272 citations · 451 across the 9 of their papers we have counts for
9 papers
Large Language Models as Zero-Shot Conversational Recommenders
Zhankui He, Zhouhang Xie, Rahul Jha +6
In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contribution…
On the Regularization of Autoencoders
Harald Steck, Dario Garcia Garcia
While much work has been devoted to understanding the implicit (and explicit) regularization of deep nonlinear networks in the supervised setting, this paper focuses on unsupervise…
Markov Random Fields for Collaborative Filtering
Harald Steck
In this paper, we model the dependencies among the items that are recommended to a user in a collaborative-filtering problem via a Gaussian Markov Random Field (MRF). We build upon…
Embarrassingly Shallow Autoencoders for Sparse Data
Harald Steck
Combining simple elements from the literature, we define a linear model that is geared toward sparse data, in particular implicit feedback data for recommender systems. We show tha…
Collaborative Filtering via High-Dimensional Regression
Harald Steck
While the SLIM approach obtained high ranking-accuracy in many experiments in the literature, it is also known for its high computational cost of learning its parameters from data.…
On the Use of Skeletons when Learning in Bayesian Networks
Harald Steck
In this paper, we present a heuristic operator which aims at simultaneously optimizing the orientations of all the edges in an intermediate Bayesian network structure during the se…
Unsupervised Active Learning in Large Domains
Harald Steck, Tommi S. Jaakkola
Active learning is a powerful approach to analyzing data effectively. We show that the feasibility of active learning depends crucially on the choice of measure with respect to whi…
Ranking by Dependence - A Fair Criteria
Harald Steck
Estimating the dependences between random variables, and ranking them accordingly, is a prevalent problem in machine learning. Pursuing frequentist and information-theoretic approa…
Learning the Bayesian Network Structure: Dirichlet Prior versus Data
Harald Steck
In the Bayesian approach to structure learning of graphical models, the equivalent sample size (ESS) in the Dirichlet prior over the model parameters was recently shown to have an…