Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation
- URL: http://arxiv.org/abs/2405.12119v1
- Date: Mon, 20 May 2024 15:37:55 GMT
- Title: Reindex-Then-Adapt: Improving Large Language Models for Conversational Recommendation
- Authors: Zhankui He, Zhouhang Xie, Harald Steck, Dawen Liang, Rahul Jha, Nathan Kallus, Julian McAuley,
- Abstract summary: Large language models (LLMs) are revolutionizing conversational recommender systems.
We propose a Reindex-Then-Adapt (RTA) framework, which converts multi-token item titles into single tokens within LLMs.
Our framework demonstrates improved accuracy metrics across three different conversational recommendation datasets.
- Score: 50.19602159938368
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Large language models (LLMs) are revolutionizing conversational recommender systems by adeptly indexing item content, understanding complex conversational contexts, and generating relevant item titles. However, controlling the distribution of recommended items remains a challenge. This leads to suboptimal performance due to the failure to capture rapidly changing data distributions, such as item popularity, on targeted conversational recommendation platforms. In conversational recommendation, LLMs recommend items by generating the titles (as multiple tokens) autoregressively, making it difficult to obtain and control the recommendations over all items. Thus, we propose a Reindex-Then-Adapt (RTA) framework, which converts multi-token item titles into single tokens within LLMs, and then adjusts the probability distributions over these single-token item titles accordingly. The RTA framework marries the benefits of both LLMs and traditional recommender systems (RecSys): understanding complex queries as LLMs do; while efficiently controlling the recommended item distributions in conversational recommendations as traditional RecSys do. Our framework demonstrates improved accuracy metrics across three different conversational recommendation datasets and two adaptation settings
Related papers
- Taxonomy-Guided Zero-Shot Recommendations with LLMs [45.81618062939684]
Large language models (LLMs) have shown promise in recommender systems (RecSys)
We propose a novel method using a taxonomy dictionary to improve the clarity and structure of item information.
TaxRec significantly enhances recommendation quality compared to traditional zero-shot approaches.
arXiv Detail & Related papers (2024-06-20T07:06:58Z) - TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendation [16.93374578679005]
TokenRec is a novel framework for tokenizing and retrieving large-scale language models (LLMs) based Recommender Systems (RecSys)
Our strategy, Masked Vector-Quantized (MQ) Tokenizer, quantizes the masked user/item representations learned from collaborative filtering into discrete tokens.
Our generative retrieval paradigm is designed to efficiently recommend top-$K$ items for users to eliminate the need for auto-regressive decoding and beam search processes.
arXiv Detail & Related papers (2024-06-15T00:07:44Z) - LlamaRec: Two-Stage Recommendation using Large Language Models for
Ranking [10.671747198171136]
We propose a two-stage framework using large language models for ranking-based recommendation (LlamaRec)
In particular, we use small-scale sequential recommenders to retrieve candidates based on the user interaction history.
LlamaRec consistently achieves datasets superior performance in both recommendation performance and efficiency.
arXiv Detail & Related papers (2023-10-25T06:23:48Z) - Recommender AI Agent: Integrating Large Language Models for Interactive
Recommendations [53.76682562935373]
We introduce an efficient framework called textbfInteRecAgent, which employs LLMs as the brain and recommender models as tools.
InteRecAgent achieves satisfying performance as a conversational recommender system, outperforming general-purpose LLMs.
arXiv Detail & Related papers (2023-08-31T07:36:44Z) - Large Language Models are Zero-Shot Rankers for Recommender Systems [76.02500186203929]
This work aims to investigate the capacity of large language models (LLMs) to act as the ranking model for recommender systems.
We show that LLMs have promising zero-shot ranking abilities but struggle to perceive the order of historical interactions.
We demonstrate that these issues can be alleviated using specially designed prompting and bootstrapping strategies.
arXiv Detail & Related papers (2023-05-15T17:57:39Z) - How to Index Item IDs for Recommendation Foundation Models [49.425959632372425]
Recommendation foundation model utilizes large language models (LLM) for recommendation by converting recommendation tasks into natural language tasks.
To avoid generating excessively long text and hallucinated recommendations, creating LLM-compatible item IDs is essential.
We propose four simple yet effective solutions, including sequential indexing, collaborative indexing, semantic (content-based) indexing, and hybrid indexing.
arXiv Detail & Related papers (2023-05-11T05:02:37Z) - Talk the Walk: Synthetic Data Generation for Conversational Music
Recommendation [62.019437228000776]
We present TalkWalk, which generates realistic high-quality conversational data by leveraging encoded expertise in widely available item collections.
We generate over one million diverse conversations in a human-collected dataset.
arXiv Detail & Related papers (2023-01-27T01:54:16Z) - Finetuning Large-Scale Pre-trained Language Models for Conversational
Recommendation with Knowledge Graph [35.033130888779226]
We present a pre-trained language model (PLM) based framework called RID conversational recommender system (CRS)
RID significantly outperforms the state-of-the-art methods on both evaluations of dialogue and recommendation.
arXiv Detail & Related papers (2021-10-14T15:49:48Z)
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.