1,431 research outputs found
Seamlessly Unifying Attributes and Items: Conversational Recommendation for Cold-Start Users
Static recommendation methods like collaborative filtering suffer from the
inherent limitation of performing real-time personalization for cold-start
users. Online recommendation, e.g., multi-armed bandit approach, addresses this
limitation by interactively exploring user preference online and pursuing the
exploration-exploitation (EE) trade-off. However, existing bandit-based methods
model recommendation actions homogeneously. Specifically, they only consider
the items as the arms, being incapable of handling the item attributes, which
naturally provide interpretable information of user's current demands and can
effectively filter out undesired items. In this work, we consider the
conversational recommendation for cold-start users, where a system can both ask
the attributes from and recommend items to a user interactively. This important
scenario was studied in a recent work. However, it employs a hand-crafted
function to decide when to ask attributes or make recommendations. Such
separate modeling of attributes and items makes the effectiveness of the system
highly rely on the choice of the hand-crafted function, thus introducing
fragility to the system. To address this limitation, we seamlessly unify
attributes and items in the same arm space and achieve their EE trade-offs
automatically using the framework of Thompson Sampling. Our Conversational
Thompson Sampling (ConTS) model holistically solves all questions in
conversational recommendation by choosing the arm with the maximal reward to
play. Extensive experiments on three benchmark datasets show that ConTS
outperforms the state-of-the-art methods Conversational UCB (ConUCB) and
Estimation-Action-Reflection model in both metrics of success rate and average
number of conversation turns.Comment: TOIS 202
Conversational Recommender System and Large Language Model Are Made for Each Other in E-commerce Pre-sales Dialogue
E-commerce pre-sales dialogue aims to understand and elicit user needs and
preferences for the items they are seeking so as to provide appropriate
recommendations. Conversational recommender systems (CRSs) learn user
representation and provide accurate recommendations based on dialogue context,
but rely on external knowledge. Large language models (LLMs) generate responses
that mimic pre-sales dialogues after fine-tuning, but lack domain-specific
knowledge for accurate recommendations. Intuitively, the strengths of LLM and
CRS in E-commerce pre-sales dialogues are complementary, yet no previous work
has explored this. This paper investigates the effectiveness of combining LLM
and CRS in E-commerce pre-sales dialogues, proposing two collaboration methods:
CRS assisting LLM and LLM assisting CRS. We conduct extensive experiments on a
real-world dataset of Ecommerce pre-sales dialogues. We analyze the impact of
two collaborative approaches with two CRSs and two LLMs on four tasks of
Ecommerce pre-sales dialogue. We find that collaborations between CRS and LLM
can be very effective in some cases.Comment: EMNLP 2023 Finding
Personalized Memory Transfer for Conversational Recommendation Systems
Dialogue systems are becoming an increasingly common part of many users\u27 daily routines. Natural language serves as a convenient interface to express our preferences with the underlying systems. In this work, we implement a full-fledged Conversational Recommendation System, mainly focusing on learning user preferences through online conversations. Compared to the traditional collaborative filtering setting where feedback is provided quantitatively, conversational users may only indicate their preferences at a high level with inexact item mentions in the form of natural language chit-chat. This makes it harder for the system to correctly interpret user intent and in turn provide useful recommendations to the user. To tackle the ambiguities in natural language conversations, we propose Personalized Memory Transfer (PMT) which learns a personalized model in an online manner by leveraging a key-value memory structure to distill user feedback directly from conversations. This memory structure enables the integration of prior knowledge to transfer existing item representations/preferences and natural language representations. We also implement a retrieval based response generation module, where the system in addition to recommending items to the user, also responds to the user, either to elicit more information regarding the user intent or just for a casual chit-chat. The experiments were conducted on two public datasets and the results demonstrate the effectiveness of the proposed approach
Leveraging Large Language Models in Conversational Recommender Systems
A Conversational Recommender System (CRS) offers increased transparency and
control to users by enabling them to engage with the system through a real-time
multi-turn dialogue. Recently, Large Language Models (LLMs) have exhibited an
unprecedented ability to converse naturally and incorporate world knowledge and
common-sense reasoning into language understanding, unlocking the potential of
this paradigm. However, effectively leveraging LLMs within a CRS introduces new
technical challenges, including properly understanding and controlling a
complex conversation and retrieving from external sources of information. These
issues are exacerbated by a large, evolving item corpus and a lack of
conversational data for training. In this paper, we provide a roadmap for
building an end-to-end large-scale CRS using LLMs. In particular, we propose
new implementations for user preference understanding, flexible dialogue
management and explainable recommendations as part of an integrated
architecture powered by LLMs. For improved personalization, we describe how an
LLM can consume interpretable natural language user profiles and use them to
modulate session-level context. To overcome conversational data limitations in
the absence of an existing production CRS, we propose techniques for building a
controllable LLM-based user simulator to generate synthetic conversations. As a
proof of concept we introduce RecLLM, a large-scale CRS for YouTube videos
built on LaMDA, and demonstrate its fluency and diverse functionality through
some illustrative example conversations
Finetuning Large-Scale Pre-trained Language Models for Conversational Recommendation with Knowledge Graph
In this paper, we present a pre-trained language model (PLM) based framework
called RID for conversational recommender system (CRS). RID finetunes the
large-scale PLMs such as DialoGPT, together with a pre-trained Relational Graph
Convolutional Network (RGCN) to encode the node representations of an
item-oriented knowledge graph. The former aims to generate fluent and diverse
dialogue responses based on the strong language generation ability of PLMs,
while the latter is to facilitate the item recommendation by learning better
node embeddings on the structural knowledge base. To unify two modules of
dialogue generation and item recommendation into a PLMs-based framework, we
expand the generation vocabulary of PLMs to include an extra item vocabulary,
and introduces a vocabulary pointer to control when to recommend target items
in the generation process. Extensive experiments on the benchmark dataset
ReDial show RID significantly outperforms the state-of-the-art methods on both
evaluations of dialogue and recommendation
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