User Interest Based Contextual Conversational Recommender System

Abstract

The Conversational recommender system is for item recommendation during human-machine dialogue, where items can be movies, music, and goods. Scenarios can be intelligent customer service and shopping assistants. The system's goal is to generate high-quality recommendations in short-round interactions. There are two main problems with the system. Firstly, recommending through dialogues is a hard problem as less contextual information is available, resulting in fewer signals for recommendation and less precision for user interest modeling. Secondly, the recommender system considers the absence of interaction between a user and an item as a negative sample. However, this approach can introduce bias in the objectives because non-interaction could potentially be a positive sample. To tackle the previously mentioned concerns, this paper suggests a new model. This model adds an extra knowledge graph to enrich contextual information. Then, this model introduces contrastive learning to separate the distribution between positive and negative samples and improve learning efficiency. This paper further tests the model on the ReDial dataset, and experiments have shown that the method is effective and performs better than previous work.