  Conversational recommender systems (CRSs) aim to capture user preferences and
provide personalized recommendations through multi-round natural language
dialogues. However, most existing CRS models mainly focus on dialogue
comprehension and preferences mining from the current dialogue session,
overlooking user preferences in historical dialogue sessions. The preferences
embedded in the user's historical dialogue sessions and the current session
exhibit continuity and sequentiality, and we refer to CRSs with this
characteristic as sequential CRSs. In this work, we leverage memory-enhanced
LLMs to model the preference continuity, primarily focusing on addressing two
key issues: (1) redundancy and noise in historical dialogue sessions, and (2)
the cold-start users problem. To this end, we propose a Memory-enhanced
Conversational Recommender System Framework with Large Language Models (dubbed
MemoCRS) consisting of user-specific memory and general memory. User-specific
memory is tailored to each user for their personalized interests and
implemented by an entity-based memory bank to refine preferences and retrieve
relevant memory, thereby reducing the redundancy and noise of historical
sessions. The general memory, encapsulating collaborative knowledge and
reasoning guidelines, can provide shared knowledge for users, especially
cold-start users. With the two kinds of memory, LLMs are empowered to deliver
more precise and tailored recommendations for each user. Extensive experiments
on both Chinese and English datasets demonstrate the effectiveness of MemoCRS.
