arXiv research

Proposes CVRCF for streaming recommender systems combining deep learning and probabilistic models.

iPrescribe offers fast online offer recommendations using deep learning.

The paper tackles carousel personalization in music streaming apps using contextual bandits.

A new recommendation system model tackles unreliable user behavior.

New method for evaluating sequential recommendations with lower variance.

The paper examines the consistency of item embeddings in recommendation systems.

New metrics needed for streaming ML due to delayed labels.

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

New model for music streaming recommends songs based on past play history.

The area of online machine learning in big data streams covers algorithms that are (1) distributed and (2) work from data streams with only a limited possibility to store past data. The first requirement mostly concerns software architectures and efficient algorithms. The second one also imposes nontrivial theoretical …

Streaming tensor factorization is a powerful tool for processing high-volume and multi-way temporal data in Internet networks, recommender systems and image/video data analysis. Existing streaming tensor factorization algorithms rely on least-squares data fitting and they do not possess a mechanism for tensor rank dete…

Proposes efficient user cold start recommendation via meta parameter partition.

Collaborative filtering (CF) has been successfully employed by many modern recommender systems. Conventional CF-based methods use the user-item interaction data as the sole information source to recommend items to users. However, CF-based methods are known for suffering from cold start problems and data sparsity proble…

Proposes a multi-stream RNN model for predicting merchant transactions.

New robustness certificates for streaming models with a sliding window.

Paper presents a reinforcement learning framework for personalized music playlist generation.

Recommending appropriate items to users is crucial in many e-commerce platforms that contain implicit data as users' browsing, purchasing and streaming history. One common approach consists in selecting the N most relevant items to each user, for a given N, which is called top-N recommendation. To do so, recommender sy…

Learning from data streams is an increasingly important topic in data mining, machine learning, and artificial intelligence in general. A major focus in the data stream literature is on designing methods that can deal with concept drift, a challenge where the generating distribution changes over time. A general assumpt…

Paper addresses challenges in benchmarking stream learning algorithms with real-world data.

Improved item recommendations for repeat interactions using sequence analysis.

OTT services are replacing traditional telecom services, affecting revenue streams.

Improved action detection for multi-person videos using attention filtering.

Proposes DTS framework to predict CTR by tracking user interest evolution over time.

CHAMELEON tackles news recommendation using deep learning, addressing cold-start issues.

An important metric of users' satisfaction and engagement within on-line streaming services is the user session length, i.e. the amount of time they spend on a service continuously without interruption. Being able to predict this value directly benefits the recommendation and ad pacing contexts in music and video strea…

Many tasks in machine learning and data mining, such as data diversification, non-parametric learning, kernel machines, clustering etc., require extracting a small but representative summary from a massive dataset. Often, such problems can be posed as maximizing a submodular set function subject to a cardinality constr…

Poisson factorization is a probabilistic model of users and items for recommendation systems, where the so-called implicit consumer data is modeled by a factorized Poisson distribution. There are many variants of Poisson factorization methods who show state-of-the-art performance on real-world recommendation tasks. How…

A new reinforcement learning framework for multi-reward processing.

Financial markets are notoriously complex environments, presenting vast amounts of noisy, yet potentially informative data. We consider the problem of forecasting financial time series from a wide range of information sources using online Gaussian Processes with Automatic Relevance Determination (ARD) kernels. We measu…

Neural nets classify Thai Lukthung songs from other genres.

A new method detects concept drift in streaming data using k-means space partitioning.

Selective reinitialization improves adaptability of neural bandits in dynamic environments.

Hop Sampling improves GNNs in non-stationary environments by preventing overfitting.

Probabilistic hash embeddings improve online learning of categorical features.

We consider a continuous time Principal-Agent model on a finite time horizon, where we look for the existence of an optimal contract both parties agreed on. Contrary to the main stream, where the principal is modelled as risk-neutral, we assume that both the principal and the agent have exponential utility, and are ris…

A new method classifies multiple correlated data streams simultaneously.

A new method for estimating SW from streaming data.

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…

A new algorithm uses bandits to diversify database activity monitoring.

Data stream clustering tackles real-time data processing challenges.

Bayesian tensor train method recovers streaming data with high accuracy.

stream-learn is a Python library for analyzing data streams with various drift types.

New method rebalances evolving data streams incrementally.

Building behavior profiles of Android applications (apps) with holistic, rich and multi-view information (e.g., incorporating several semantic views of an app such as API sequences, system calls, etc.) would help catering downstream analytics tasks such as app categorization, recommendation and malware analysis signifi…

PySAD offers a unified Python framework for efficient streaming anomaly detection.

A new method detects anomalies in multivariate streams without unit dependence.

New estimator uses clustering to improve off-policy evaluation accuracy.

Context improves one-class classifiers in dynamic data streams.