As its availability and generality in online services, implicit feedback is
more commonly used in recommender systems. However, implicit feedback usually
presents noisy samples in real-world recommendation scenarios (such as
misclicks or non-preferential behaviors), which will affect precise user
preference learning. To overcome the noisy samples problem, a popular solution
is based on dropping noisy samples in the model training phase, which follows
the observation that noisy samples have higher training losses than clean
samples. Despite the effectiveness, we argue that this solution still has
limits. (1) High training losses can result from model optimization instability
or hard samples, not just noisy samples. (2) Completely dropping of noisy
samples will aggravate the data sparsity, which lacks full data exploitation.
To tackle the above limitations, we propose a Double Correction Framework for
Denoising Recommendation (DCF), which contains two correction components from
views of more precise sample dropping and avoiding more sparse data. In the
sample dropping correction component, we use the loss value of the samples over
time to determine whether it is noise or not, increasing dropping stability.
Instead of averaging directly, we use the damping function to reduce the bias
effect of outliers. Furthermore, due to the higher variance exhibited by hard
samples, we derive a lower bound for the loss through concentration inequality
to identify and reuse hard samples. In progressive label correction, we
iteratively re-label highly deterministic noisy samples and retrain them to
further improve performance. Finally, extensive experimental results on three
datasets and four backbones demonstrate the effectiveness and generalization of
our proposed framework.

通过双重校正框架进行去噪推荐，解决了推荐系统中显式反馈嘈杂样本的问题，提高了准确性和数据利用率。

双重修正框架用于去噪推荐

Double Correction Framework for Denoising Recommendation

Recent popularity surrounds large AI language models due to their impressive
natural language capabilities. They contribute significantly to
language-related tasks, including prompt-based learning, making them valuable
for various specific tasks. This approach unlocks their full potential,
enhancing precision and generalization. Research communities are actively
exploring their applications, with ChatGPT receiving recognition.
Despite extensive research on large language models, their potential in
recommendation scenarios still needs to be explored. This study aims to fill
this gap by investigating ChatGPT's capabilities as a zero-shot recommender
system. Our goals include evaluating its ability to use user preferences for
recommendations, reordering existing recommendation lists, leveraging
information from similar users, and handling cold-start situations. We assess
ChatGPT's performance through comprehensive experiments using three datasets
(MovieLens Small, Last.FM, and Facebook Book).
We compare ChatGPT's performance against standard recommendation algorithms
and other large language models, such as GPT-3.5 and PaLM-2. To measure
recommendation effectiveness, we employ widely-used evaluation metrics like
Mean Average Precision (MAP), Recall, Precision, F1, normalized Discounted
Cumulative Gain (nDCG), Item Coverage, Expected Popularity Complement (EPC),
Average Coverage of Long Tail (ACLT), Average Recommendation Popularity (ARP),
and Popularity-based Ranking-based Equal Opportunity (PopREO).
Through thoroughly exploring ChatGPT's abilities in recommender systems, our
study aims to contribute to the growing body of research on the versatility and
potential applications of large language models. Our experiment code is
available on the GitHub repository:
this https URL

本研究旨在填补大型语言模型在推荐场景中的潜力研究空白，并通过实验评估 ChatGPT 在零样本推荐系统中的能力及其与标准推荐算法以及其他大型语言模型的对比表现。

评估 ChatGPT 作为推荐系统的方法论

Evaluating ChatGPT as a Recommender System: A Rigorous Approach

The goal of sequential recommendation (SR) is to predict a user's potential
interested items based on her/his historical interaction sequences. Most
existing sequential recommenders are developed based on ID features, which,
despite their widespread use, often underperform with sparse IDs and struggle
with the cold-start problem. Besides, inconsistent ID mappings hinder the
model's transferability, isolating similar recommendation domains that could
have been co-optimized. This paper aims to address these issues by exploring
the potential of multi-modal information in learning robust and generalizable
sequence representations. We propose MISSRec, a multi-modal pre-training and
transfer learning framework for SR. On the user side, we design a
Transformer-based encoder-decoder model, where the contextual encoder learns to
capture the sequence-level multi-modal synergy while a novel interest-aware
decoder is developed to grasp item-modality-interest relations for better
sequence representation. On the candidate item side, we adopt a dynamic fusion
module to produce user-adaptive item representation, providing more precise
matching between users and items. We pre-train the model with contrastive
learning objectives and fine-tune it in an efficient manner. Extensive
experiments demonstrate the effectiveness and flexibility of MISSRec, promising
an practical solution for real-world recommendation scenarios.

该论文提出了一种基于多模态信息的多模态预训练和迁移学习框架（MISSRec），用于顺序推荐，以解决现有基于 ID 特征的推荐方法在稀疏 ID 和冷启动问题方面的性能不佳的问题。通过在用户和候选项两方面分别设计 Transformer-based 的编码器 - 解码器模型和动态融合模块，MISSRec 能够实现更鲁棒且可迁移的序列表示，该方法在实验中表现出的效果和灵活性使其成为实际推荐场景的可行解决方案。