A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Adversarial validation detects concept drift in user targeting systems.
AI2V learns user representations by focusing on recent interests.
A new method for precise user targeting in advertising using hyperbolic manifold learning.
Real-time personalization for HAR models learns from new users without prior data.
Boosting trees predict Twitch subscriptions from user activity.
Method optimizes diffusion model generation to meet user preferences.
CnGAN generates synthetic user preferences for non-overlapped users in cross-network recommender systems.
The paper develops methods to predict the probability of achieving a user goal in a task, ensuring the system alerts when the probability falls below a threshold.
Improved multi-user VoiceFilter-Lite model for speech recognition.
A new method improves recommendation accuracy by learning from multiple networks and time-dependent user preferences.
Study optimal bidding strategies for digital ads targeting purchases and health campaigns.
Data poisoning attacks can manipulate recommender systems to recommend target items.
Predicts user age and gender on Tumblr using rich content.
Defense against user shilling attacks in collaborative filtering using edge reweighting.
A new method for recommending groups and activities based on geo-social data.
In the age of data driven solution, the customer demographic attributes, such as gender and age, play a core role that may enable companies to enhance the offers of their services and target the right customer in the right time and place. In the marketing campaign, the companies want to target the real user of the GSM …
Owners of a web-site are often interested in analysis of groups of users of their site. Information on these groups can help optimizing the structure and contents of the site. In this paper we use an approach based on formal concepts for constructing taxonomies of user groups. For decreasing the huge amount of concepts…
Bayesian model improves BCI performance for ALS users.
Online purchase decisions in organizations can go through a complex journey with multiple agents involved in the decision making process. Depending on the product being purchased, and the organizational structure, the process may involve employees who first conduct market research, and then influence decision makers wh…
Can machine learning models for recommendation be easily fooled? While the question has been answered for hand-engineered fake user profiles, it has not been explored for machine learned adversarial attacks. This paper attempts to close this gap. We propose a framework for generating fake user profiles which, when inco…
Human behavior expression and experience are inherently multi-modal, and characterized by vast individual and contextual heterogeneity. To achieve meaningful human-computer and human-robot interactions, multi-modal models of the users states (e.g., engagement) are therefore needed. Most of the existing works that try t…
We consider the problem of personalization of online services from the viewpoint of ad targeting, where we seek to find the best ad categories to be shown to each user, resulting in improved user experience and increased advertisers' revenue. We propose to address this problem as a task of ranking the ad categories dep…
We consider the problem of search through comparisons, where a user is presented with two candidate objects and reveals which is closer to her intended target. We study adaptive strategies for finding the target, that require knowledge of rank relationships but not actual distances between objects. We propose a new str…
Bayesian nonparametric model predicts user activity and intervention success.
New approach improves cross-domain recommendation for sparse target domains.
A reciprocal recommendation problem is one where the goal of learning is not just to predict a user's preference towards a passive item (e.g., a book), but to recommend the targeted user on one side another user from the other side such that a mutual interest between the two exists. The problem thus is sharply differen…
Although modern recommendation systems can exploit the structure in users' item feedback, most are powerless in the face of new users who provide no structure for them to exploit. In this paper we introduce ImplicitCE, an algorithm for recommending items to new users during their sign-up flow. ImplicitCE works by trans…
The size of a website's active user base directly affects its value. Thus, it is important to monitor and influence a user's likelihood to return to a site. Essential to this is predicting when a user will return. Current state of the art approaches to solve this problem come in two flavors: (1) Recurrent Neural Networ…
ViTaX provides formal guarantees for targeted explanations in safety-critical systems.
Recommender system is an important component of many web services to help users locate items that match their interests. Several studies showed that recommender systems are vulnerable to poisoning attacks, in which an attacker injects fake data to a given system such that the system makes recommendations as the attacke…
The problem of content search through comparisons has recently received considerable attention. In short, a user searching for a target object navigates through a database in the following manner: the user is asked to select the object most similar to her target from a small list of objects. A new object list is then p…
SCM-GAN converts any song to sound like a different singer.
ML4Chem offers a user-friendly platform for developing and deploying machine learning models in chemistry.
Semi-supervised method enhances recommendation from sparse data.
We quantify content availability and user discovery opportunities in recommender systems.
User intended actions are widely seen in many areas. Forecasting these actions and taking proactive measures to optimize business outcome is a crucial step towards sustaining the steady business growth. In this work, we focus on pre- dicting attrition, which is one of typical user intended actions. Conventional attriti…
Smart app tracks relapse history and predicts relapse based on spatial-temporal factors.
Proposes privacy-preserving sensor data transformations to prevent user re-identification and sensitive activity inference.
User behavior data in recommender systems are driven by the complex interactions of many latent factors behind the users' decision making processes. The factors are highly entangled, and may range from high-level ones that govern user intentions, to low-level ones that characterize a user's preference when executing an…
The paper tackles the issue of preferential attachment in targeted display advertising by developing domain-adaptation approaches.
Click-through rate (CTR) prediction is a critical task in online advertising systems. A large body of research considers each ad independently, but ignores its relationship to other ads that may impact the CTR. In this paper, we investigate various types of auxiliary ads for improving the CTR prediction of the target a…
The paper tackles biases in session-based recommender systems by modeling user interest as a stochastic process.
Understanding and interpreting how machine learning (ML) models make decisions have been a big challenge. While recent research has proposed various technical approaches to provide some clues as to how an ML model makes individual predictions, they cannot provide users with an ability to inspect a model as a complete e…
New framework optimizes for 'value' rather than engagement.
Improved image synthesis with user scribbles and text prompts.
Taobao, as the largest online retail platform in the world, provides billions of online display advertising impressions for millions of advertisers every day. For commercial purposes, the advertisers bid for specific spots and target crowds to compete for business traffic. The platform chooses the most suitable ads to …
Develops a measure for subjective explainability of ML predictions.
With the increasing variety of services that e-commerce platforms provide, criteria for evaluating their success become also increasingly multi-targeting. This work introduces a multi-target optimization framework with Bayesian modeling of the target events, called Deep Bayesian Multi-Target Learning (DBMTL). In this f…