TREA : Tree-Structure Reasoning Schema for Conversational Recommendation (2023)

Abstract: Conversational recommender systems (CRS) aim to timely trace the dynamic interests of users through dialogues and generate relevant responses for item recommendations. Recently, various external knowledge bases (especially knowledge graphs) are incorporated into CRS to enhance the understanding of conversation contexts. However, recent reasoning-based models heavily rely on simplified structures such as linear structures or fixed-hierarchical structures for causality reasoning, hence they cannot fully figure out sophisticated relationships among utterances with external knowledge. To address this, we propose a novel Tree structure Reasoning schEmA named TREA. TREA constructs a multi-hierarchical scalable tree as the reasoning structure to clarify the causal relationships between mentioned entities, and fully utilizes historical conversations to generate more reasonable and suitable responses for recommended results. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach.

## introduction conversation recommender system (crs) has become increasingly popular as its superiority in timely discovering user dynamic preferences in practice. as opposed to traditional passive-mode recommendation systems, it highlights the importance of proactively clarifying and tracing user interests through live conversation interactions, which notably enhance the success rate of item recommendations. since sole contextual utterances are insufficient for comprehensively understanding user preferences, there are many efforts devoted to incorporat-corresponding author ing various external knowledge @xcite @xcite @xcite , which typically enrich the contextual information with mentioned entities recognized over utterances. however, these methods fail to model the complex causal relations among mentioned entities, owing to the diversity of user interest expression and the frequent shift of conversation topic as shown in figure 1 . actually, it is non-trivial to explicitly model the complex causal relationships of conversations. although there are several reasoning-based methods proposed for crs, their simplified structures make the objective unattainable. some researches @xcite track the mentioned entities as linear sequential fragments analogous to (1) in figure 1 . however, the linear structure is only suitable for adjacent relation modeling, which may not always work well since the actual causality between mentioned entities exists multi-hop jumps ("comedy"-"la la land" in figure 1 ). other studies @xcite propose other forms of specially-designed structures for reasoning akin to (2) in figure 1 , but they generally have fixed hierarchies, which often degenerate into a simple 2-layer hierarchy "history"-"prediction", neglecting the causal relations of historical entities. therefore, neither of them is applicable for full modeling of the complex reasoning causality within conversations. to improve the reasoning capability of crs, the challenges are twofold. the first challenge lies in empowering the model to illuminate the causal inference between all mentioned entities. to tackle this, we performs abductive reasoning for each mentioned entity to construct the multi-hierarchical reasoning tree. the reasoning tree explicitly preserves logical relations between all entities and can be continuously expanded as the conversation continues, which provides the model with a clear to validate the effectiveness of our approach, we conduct experiments on two public crs datasets. experimental results show that our trea outperforms competitive baselines on both the recommendation and conversation tasks. our main contributions are summarized as follows: ## related work conversational recommender system (crs) explores user preference through natural language dialogues. previous works can be roughly categorized into two types. the first category of crs is recommendation-biased crs @xcite @xcite . this category focuses solely on interactive recommendations but the function of natural language is ignored. several fixed response templates are preset on the agents and users cannot use free text but only have limited options, which can be detrimental to the user experience. the other category of crss is dialog-biased crs @xcite @xcite @xcite . this category emphasizes the critical effect of natural language, aiming to understand user utterances for accurate recommendations and generate human-like responses. noticing that entities @xcite @xcite mentioned in conversations are important cues for modeling user preferences, @xcite firstly integrates kg to enhance the user representation. zhou et al. (2020a); liang et al. (2021) use two kgs on entity-granularity and word-granularity respectively to represent the user preference more comprehensively. subsequent researches introduce other types of external knowledge e.g. item description @xcite or pretrained language models (plms) @xcite to further assist the user representations. however, they commonly treat each mentioned knowledge piece equally and integrate them into an aggregated representation. recently, some researches manage to model the reasoning process during conversations. @xcite linearize the mentioned entity sequence and reasoning the inferential causality between the adjacent entity pairs. ma et al. (2021) create non-linear reasoning structures, but they do not preserve the hierarchy of historical turns. therefore these reasoning methods have limited performance improvement. to sort out the causal relations among utterances, our model performs tree-structured reasoning on the entire dialogue history for each mentioned entity. we also inject the reasoning information into the generation process to make responses more relevant, achieving that the reasoning process facilitates both recommendation and generation tasks simultaneously. ## methods in this section, we present the tree-structure reasoning schema trea as demonstrated in figure 2 . specifically, we first introduce the encoding of entities and word tokens. then we illustrate the construction procedure of the reasoning tree. later, we describe how the reasoning information supports the generation module. finally, we explain the process of parameter optimization. ## experiment 4.1 dataset. we conduct our experiments on two widely-applied benchmark datasets on crs, which are multilingual including english (redial) and chinese (tg-redial). redial @xcite collects highquality dialogues for recommendations on movies through crowd-sourcing workers on amazon mechanical turk(amt). the workers create conversations for the task of movie recommendation in a user-recommender pair setting following a set of detailed instructions. it contains 10,006 conversations consisting of 182,150 utterances. tg-redial @xcite is annotated in a semiautomatic way. it emphasizes natural topic transitions from non-recommendation scenarios to the desired recommendation scenario. each conversation includes a topic path to enforce natural semantic transitions. it contains 10,000 conversations consisting of 129,392 utterances. ## conclusion in this paper, we propose a novel tree-structure reasoning schema for crs to clarify the sophisticated relationships between mentioned entities for accurate user modeling. in the constructed reasoning tree, each entity is connected to its cause which motivates the mention of the entity to pro-vide a clear reference for the current recommendation. the generation module also interacts with the reasoning tree to extract relevant textual information. extensive experimental results have shown that our approach outperforms several competitive baselines, especially in long conversation scenarios.