Introduction
Conversational recommender systems (CRS) have become an active research topic, which leverages both natural language processing and recommendation techniques to provide high-quality recommendations through interactive conversations with users Jannach et al. ( 2021 ); Gao et al. ( 2021 ); Pramod and Bafna ( 2022 ) .
CRS consists of two sub-tasks: 1) generating natural language responses to interact with user ( conversation ); and 2) recommending desirable items to user based on dialogue context ( recommendation ). An example of CRS data and model prediction is shown in Figure 1 . In general, CRS represents a significant advancement in the field of recommendation, which could be applied to various possible use cases, such as e-commerce, entertainment and content platforms.

Existing CRS methods can be roughly categorized into attribute-based and generation-based methods. The attribute-based methods Lei et al. ( 2020 ); Ren et al. ( 2020 ); Zou et al. ( 2020 ) focus on collecting user preferences on item attributes to narrow down recommendation space to items with desired properties. The generation-based methods Zhou et al. ( 2020a , 2022 ); Wang et al. ( 2022c ) aim to acquire feedback from users, generate natural responses, and establish a comprehensive understanding of conversation to recommend the most desirable items to user. In this work, we focus on generation-based CRS, which was greatly facilitated with the rise of task-specific CRS datasets like ReDial Li et al. ( 2018 ) , INSPIRED Hayati et al. ( 2020 ) , TG-ReDial Zhou et al. ( 2020b ) and DuRecDial Liu et al. ( 2020 ) .

The key challenge of CRS methods consists in how to jointly model language generation and item recommendation, which are tasks of entirely different natures. Early approaches Chen et al. ( 2019 ); Zhou et al. ( 2020a ); Zhang et al. ( 2022 ); Zhou et al. ( 2022 ) mainly model conversation and recommendation tasks separately by incorporating external knowledge graphs (KG) for item semantics and designing auxiliary strategies to enhance the interactions between two tasks. They generally treat items as nodes, which neglects the affluent textual information of items. They also sustain semantic misalignment issue due to inconsistent item and word representations, because conversation and recommendation modules are separately learned. Recent approaches Wang et al. ( 2022a , b , c ); Yang et al. ( 2022 ) explore to seamlessly integrate conversation and recommendation modules for better knowledge sharing and semantic alignment via unified frameworks. However, due to the natural gap between recommendation and conversation, they still require multiple training phases Wang et al. ( 2022c ) and/or additional modules Wang et al. ( 2022a ); Yang et al. ( 2022 ) to integrate the two tasks, failing to reach desired level of integration.

With the rapid development of language models (LMs), LMs for recommendation has gained significant attention. Based on LMs, recent work Wu et al. ( 2023 ); Lin et al. ( 2023 ) also shows a growing correlation between recommendation and language tasks. Thus, instead of applying structured KGs, we stick to using item text descriptions together with dialogue contexts for CRS, which formulates the CRS directly as a natural language processing task. Specifically, we devise a P arameter- E fficient C onversational R ecommender S ystem ( PECRS ), which jointly solves recommendation and conversation by training a single model once, to bypass the shortcomings of prior work in CRS. PECRS only relies on a frozen pre-trained LM as backbone and employs a parameter-efficient plugin module to unify response generation and item recommendation in a simple yet flexible manner. Besides, we design a shared negative sampling strategy to sample negative items across subtasks and data points within the same mini-batch to boost both training efficiency and model performance. Moreover, thanks to the parameter-efficient plugin module, PECRS can easily scale up to larger LM backbones without significantly increasing training parameters.
In brief, our contributions are the following:

To the best of our knowledge, this is the first work solving CRS by optimizing a single model in a single training phase and bypassing the need for either KGs or additional item encoders.

We demonstrate how to jointly generate response and learn item representations using a single and frozen language model. Through parameter-efficient fine-tuning techniques, our method is with low computation cost, and can easily scale to larger backbones for higher performance.

Experiments on two benchmark datasets, ReDial and INSPIRED, demonstrate the effectiveness of our proposed PECRS method, which is competitive with SOTA.