Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge Distillation (2023)

Abstract: In Conversational Recommendation System (CRS), an agent is asked to recommend a set of items to users within natural language conversations. To address the need for both conversational capability and personalized recommendations, prior works have utilized separate recommendation and dialogue modules. However, such approach inevitably results in a discrepancy between recommendation results and generated responses. To bridge the gap, we propose a multi-task learning for a unified CRS, where a single model jointly learns both tasks via Contextualized Knowledge Distillation (ConKD). We introduce two versions of ConKD: hard gate and soft gate. The former selectively gates between two task-specific teachers, while the latter integrates knowledge from both teachers. Our gates are computed on-the-fly in a context-specific manner, facilitating flexible integration of relevant knowledge. Extensive experiments demonstrate that our single model significantly improves recommendation performance while enhancing fluency, and achieves comparable results in terms of diversity.

## introduction natural language dialogue systems generally fall into either task-oriented system @xcite @xcite or opendomain dialogue system @xcite @xcite . despite the same modality (conversation), the tasks differ in their objectives; the former aims to achieve certain tasks (e.g. booking hotels), while the latter engage in an open-ended dialogue. conversational recommendation (cr) is an emerging task in natural language dialogue, which combines the task-oriented and open-domain @xcite . the task aims to recommend proper items to users through natural language conversations, and the model-generated conversation is expected to be fluent and suit the context. unlike traditional recommendation systems, the interactive nature of multi-turn dialogue allows the agent to explore explicit user interests that may not be present in the user's history. this advantage particularly stands out compared to the traditional models that have access to only few purchase histories or implicit feedback (e.g. click) from users. to generate appropriate responses containing recommended items aligned with a user's taste, an agent needs to possess both recommendation and dialogue capabilities. previous works addressed the issue with separate modules @xcite @xcite . a recommendation module learns user preferences based on conversation history and retrieve relevant items, while a dialogue module generates the final response sentences. in conversational recommendation system (crs), a major problem lies in incorporating the two separate modules. common strategies for injecting recommendation ability into the responses include copy mechanism @xcite and pointer network @xcite . despite these efforts, the prior approaches @xcite @xcite have demonstrated a discrepancy between the separate modules: the results of a recommendation model are not integrated in the generated response as depicted in figure 1 . the dialogue module suggests "the avengers", while the recommendation module recommends "titanic", revealing a clear disagreement between the two modules. accordingly, the probability distribution of the recommendation model is not directly reflected in the output of the dialogue model. such mismatch is inevitable when cr is formulated as two separate tasks, failing to serve the original purpose of the task. to address this challenge, we propose a multitask learning approach for a unified crs using contextualized knowledge distillation (conkd). we build a crs with a single model via knowledge transfer by two teacher models, a dialogue teacher and a recommendation teacher. however, combining the knowledge is not straightforward due to the nature of crs; the task differs in each time step depending on the context. in this light, we introduce two gating mechanisms, hard gate and soft gate, to effectively fuse teachers' knowledge. with hard gate, knowledge transfer comes solely from either of the teachers, while soft gate integrates both sources of knowledge. we introduce an adaptive nature in the gates which is context-specific and computed on-the-fly during forward propagation. to our knowledge, this is the first work to explicitly demonstrate the existence of the discrepancy, and provide a dedicated training mechanism to address it. moreover, our approach offers the flexibility in selecting model architectures, enabling integration of diverse language and recommendation models. extensive experiments conducted on a widely used benchmark dataset @xcite demonstrate that our single model significantly outperforms baselines in terms of recommendation performance and response fluency, while achieving comparable results in response diversity. the contributions of our work can be summarized as follows: ## unified crs via conkd in this section, we first demonstrate the mismatch issue with preliminary experiments and introduce our approach that mitigates such problem. ## conclusion in this study, we introduce contextualized knowledge distillation with hard gate and soft gate. in hard gate, a student is either learned from a recommendation teacher or from a dialogue teacher with a discrete value, while the soft gate fuses knowledge from both teachers, removing a hyperparameter from the hard gate. the gates are computed in a context-specific manner by aggregating the probability mass on the interest set (a movie item set in our experiment). our work verifies the idea in the popular benchmark dataset and the result illustrates the superior performance of our approach compared to strong baselines. in addition, human evaluation mainly conforms with the automatic evaluation, demonstrating that the proposed approach is a well-balanced model with both recommendation and chit-chat ability.