Related Work
Existing conversational recommender systems (CRS) can be roughly categorized into attribute-based and generation-based CRS methods. The attribute-based CRS methods utilize predefined actions to interact with users and target on accomplishing the recommendation task with fewer turns Christakopoulou et al. ( 2016 ); Sun and Zhang ( 2018 ); Lei et al. ( 2020 ); Ren et al. ( 2020 ); Zou et al. ( 2020 ); Hu et al. ( 2022a ) . Our work belongs to the generation-based CRS, which focuses on developing natural language based approaches to make high-quality recommendation and generate human-like responses simultaneously Li et al. ( 2018 ); Hayati et al. ( 2020 ); Zhou et al. ( 2020b ); Liu et al. ( 2020 ) .

Generation-based CRS methods usually devise a recommendation module and a conversation module to implement item recommendation and response generation, respectively. Li et al. ( 2018 ) propose the first CRS dataset named ReDial, and solve it via encoder-decoder-based dialogue generator and autoencoder-based recommender. Subsequent work commonly adopts external resources to incorporate sufficient contextual information for better performance. Numerous works Chen et al. ( 2019 ); Zhou et al. ( 2020a , 2021 ); Ma et al. ( 2020 ); Zhang et al. ( 2022 ); Liang et al. ( 2021 ); Li et al. ( 2022 ); Liu et al. ( 2023 ); Zhang et al. ( 2023b ) use knowledge graphs (KG) Auer et al. ( 2007 ); Speer et al. ( 2017 ) coupled with graph networks Schlichtkrull et al. ( 2018 ) to enhance the items and user preference understanding by designing sophisticated semantic alignment strategies. RevCore Lu et al. ( 2021 ) and C 2 -CRS Zhou et al. ( 2022 ) further incorporate movie reviews to enrich the contextual knowledge via cross-attention Lu et al. ( 2021 ) and contrastive learning Zhou et al. ( 2022 ) . Despite consecutive improvements, these works rely on different architectures for conversation and recommendation, making them difficult to be effectively integrated for end-to-end training and knowledge sharing. Consequently, they still suffer from a mismatch between conversation and recommendation modules as well as inferior efficiency.

To remedy the aforementioned issues, recent approaches explore to jointly learn both conversation and recommendation tasks by pre-trained LMs. UniCRS Wang et al. ( 2022c ) adopts the DialoGPT Zhang et al. ( 2020 ) for both conversation and recommendation by tuning soft prompts Lester et al. ( 2021 ) dedicated to each task. Nevertheless, UniCRS requires three rounds of optimization, i.e., semantic fusion pre-training, conversation tuning, and recommendation tuning. UniMIND Deng et al. ( 2023 ) follows the UniCRS paradigm with BART Lewis et al. ( 2020 ) as the backbone, which unifies multi-goal CRS, i.e., multi-tasks, using prompting strategy with multiple training stages. RecInDial Wang et al. ( 2022a ) augments items into DialoGPT vocabulary and designs a pointer mechanism for dynamic word and item prediction to achieve single multi-tasking process. Similarly, BARCOR Wang et al. ( 2022b ) utilizes BART to recommend items with encoder and generate responses with decoder concurrently. Instead of using KG, MESE Yang et al. ( 2022 ) encodes item representations using metadata and fuses them into dialogue for joint conversation and recommendation learning using GPT-2 Radford et al. ( 2019 ) as the backbone. Although these methods attempt to integrate conversation and recommendation tasks for joint optimization, they rely on extra modules ( e.g., R-GCN Schlichtkrull et al. ( 2018 ) and DistilBERT Sanh et al. ( 2019 ) ) for either item encoding or semantic fusion, and multi-round training stages. In contrast, our goal is to design a framework to unify the CRS training under a single model optimized in a single training stage.

Our work also employs parameter-efficient fine-tuning (PEFT) strategies. PEFT, including prompt tuning Lester et al. ( 2021 ) , Adapters Houlsby et al. ( 2019 ) , and LoRA Hu et al. ( 2022b ) , is a series of techniques to adapt (large) LMs with fewer parameters and low computation costs to achieve same or even better performance comparing to the standard fine-tuning on downstream tasks. PEFT has shown great promise in various natural language Zhang et al. ( 2023a ); Dettmers et al. ( 2023 ) , computer vision He et al. ( 2022 ); Chen et al. ( 2023 ) , and recommendation Fu et al. ( 2023 ) tasks, but remains underexplored in CRS area. In this work, we aim to train CRS via PEFT plugins without touching the parameters of the backbone LM.