Language-Based User Profiles for Recommendation
- URL: http://arxiv.org/abs/2402.15623v1
- Date: Fri, 23 Feb 2024 21:58:50 GMT
- Title: Language-Based User Profiles for Recommendation
- Authors: Joyce Zhou, Yijia Dai, Thorsten Joachims
- Abstract summary: The Language-based Factorization Model (LFM) is an encoder/decoder model where both the encoder and the decoder are large language models (LLMs)
The encoder LLM generates a compact natural-language profile of the user's interests from the user's rating history.
We evaluate our LFM approach on the MovieLens dataset, comparing it against matrix factorization and an LLM model that directly predicts from the user's rating history.
- Score: 24.685132962653793
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Most conventional recommendation methods (e.g., matrix factorization)
represent user profiles as high-dimensional vectors. Unfortunately, these
vectors lack interpretability and steerability, and often perform poorly in
cold-start settings. To address these shortcomings, we explore the use of user
profiles that are represented as human-readable text. We propose the
Language-based Factorization Model (LFM), which is essentially an
encoder/decoder model where both the encoder and the decoder are large language
models (LLMs). The encoder LLM generates a compact natural-language profile of
the user's interests from the user's rating history. The decoder LLM uses this
summary profile to complete predictive downstream tasks. We evaluate our LFM
approach on the MovieLens dataset, comparing it against matrix factorization
and an LLM model that directly predicts from the user's rating history. In
cold-start settings, we find that our method can have higher accuracy than
matrix factorization. Furthermore, we find that generating a compact and
human-readable summary often performs comparably with or better than direct LLM
prediction, while enjoying better interpretability and shorter model input
length. Our results motivate a number of future research directions and
potential improvements.
Related papers
- Review-LLM: Harnessing Large Language Models for Personalized Review Generation [8.898103706804616]
Large Language Models (LLMs) have shown superior text modeling and generating ability.
We propose Review-LLM that customizes LLMs for personalized review generation.
arXiv Detail & Related papers (2024-07-10T09:22:19Z) - Fairer Preferences Elicit Improved Human-Aligned Large Language Model Judgments [41.25558612970942]
We show that large language models (LLMs) exhibit preference biases and worrying sensitivity to prompt designs.
Motivated by this phenomenon, we propose an automatic Zero-shot Evaluation-oriented Prompt Optimization framework, ZEPO.
arXiv Detail & Related papers (2024-06-17T09:48:53Z) - Aligning Large Language Models with Self-generated Preference Data [72.99676237703099]
We propose a new framework that boosts the alignment of large language models (LLMs) with human preferences.
Our key idea is leveraging the human prior knowledge within the small (seed) data.
We introduce a noise-aware preference learning algorithm to mitigate the risk of low quality within generated preference data.
arXiv Detail & Related papers (2024-06-06T18:01:02Z) - Aligning Large Language Models via Fine-grained Supervision [20.35000061196631]
Pre-trained large-scale language models (LLMs) excel at producing coherent articles, yet their outputs may be untruthful, toxic, or fail to align with user expectations.
Current approaches focus on using reinforcement learning with human feedback to improve model alignment.
We propose a method to enhance LLM alignment through fine-grained token-level supervision.
arXiv Detail & Related papers (2024-06-04T20:21:45Z) - CodecLM: Aligning Language Models with Tailored Synthetic Data [51.59223474427153]
We introduce CodecLM, a framework for adaptively generating high-quality synthetic data for instruction-following abilities.
We first encode seed instructions into metadata, which are concise keywords generated on-the-fly to capture the target instruction distribution.
We also introduce Self-Rubrics and Contrastive Filtering during decoding to tailor data-efficient samples.
arXiv Detail & Related papers (2024-04-08T21:15:36Z) - LLMRefine: Pinpointing and Refining Large Language Models via Fine-Grained Actionable Feedback [65.84061725174269]
Recent large language models (LLM) are leveraging human feedback to improve their generation quality.
We propose LLMRefine, an inference time optimization method to refine LLM's output.
We conduct experiments on three text generation tasks, including machine translation, long-form question answering (QA), and topical summarization.
LLMRefine consistently outperforms all baseline approaches, achieving improvements up to 1.7 MetricX points on translation tasks, 8.1 ROUGE-L on ASQA, 2.2 ROUGE-L on topical summarization.
arXiv Detail & Related papers (2023-11-15T19:52:11Z) - Evaluating and Explaining Large Language Models for Code Using Syntactic
Structures [74.93762031957883]
This paper introduces ASTxplainer, an explainability method specific to Large Language Models for code.
At its core, ASTxplainer provides an automated method for aligning token predictions with AST nodes.
We perform an empirical evaluation on 12 popular LLMs for code using a curated dataset of the most popular GitHub projects.
arXiv Detail & Related papers (2023-08-07T18:50:57Z) - On Learning to Summarize with Large Language Models as References [101.79795027550959]
Large language models (LLMs) are favored by human annotators over the original reference summaries in commonly used summarization datasets.
We study an LLM-as-reference learning setting for smaller text summarization models to investigate whether their performance can be substantially improved.
arXiv Detail & Related papers (2023-05-23T16:56:04Z) - Do LLMs Understand User Preferences? Evaluating LLMs On User Rating
Prediction [15.793007223588672]
Large Language Models (LLMs) have demonstrated exceptional capabilities in generalizing to new tasks in a zero-shot or few-shot manner.
We investigate various LLMs in different sizes, ranging from 250M to 540B parameters and evaluate their performance in zero-shot, few-shot, and fine-tuning scenarios.
arXiv Detail & Related papers (2023-05-10T21:43:42Z) - Why do Nearest Neighbor Language Models Work? [93.71050438413121]
Language models (LMs) compute the probability of a text by sequentially computing a representation of an already-seen context.
Retrieval-augmented LMs have shown to improve over standard neural LMs, by accessing information retrieved from a large datastore.
arXiv Detail & Related papers (2023-01-07T11:12:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.