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.
As a vast number of ingredients exist in the culinary world, there are countless food ingredient pairings, but only a small number of pairings have been adopted by chefs and studied by food researchers. In this work, we propose KitcheNette which is a model that predicts food ingredient pairing scores and recommends opt…
A recipe recommendation system suggests missing ingredients using collaborative filtering.
Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a systematic, unifying taxonomy to categorize existing methods. We distinguish methods that affect data,…
TS-UCB improves Thompson Sampling with minimal extra computation.
SupRB learns rules for continuous decision problems from examples.
Predicts food ingredient amounts from images.
New framework controls statistical dispersion for high-stakes applications.
Deviation-based learning improves recommender systems by abstaining from recommending choices users might follow.
CAFL breaks feedback loops in recommender systems using causal inference.
In this work we propose a new computational framework, based on generative deep models, for synthesis of photo-realistic food meal images from textual descriptions of its ingredients. Previous works on synthesis of images from text typically rely on pre-trained text models to extract text features, followed by a genera…
This survey explores causal inference in banking, finance, and insurance.
DeepFair improves fairness in recommender systems without sacrificing accuracy.
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.
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…
Advances citation and subject label recommendation using multi-modal adversarial autoencoders.
Data poisoning attacks can manipulate recommender systems to recommend target items.
Proposes CF-SFL to improve sparse data recommendation.
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…
Develops a real-time exercise recommendation system using deep learning.
Survey of IoT recommendation systems and their limitations.
Recommender systems are used in variety of domains affecting people's lives. This has raised concerns about possible biases and discrimination that such systems might exacerbate. There are two primary kinds of biases inherent in recommender systems: observation bias and bias stemming from imbalanced data. Observation b…
ComiRec framework predicts user interests for personalized recommendations.
In this paper, we investigate the common scenario where every candidate item for recommendation is characterized by a maximum capacity, i.e., number of seats in a Point-of-Interest (POI) or size of an item's inventory. Despite the prevalence of the task of recommending items under capacity constraints in a variety of s…
Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper…
Unified deep framework for personalized recommendations with uncertainty.
A study compares local music recommendation algorithms, finding neighborhood-based methods perform best.
Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper…
Proposes a deep hybrid model for better recommendation systems.
SharedMF uses secret sharing to protect privacy in distributed recommendation systems.
The goal of recommendation is to show users items that they will like. Though usually framed as a prediction, the spirit of recommendation is to answer an interventional question---for each user and movie, what would the rating be if we "forced" the user to watch the movie? To this end, we develop a causal approach to …
Many businesses are using recommender systems for marketing outreach. Recommendation algorithms can be either based on content or driven by collaborative filtering. We study different ways to incorporate content information directly into the matrix factorization approach of collaborative filtering. These content-booste…
Proposes auditing for envy-freeness in recommender systems to assess individual preferences.
Paper optimizes recommendation systems for long-term business metrics.
Proposes using frequent sequences to improve sequential recommendation models.
CLEAR learns causal graphs from attention in recommender systems to explain user behavior.
The majority of recommender systems are designed to recommend items (such as movies and products) to users. We focus on the problem of recommending buyers to sellers which comes with new challenges: (1) constraints on the number of recommendations buyers are part of before they become overwhelmed, (2) constraints on th…
Paper proposes unbiased learning for recommendation causal effects.
Survey of deep learning methods for recommender systems challenges.
Paper introduces RTT2Vec for real-time grocery recommendations, achieving 9.4% uplift over baselines.
Negative user preference is an important context that is not sufficiently utilized by many existing recommender systems. This context is especially useful in scenarios where the cost of negative items is high for the users. In this work, we describe a new recommender algorithm that explicitly models negative user prefe…
Improves recommender systems using mathematical principles.
In many online applications interactions between a user and a web-service are organized in a sequential way, e.g., user browsing an e-commerce website. In this setting, recommendation system acts throughout user navigation by showing items. Previous works have addressed this recommendation setup through the task of pre…
CnGAN generates synthetic user preferences for non-overlapped users in cross-network recommender systems.
Recommenders have become widely popular in recent years because of their broader applicability in many e-commerce applications. These applications rely on recommenders for generating advertisements for various offers or providing content recommendations. However, the quality of the generated recommendations depends on …