Scott Sanner

Low-Rank Linear Cold-Start Recommendation from Social Data

2017 · Proceedings of the AAAI Conference on Artificial Intelligence

The cold-start problem involves recommendation of content to new users of a system, for whom there is no historical preference information available. This proves a challenge for collaborative filtering algorithms that inherently rely on such …

Unintended Bias in Language Model-driven Conversational Recommendation

2022 · arXiv (Cornell University)

Conversational Recommendation Systems (CRSs) have recently started to leverage pretrained language models (LM) such as BERT for their ability to semantically interpret a wide range of preference statement variations. However, pretrained LMs are well-known to …

Verifiable, Debuggable, and Repairable Commonsense Logical Reasoning via LLM-based Theory Resolution

2024

Recent advances in Large Language Models (LLM) have led to substantial interest in their application to commonsense reasoning tasks.Despite their potential, LLMs are susceptible to reasoning errors and hallucinations that may be harmful in use …

GENNEXT: The Next Generation of IR and Recommender Systems with Language Agents, Generative Models, and Conversational AI

2025

We present GENNEXT, a workshop dedicated to exploring the integration of language agents, generative models, and conversational AI within information retrieval (IR) and recommender systems (RS). Building on the success of our recent RecSys'24 workshop, …

AutoRec

2015

This paper proposes AutoRec, a novel autoencoder framework for collaborative filtering (CF). Empirically, AutoRec's compact and efficiently trainable model outperforms state-of-the-art CF techniques (biased matrix factorization, RBM-CF and LLORMA) on the Movielens and Netflix datasets.

Deep language-based critiquing for recommender systems

2019

Critiquing is a method for conversational recommendation that adapts recommendations in response to user preference feedback regarding item attributes. Historical critiquing methods were largely based on constraint- and utility-based methods for modifying recommendations w.r.t. these …

Large Language Models are Competitive Near Cold-start Recommenders for Language- and Item-based Preferences

2023

Traditional recommender systems leverage users’ item preference history to recommend novel content that users may like. However, modern dialog interfaces that allow users to express language-based preferences offer a fundamentally different modality for preference input. …