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Found 15 papers, 2 papers with code
Optimization and Analysis of the pAp@k Metric for Recommender Systems
Modern recommendation and notification systems must be robust to data imbalance, limitations on the number of recommendations/notifications, and heterogeneous engagement profiles across users.
All Against Some: Efficient Integration of Large Language Models for Message Passing in Graph Neural Networks
In this paper, we investigate how LLMs can be leveraged in a computationally efficient fashion to benefit rich graph-structured data, a modality relatively unexplored in LLM literature.
Context-Aware Clustering using Large Language Models
Thus, we propose CACTUS (Context-Aware ClusTering with aUgmented triplet losS), a systematic approach that leverages open-source LLMs for efficient and effective supervised clustering of entity subsets, particularly focusing on text-based entities.
ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding
ForeSeer transfers reviews from similar products on a large product graph and exploits these reviews to predict aspects that might emerge in future reviews.
Metric Elicitation; Moving from Theory to Practice
Specifically, we create a web-based ME interface and conduct a user study that elicits users' preferred metrics in a binary classification setting.
Classification Performance Metric Elicitation and its Applications
Specifically, we provide novel strategies for eliciting linear and linear-fractional metrics for binary and multiclass classification problems, which are then extended to a framework that elicits group-fair performance metrics in the presence of multiple sensitive groups.
Optimizing Black-box Metrics with Iterative Example Weighting
We consider learning to optimize a classification metric defined by a black-box function of the confusion matrix.
Quadratic Metric Elicitation for Fairness and Beyond
Metric elicitation is a recent framework for eliciting classification performance metrics that best reflect implicit user preferences based on the task and context.
Rich-Item Recommendations for Rich-Users: Exploiting Dynamic and Static Side Information
In this paper, we study the problem of recommendation system where the users and items to be recommended are rich data structures with multiple entity types and with multiple sources of side-information in the form of graphs.
Multiclass Performance Metric Elicitation
Metric Elicitation is a principled framework for selecting the performance metric that best reflects implicit user preferences.
Online Diverse Learning to Rank from Partial-Click Feedback
We assume that the user examines the list of recommended items until the user is attracted by an item, which is clicked, and does not examine the rest of the items.
Clustered Monotone Transforms for Rating Factorization
This non-linear transformation of the rating scale shatters the low-rank structure of the rating matrix, therefore resulting in a poor fit and consequentially, poor recommendations.
Performance Metric Elicitation from Pairwise Classifier Comparisons
Given a binary prediction problem, which performance metric should the classifier optimize?
Generating Appealing Brand Names
Providing appealing brand names to newly launched products, newly formed companies or for renaming existing companies is highly important as it can play a crucial role in deciding its success or failure.
