CLICK : Contrastive Learning for Injecting Contextual Knowledge to Conversational Recommender System (2023)

Abstract: Conversational recommender systems (CRSs) capture a user preference through a conversation. However, the existing CRSs lack capturing comprehensive user preferences. This is because the items mentioned in a conversation are mainly regarded as a user preference. Thus, they have limitations in identifying a user preference from a dialogue context expressed without preferred items. Inspired by the characteristic of an online recommendation community where participants identify a context of a recommendation request and then comment with appropriate items, we exploit the Reddit data. Specifically, we propose a Contrastive Learning approach for Injecting Contextual Knowledge (CLICK) from the Reddit data to the CRS task, which facilitates the capture of a context-level user preference from a dialogue context, regardless of the existence of preferred item-entities. Moreover, we devise a relevance-enhanced contrastive learning loss to consider the fine-grained reflection of multiple recommendable items. We further develop a response generation module to generate a persuasive rationale for a recommendation. Extensive experiments on the benchmark CRS dataset show the effectiveness of CLICK, achieving significant improvements over state-of-the-art methods.

## introduction recommender systems help users find potential items of interest in the rapidly expanding pool of candidates. to do so, the recommender systems aim to accurately identify user preferences. thus, traditional recommender systems @xcite utilize click or purchase data to obtain user preferences. however, such traditional approaches lack dynamically modeling user preferences since the data has a temporal validity and does not offer a diverse clue to user information. conversational recommender system (crs) is a research area that overcomes the limitation of the traditional user i am stressed out from work today. so, i'm looking for a mindless movie. recommender you must be exhausted! have you ever watched "21 jump street"? it is so much fun. oh wait, i have a little son who wants to watch a movie together. then, i recommend "yes man". it's a family movie. your son also will like it. ## pre-training for contextual knowledge injection we introduce the training scheme of click that follows a two-stage mechanism: 1) a pre-training stage for the contextual knowledge injection and 2) a fine-tuning stage for the conversational recommendation task. in the two stages, we utilize dbpedia @xcite as a kg, following the existing crss @xcite @xcite that take advantage of kgs to capture user preferences based on factual information such as objects, concepts, and relationships among them. the kg is defined as @xmath0, r, e2)|e1, @xmath1, @xmath2, where a fact (e1, r, e2) consists of relation r from entity e1 to entity e2 from entity set e and relation set r. the reddit data @xcite ) is leveraged to learn the contextual knowledge that facilitates the inference of a preferred item from a user utterance. the reddit data is denoted as @xmath3, s, v, t)} , where q is a seeker's request text to receive a recommendation, s is an item-suggestion by a recommender, v is a relevance score of the item-suggestion, and t is an optional rationale for the item-suggestion. we describe the details of the reddit dataset in section 5.1. in the pre-training stage, our approach infuses the contextual knowledge from reddit data into a kg-encoder and a text-encoder by training them via a devised contrastive learning loss. then, a response generator is pre-trained to equip the ability to generate an explanation for a recommendation based on the recognized needs. the pre-training stage of click is described in figure 2 . ## conclusions and future work in this paper, we proposed the contextual knowledge injection via contrastive learning to capture a comprehensive user preference from a dialogue context. utilizing the extracted contextual knowledge, click not only precisely captures the preference on mentioned entities but also identifies the user's needs in a dialogue context. moreover, the relevance-enhanced contrastive learning loss enables the contextual knowledge to reflect the acceptance degree of recommendations. we further developed the context-enhanced response generator to provide a persuasive explanation. lastly, experimental results on the benchmark dataset confirmed the effectiveness of click on providing appropriate recommendations, as well as generating high-quality responses. in future work, we will explore a crs with a recommendation dialogue strategy of actively asking questions to elicit a user taste instead of passively communicating with a user.