth restructuring of its artificial intelligence efforts in six months, according to three people familiar with the matter. Meta’s new AI organization, Meta Superintelligence Labs, is expected to be divided into four groups: a new lab that has been going by TBD ...

Algorithmic Collusion of Pricing and Advertising on E-commerce Platforms
Hangcheng Zhao, Ron Berman
https://arxiv.org/abs/2508.08325 https://arxiv.org/pdf/…

Algorithmic Collusion of Pricing and Advertising on E-commerce Platforms
Online sellers have been adopting AI learning algorithms to automatically make product pricing and advertising decisions on e-commerce platforms. When sellers compete using such algorithms, one concern is that of tacit collusion - the algorithms learn to coordinate on higher than competitive. We empirically investigate whether these concerns are valid when sellers make pricing and advertising decisions together, i.e., two-dimensional decisions. Our empirical strategy is to analyze competition w…

Real-time and personalized product recommendations for large e-commerce platforms
Matteo Tolloso, Davide Bacciu, Shahab Mokarizadeh, Marco Varesi
https://arxiv.org/abs/2506.21368 …

Real-time and personalized product recommendations for large e-commerce platforms
We present a methodology to provide real-time and personalized product recommendations for large e-commerce platforms, specifically focusing on fashion retail. Our approach aims to achieve accurate and scalable recommendations with minimal response times, ensuring user satisfaction, leveraging Graph Neural Networks and parsimonious learning methodologies. Extensive experimentation with datasets from one of the largest e-commerce platforms demonstrates the effectiveness of our approach in foreca…

When you get the option to `Sign in with Google/Microsoft/Facebook` you're really using #OAuth. Aside from those platforms knowing what you're doing everywhere all the time, there are compelling reasons for both 3rd party services and users. (not many, but a few).
But if you DO link your #Microsoft /

Poor Software Product Management Chronicles: E-Auth, I-Auth, OAuth, Fuck Off
From the This Is Why We Can't Have Nice Things department. When you are registering or logging into an online or app service and they ...

Distributed Quantum Inner Product Estimation with Low-Depth Circuits
Congcong Zheng, Kun Wang, Xutao Yu, Ping Xu, Zaichen Zhang
https://arxiv.org/abs/2506.19574

Distributed Quantum Inner Product Estimation with Low-Depth Circuits
Distributed inner product estimation (DIPE) is a fundamental task in quantum information, aiming to estimate the inner product between two unknown quantum states prepared on distributed quantum platforms. Existing rigorous sample complexity analyses are limited to unitary $4$-designs, which present practical challenges for near-term quantum devices. This work addresses this challenge by exploring DIPE with low-depth quantum circuits. We first establish that DIPE with an arbitrary unitary $2$-de…

AI based Content Creation and Product Recommendation Applications in E-commerce: An Ethical overview
Aditi Madhusudan Jain, Ayush Jain
https://arxiv.org/abs/2506.17370

AI based Content Creation and Product Recommendation Applications in E-commerce: An Ethical overview
As e-commerce rapidly integrates artificial intelligence for content creation and product recommendations, these technologies offer significant benefits in personalization and efficiency. AI-driven systems automate product descriptions, generate dynamic advertisements, and deliver tailored recommendations based on consumer behavior, as seen in major platforms like Amazon and Shopify. However, the widespread use of AI in e-commerce raises crucial ethical challenges, particularly around data priv…

EcomMMMU: Strategic Utilization of Visuals for Robust Multimodal E-Commerce Models
Xinyi Ling, Hanwen Du, Zhihui Zhu, Xia Ning
https://arxiv.org/abs/2508.15721 https://

EcomMMMU: Strategic Utilization of Visuals for Robust Multimodal E-Commerce Models
E-commerce platforms are rich in multimodal data, featuring a variety of images that depict product details. However, this raises an important question: do these images always enhance product understanding, or can they sometimes introduce redundancy or degrade performance? Existing datasets are limited in both scale and design, making it difficult to systematically examine this question. To this end, we introduce EcomMMMU, an e-commerce multimodal multitask understanding dataset with 406,190 sa…

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…

Auctions Meet Bandits: An Empirical Analysis
Mohammad Rashid, Omid Rafieian, Soheil Ghili
https://arxiv.org/abs/2508.21162 https://arxiv.org/pdf/2508.21162…

Auctions Meet Bandits: An Empirical Analysis
Sponsored search positions are typically allocated through real-time auctions, where the outcomes depend on advertisers' quality-adjusted bids - the product of their bids and quality scores. Although quality scoring helps promote ads with higher conversion outcomes, setting these scores for new advertisers in any given market is challenging, leading to the cold-start problem. To address this, platforms incorporate multi-armed bandit algorithms in auctions to balance exploration and exploitation…

VL-CLIP: Enhancing Multimodal Recommendations via Visual Grounding and LLM-Augmented CLIP Embeddings
Ramin Giahi, Kehui Yao, Sriram Kollipara, Kai Zhao, Vahid Mirjalili, Jianpeng Xu, Topojoy Biswas, Evren Korpeoglu, Kannan Achan
https://arxiv.org/abs/2507.17080

VL-CLIP: Enhancing Multimodal Recommendations via Visual Grounding and LLM-Augmented CLIP Embeddings
Multimodal learning plays a critical role in e-commerce recommendation platforms today, enabling accurate recommendations and product understanding. However, existing vision-language models, such as CLIP, face key challenges in e-commerce recommendation systems: 1) Weak object-level alignment, where global image embeddings fail to capture fine-grained product attributes, leading to suboptimal retrieval performance; 2) Ambiguous textual representations, where product descriptions often lack cont…