Parameter-Efficient Conversational Recommender System as a Language Processing Task (2024)

Abstract: Conversational recommender systems (CRS) aim to recommend relevant items to users by eliciting user preference through natural language conversation. Prior work often utilizes external knowledge graphs for items’ semantic information, a language model for dialogue generation, and a recommendation module for ranking relevant items. This combination of multiple components suffers from a cumber-some training process, and leads to semantic misalignment issues between dialogue generation and item recommendation. In this paper, we represent items in natural language and formulate CRS as a natural language processing task. Accordingly, we leverage the power of pre-trained language models to encode items, understand user intent via conversation, perform item recommendation through semantic matching, and generate dialogues. As a unified model, our PECRS (Parameter-Efficient CRS), can be optimized in a single stage, without relying on non-textual metadata such as a knowledge graph. Experiments on two benchmark CRS datasets, ReDial and INSPIRED, demonstrate the effectiveness of PECRS on recommendation and conversation. Our code is available at: https://github.com/Ravoxsg/efficient_unified_crs.

## 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 @xcite @xcite . 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 figure 1 : an example of dialogue from redial @xcite , where blue color denotes the movie items. existing crs methods can be roughly categorized into attribute-based and generation-based methods. the attribute-based methods @xcite @xcite focus on collecting user preferences on item attributes to narrow down recommendation space to items with desired properties. the generation-based methods @xcite @xcite 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 @xcite , inspired @xcite , tg-redial @xcite and durecdial @xcite . 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 @xcite @xcite 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 @xcite 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 @xcite and/or additional modules @xcite 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 @xcite ) 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 parameter-efficient conversational recommender system (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: ## related work existing conversational recommender systems (crs) can be roughly categorized into attributebased 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 @xcite @xcite @xcite . our work belongs to the generation-based crs, which focuses on developing natural language based approaches to make high-quality recommendation and generate humanlike responses simultaneously @xcite @xcite . 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 @xcite @xcite @xcite @xcite @xcite use knowledge graphs (kg) @xcite coupled with graph networks @xcite to enhance the items and user preference understanding by designing sophisticated semantic alignment strategies. revcore @xcite and c 2 -crs @xcite further incorporate movie reviews to enrich the contextual knowledge via cross-attention @xcite and contrastive learning @xcite . 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 @xcite adopts the dialogpt @xcite for both conversation and recommendation by tuning soft prompts @xcite dedicated to each task. nevertheless, unicrs requires three rounds of optimization, i.e., semantic fusion pre-training, conversation tuning, and recommendation tuning. unimind @xcite follows the uni-crs paradigm with bart @xcite as the backbone, which unifies multi-goal crs, i.e., multi-tasks, using prompting strategy with multiple training stages. recindial @xcite augments items into dialogpt vocabulary and designs a pointer mechanism for dynamic word and item prediction to achieve single multi-tasking process. similarly, barcor @xcite utilizes bart to recommend items with encoder and generate responses with decoder concurrently. instead of using kg, mese @xcite encodes item representations using metadata and fuses them into dialogue for joint conversation and recommendation learning using gpt-2 @xcite 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 @xcite and distil-bert @xcite ) 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 finetuning (peft) strategies. peft, including prompt tuning @xcite , adapters @xcite , and lora @xcite , 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 @xcite , computer vision @xcite , and recommendation @xcite 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. ## methodology in this section, we first describe the problem statement of conversational recommendation systems (crs). then we present the proposed parameter-efficient conversational recommender system (pecrs) method in detail. the overall architecture of pecrs is shown in figure 2 . ## analysis in this section, we provide more detailed insights about the behavior of pecrs. ## conclusion in this work, we formulate conversational recommendation as a language processing task and propose a unified parameter-efficient crs (pecrs) framework to solve it in a single-stage end-to-end manner. pecrs effectively addresses the inferior training efficiency via parameter-efficient finetuning techniques and semantic misalignment issues via joint conversation and recommendation modeling. through experiments, we show that pecrs achieves performance competitive with sota on both recommendation and response generation on benchmark datasets. moreover, for response evaluation, we reveal the commonly used dist@k metrics are not reliable, and advocate for reference-based metrics (e.g rouge) for more accurate evaluation. generally, we show that it is promising to explore unified framework for crs under the natural language paradigm via language model and rich textual items data.