Conversational recommender systems (CRS) enable users to articulate their
preferences and provide feedback through natural language. With the advent of
large language models (LLMs), the potential to enhance user engagement with CRS
and augment the recommendation process with LLM-generated content has received
increasing attention. However, the efficacy of LLM-powered CRS is contingent
upon the use of prompts, and the subjective perception of recommendation
quality can differ across various recommendation domains. Therefore, we have
developed a ChatGPT-based CRS to investigate the impact of these two factors,
prompt guidance (PG) and recommendation domain (RD), on the overall user
experience of the system. We conducted an online empirical study (N = 100) by
employing a mixed-method approach that utilized a between-subjects design for
the variable of PG (with vs. without) and a within-subjects design for RD (book
recommendations vs. job recommendations). The findings reveal that PG can
substantially enhance the system's explainability, adaptability, perceived ease
of use, and transparency. Moreover, users are inclined to perceive a greater
sense of novelty and demonstrate a higher propensity to engage with and try
recommended items in the context of book recommendations as opposed to job
recommendations. Furthermore, the influence of PG on certain user experience
metrics and interactive behaviors appears to be modulated by the recommendation
domain, as evidenced by the interaction effects between the two examined
factors. This work contributes to the user-centered evaluation of ChatGPT-based
CRS by investigating two prominent factors and offers practical design
guidance.

使用 ChatGPT 为基础的对话式推荐系统研究了 Prompt Guidance（PG）和 Recommendation Domain（RD）这两个因素对系统整体用户体验的影响，并发现 PG 可以大大提升系统的可解释性、适应性、感知易用性和透明度，而用户在书籍推荐领域相较于职位推荐领域更倾向于感知到新颖性，并展现更高的从事和尝试推荐项目的倾向。此外，PG 对某些用户体验度量和交互行为的影响似乎受到推荐领域的调节，这也证明了这两个因素之间的交互效应。该研究为基于 ChatGPT 的用户中心评估的对话式推荐系统提供了实用的设计指导。

ChatGPT 基于对话推荐系统的用户体验导航：提示引导和推荐领域的影响

Navigating User Experience of ChatGPT-based Conversational Recommender  Systems: The Effects of Prompt Guidance and Recommendation Domain

Inferring missing regions from severely occluded point clouds is highly
challenging. Especially for 3D shapes with rich geometry and structure details,
inherent ambiguities of the unknown parts are existing. Existing approaches
either learn a one-to-one mapping in a supervised manner or train a generative
model to synthesize the missing points for the completion of 3D point cloud
shapes. These methods, however, lack the controllability for the completion
process and the results are either deterministic or exhibiting uncontrolled
diversity. Inspired by the prompt-driven data generation and editing, we
propose a novel prompt-guided point cloud completion framework, coined
P2M2-Net, to enable more controllable and more diverse shape completion. Given
an input partial point cloud and a text prompt describing the part-aware
information such as semantics and structure of the missing region, our
Transformer-based completion network can efficiently fuse the multimodal
features and generate diverse results following the prompt guidance. We train
the P2M2-Net on a new large-scale PartNet-Prompt dataset and conduct extensive
experiments on two challenging shape completion benchmarks. Quantitative and
qualitative results show the efficacy of incorporating prompts for more
controllable part-aware point cloud completion and generation. Code and data
are available at this https URL

我们提出了一个基于提示的点云补全框架，名为 P2M2-Net，用于更可控和多样化的形状补全。通过使用 Transformer 模型，我们的框架能够高效地融合多模态特征并按照提示指导生成多样化的结果。我们在一个新的大规模 PartNet-Prompt 数据集上训练 P2M2-Net，并在两个具有挑战性的形状补全基准上进行了广泛实验。定量和定性结果表明了引入提示进行更可控的部分感知点云补全和生成的有效性。