Toward Knowledge-Enriched Conversational Recommendation Systems (2022)

Abstract: Conversational Recommendation Systems recommend items through language based interactions with users. In order to generate naturalistic conversations and effectively utilize knowledge graphs (KGs) containing background information, we propose a novel Bag-of-Entities loss, which encourages the generated utterances to mention concepts related to the item being recommended, such as the genre or director of a movie. We also propose an alignment loss to further integrate KG entities into the response generation network. Experiments on the large-scale REDIAL dataset demonstrate that the proposed system consistently outperforms state-of-the-art baselines.

## introduction conversational recommendation systems (crs) have received increasing attention from the natural language processing community @xcite @xcite @xcite @xcite . crs aims to recommend items, such as movies or songs, in naturalistic interactive conversations with the user. this interactive form allows the system to provide recommendations tailored to preferences provided by the user at the moment. a crucial issue of crs is to extract user preferences from the conversation, which often requires background information provided by knowledge graphs (kgs). as an example, in figure 1 , the user mentions two movies that belong to the horror genre. to this end, some existing studies @xcite leverage knowledge graphs to understand user intentions. we observe that when humans recommend items to friends, they usually describe attributes of the item. for example, to recommend a movie, they may mention the director or actors. such information can be easily extracted from the knowledge graph, but has not been well utilized by existing approaches. to emulate naturalistic conversations, we propose a bag-of-entities (boe) loss, which encourages the generated utterances to mention concepts related to the item. moreover, we propose an alignment loss that ties the word embeddings to the entity embeddings. experiments demonstrate that the proposed two losses improve model performance. the proposed the knowledge-enriched conversational recommendation system (kecrs) consistently outperforms state-of-the-art crss on the large-scale re-dial dataset (li et al., 2018). ## related work we briefly review work on conversational recommendation systems and conversational characters in e-commerce settings. a number of works on conversational recommendation systems focus solely on interactive recommendation rather than language understanding @xcite @xcite @xcite @xcite . in contrast, a second category of works aims to provide both accurate interactive recommendations and generate natural recommendation network entity sequence 𝑬 look up 𝐻 self attention hidden representation 𝒄 𝑬 graph neural network entity embedding 𝑯 knowledge graph matching 0.9 0.7 … encoder decoder vocabulary embedding 𝑾 𝒗 conversation context entity selection generation hidden representation 𝒄 𝑬 kg entity distribution vocabulary distribution embedding alignment response generation network predict: you'll have to check out it (2017) if you like horror movies with murder mystery. and human-like responses @xcite @xcite @xcite @xcite . finally, research on conversational characters for e-commerce has the broad goal of building a complete shopping assistant that can answer a variety of questions in addition to recommendation @xcite @xcite . ## approach the overall goal of a conversational recommendation system is to identify an item (e.g., a movie, a song, or a piece of merchandise) that the user will likely interact with and suggest the item to the user in the form of natural language conversations. formally, we represent the historic conversation @xmath0, x 2 , ..., x n ⟩ as a sequence of n utterances x i . the knowledge graph @xmath1, r, v t )} is a set of entities e and a set of relationships r between the head entity v @xmath2. the conversational recommendation task is to predict the next utterance x n+1 using the recommendation network f (x, g) and the response gen- figure 2 shows the overall structure of our proposed method, the knowledge-enriched conversational recommendation system (kecrs).