arXiv research

Develops a real-time exercise recommendation system using deep learning.

Paper proposes MACDAE to infer real-time O2O contexts.

This paper proposes a real-time signal plan recommendation system for traffic incidents.

Paper introduces RTT2Vec for real-time grocery recommendations, achieving 9.4% uplift over baselines.

Recently, deep learning models play more and more important roles in contents recommender systems. However, although the performance of recommendations is greatly improved, the "Matthew effect" becomes increasingly evident. While the head contents get more and more popular, many competitive long-tail contents are diffi…

In this paper, we present iPrescribe, a scalable low-latency architecture for recommending 'next-best-offers' in an online setting. The paper presents the design of iPrescribe and compares its performance for implementations using different real-time streaming technology stacks. iPrescribe uses an ensemble of deep lear…

This work explores the ability of collective matrix factorization models in recommender systems to make predictions about users and items for which there is side information available but no feedback or interactions data, and proposes a new formulation with a faster cold-start prediction formula that can be used in rea…

In online advertising, display ads are increasingly being placed based on real-time auctions where the advertiser who wins gets to serve the ad. This is called real-time bidding (RTB). In RTB, auctions have very tight time constraints on the order of 100ms. Therefore mechanisms for bidding intelligently such as clickth…

New system assigns vehicles to routes for cost and energy efficiency.

We present a learning-based system for rapid mass-scale material synthesis that is useful for novice and expert users alike. The user preferences are learned via Gaussian Process Regression and can be easily sampled for new recommendations. Typically, each recommendation takes 40-60 seconds to render with global illumi…

Paper proposes real-time risk metrics for stablecoin protocols.

SAFER improves personalized treatment recommendations for dynamic clinical contexts.

PoDiRe learns long-term rewards in multi-task recommendations.

DVAO predicts volumetric ambient occlusion for real-time volume rendering.

Unified approach for conversational recommendation by integrating attributes and items.

Paper introduces online tensor inference for real-time data analysis.

This work recommends personalized search stories to users based on their interests.

This paper improves recommender systems by handling dynamic user preferences and item popularity.

A new method predicts student skill success rates in real-time.

In this paper, we introduce the notion of motif closure and describe higher-order ranking and link prediction methods based on the notion of closing higher-order network motifs. The methods are fast and efficient for real-time ranking and link prediction-based applications such as web search, online advertising, and re…

Machine learning improves prediction of complex geology ahead of drilling.

In coming years residential consumers will face real-time electricity tariffs with energy prices varying day to day, and effective energy saving will require automation - a recommender system, which learns consumer's preferences from her actions. A consumer chooses a scenario of home appliance use to balance her comfor…

While matrix factorisation models are ubiquitous in large scale recommendation and search, real time application of such models requires inner product computations over an intractably large set of item factors. In this manuscript we present a novel framework that uses the inverted index representation to exploit struct…

LLMs show biases in investment analysis, leading to unreliable recommendations.

Machine learning plays an increasing role in intelligent tutoring systems as both the amount of data available and specialization among students grow. Nowadays, these systems are frequently deployed on mobile applications. Users on such mobile education platforms are dynamic, frequently being added, accessing the appli…

DINOSAUR improves retrieval by accounting for embedding uncertainty in recommender systems.

Proposes a deep RL framework for optimizing task allocation in crowdsourcing platforms.

Paper tackles dynamic assortment with dual contexts, improving revenue in e-commerce.

The study examines how alternative resource adequacy contract designs affect market participants' risk profiles and resource mix.

New RL framework improves real-time control performance.

RL platform enhances user journeys in healthcare apps.

Deviation-based learning improves recommender systems by abstaining from recommending choices users might follow.

This study compares two neural models for financial forecasting, showing their superiority.

Wearables like smartwatches which are embedded with sensors and powerful processors, provide a strong platform for development of analytics solutions in sports domain. To analyze players' games, while motion sensor based shot detection has been extensively studied in sports like Tennis, Golf, Baseball; Table Tennis and…

We create real-time geodesic rendering for non-isotropic geometries.

PRISM provides real-time SLAM with uncertainty estimates for agent and map states.

CAFL breaks feedback loops in recommender systems using causal inference.

Improved real-time UAV terrain following with RVM-RLS filter.

FedCM measures contributions in real-time for federated learning.

DeepFair improves fairness in recommender systems without sacrificing accuracy.

Generative profiling improves real-time task timing for varied resource contexts.

Machine learning models learn what we teach them to learn. Machine learning is at the heart of recommender systems. If a machine learning model is trained on biased data, the resulting recommender system may reflect the biases in its recommendations. Biases arise at different stages in a recommender system, from existi…

The paper aims to define a benchmark for deep learning recommendation models.

Deep learning solutions are being increasingly used in mobile applications. Although there are many open-source software tools for the development of deep learning solutions, there are no guidelines in one place in a unified manner for using these tools towards real-time deployment of these solutions on smartphones. Fr…

Interprets feature interactions in ad-click prediction models.

Interactive recommender systems that enable the interactions between users and the recommender system have attracted increasing research attentions. Previous methods mainly focus on optimizing recommendation accuracy. However, they usually ignore the diversity of the recommendation results, thus usually results in unsa…

Survey on using knowledge graphs for better recommender systems.

Job recommendation has traditionally been treated as a filter-based match or as a recommendation based on the features of jobs and candidates as discrete entities. In this paper, we introduce a methodology where we leverage the progression of job selection by candidates using machine learning. Additionally, our recomme…