H ut CRS : Hierarchical User-Interest Tracking for Conversational Recommender System (2023)

Abstract: Conversational Recommender System (CRS) aims to explicitly acquire user preferences towards items and attributes through natural language conversations. However, existing CRS methods ask users to provide explicit answers (yes/no) for each attribute they require, regardless of users’ knowledge or interest, which may significantly reduce the user experience and semantic consistency. Furthermore, these methods assume that users like all attributes of the target item and dislike those unrelated to it, which can introduce bias in attribute-level feedback and impede the system’s ability to accurately identify the target item. To address these issues, we propose a more realistic, user-friendly, and explainable CRS framework called Hierarchical User-Interest Tracking for Conversational Recommender System (HutCRS). HutCRS portrays the conversation as a hierarchical interest tree that consists of two stages. In stage I, the system identifies the aspects that the user prefers while the system asks about attributes related to these positive aspects or recommends items in stage II. In addition, we develop a Hierarchical-Interest Policy Learning (HIPL) module to integrate the decision-making process of which aspects to ask and when to ask about attributes or recommend items. Moreover, we classify the attribute-level feedback results to further enhance the system’s ability to capture special information, such as attribute instances that are accepted by users but not presented in their historical interactive data. Extensive experiments on four benchmark datasets demonstrate the superiority of our method. The implementation of HutCRS is publicly available at https://github.com/xinle1129/HutCRS.

## introduction conversational recommender systems (crss) have become one of the trending research topics in re-* both authors contributed equally to this research. although mcr is widely acknowledged as the most realistic setting for crs, the existing mcr assumptions regarding user interest deviate from real-world scenarios. first, mcr presupposes that users have definitive answers(yes or no) for all queried attributes. however, users might be unaware of or indifferent to certain attributes, and compelling them to respond could reduce the user experience and generate inconsistent conversation utterances. additionally, mcr assumes that users will accept all attributes that belong to the target item and reject those unrelated to it. in practice, users might not necessarily like all attributes of the target item, nor dislike attributes that are not included in the target item. this discrepancy can result in invalid attribute-level feedback and prevent the system from identifying the target item accurately. for instance, considering a target item "an esports phone with a large battery capacity but low-performance camera", the user doesn't prioritize the performance of the camera, but it doesn't imply that the user prefers a low-performance camera. if the system asks about the "low-performance camera", it may receive negative feedback which could cause it to miss out on the target item. to address the aforementioned issues, we propose a novel framework hierarchical user-interest tracking for conversational recommender system (hutcrs). as shown in figure 1 , we represent the user's preferences in the current session as a hierarchical tree that depicts the user's interests and priorities, facilitating a better understanding of their needs and preferences. specifically, the system first prompts the user to specify their aspects of interest based on user-initialized attributes. once this information is obtained by the system, it will either ask about attributes associated with the positive aspects or recommend items. the interaction based on the hierarchical interest tree enables the system to ask about attributes that the user is indeed interested in, thereby improving user experience and consistency in the conversation. besides, when attribute instances belong to the target item and align with the aspects of interest to the user, they are more likely to be accepted. this clear preference will lead to the generation of more authentic feedback. inspired by @xcite , we develop a hierarchical-interest policy learning (hipl) module to integrate the decision-making process of which aspects to ask and when to ask attributes or when to recommend items. in addition, we classify the attribute-level feedback results to further enhance the system's ability to capture special information. positive attributes that are not present in the historical data and negative attributes that are present in the historical data are marked as "special". extensive experiments on yelp, lastfm, amazon-book, and movielen demonstrate the superiority of hutcrs compared with state-of-the-art baselines. in summary, our contributions can be concluded as follows: in single-round scenario @xcite , the crs only makes recommendations once, ending the session regardless of the results. in multi-round scenario, the crs can interacts with the user by asking attributes and recommending items multiple times until the task succeeds or the user terminates the session. due to the wide acknowledgement of multiround conversational recommendation (mcr) as the most realistic setting @xcite for crs, this work is conducted based on the multiround scenario. ear @xcite ) extends the single-round setting into a multi-round setting where the system can recommend multiple times. scpr @xcite models the mcr as an interactive path reasoning problem on a graph, which is able to prune off many irrelevant candidate attributes. fpan @xcite utilizes a gating mechanism to aggregate online feedback from users. unicorn @xcite proposes to formulate three decision-making problems in crs as a unified policy learning task based on dynamic weighted graph. mimcpl @xcite extends mcr to a more realistic scenario setting named mimcr where the user may accept multiple attribute instances with the same type and multiple items with partially overlapped attributes. however, the existing mcr assumptions about user interest still deviate from real-world scenarios. hence, we propose a novel framework called hutcrs to tackle these issues. ## definition and preliminary to better describe and understand the hutcrs, we set up some basic notions in this section. we define the sets of users and items as u and v, respectively. additionally, we separately define the sets of aspect (i.e. type) and attribute instances as c and p. each item instance @xmath0. each attribute instance @xmath1. in each episode, there is a set v u of items that are acceptable to the user @xmath2. the set is represented as ## hutcrs to improve user experience and semantic consistency, we propose a novel framework hierarchical user-interest tracking for conversation recommender system (hutcrs) for mcr. similarly to zhang et al. (2022); lei et al. (2020b); deng et al. (2021) , our framework also aims to learn the policy network π(a t |s t ) to maximize the expected cumulative rewards as: π * = arg max π∈π e[ t @xmath3, where s t denotes the current state, a t denotes the action taken by the agent and the r t is the intermediate reward. as shown in figure 2 , our framework portrays the conversation as a hierarchical interest tree that consists of two stages where each stage comprises two components: hierarchical user interest tracking and hierarchical-interest policy learning. in stage i, the system aims to identify the aspects that the user prefers, while in stage ii, the system will either ask about attributes related to these positive aspects or recommend items.