In this paper, we propose a novel approach for generating document embeddings
using a combination of Sentence-BERT (SBERT) and RoBERTa, two state-of-the-art
natural language processing models. Our approach treats sentences as tokens and
generates embeddings for them, allowing the model to capture both
intra-sentence and inter-sentence relations within a document. We evaluate our
model on a book recommendation task and demonstrate its effectiveness in
generating more semantically rich and accurate document embeddings. To assess
the performance of our approach, we conducted experiments on a book
recommendation task using the Goodreads dataset. We compared the document
embeddings generated using our MULTI-BERT model to those generated using SBERT
alone. We used precision as our evaluation metric to compare the quality of the
generated embeddings. Our results showed that our model consistently
outperformed SBERT in terms of the quality of the generated embeddings.
Furthermore, we found that our model was able to capture more nuanced semantic
relations within documents, leading to more accurate recommendations. Overall,
our results demonstrate the effectiveness of our approach and suggest that it
is a promising direction for improving the performance of recommendation
systems

我们提出了一种使用 Sentence-BERT（SBERT）和 RoBERTa 两种最先进的自然语言处理模型结合生成文档嵌入的新方法。通过将句子视为标记并为其生成嵌入，我们的方法可以捕捉文档内句子间和句子间的关系，从而生成更具语义丰富性和准确性的文档嵌入。通过对 Goodreads 数据集上的图书推荐任务进行实验，我们评估了我们的模型，并证明了其在生成嵌入方面的有效性。与仅使用 SBERT 生成的文档嵌入相比，我们使用 MULTI-BERT 模型生成的文档嵌入在嵌入质量方面始终表现更好，通过精确度作为评估指标，我们发现我们的模型能够捕捉到更细微的语义关系，从而实现更准确的推荐。总体而言，我们的结果证明了我们的方法的有效性，并表明这是一个改进推荐系统性能的有前途的方向。

用于推荐系统的多 BERT 嵌入

Multi-BERT for Embeddings for Recommendation System

With the continuous development and change exhibited by large language model
(LLM) technology, represented by generative pretrained transformers (GPTs),
many classic scenarios in various fields have re-emerged with new
opportunities. This paper takes ChatGPT as the modeling object, incorporates
LLM technology into the typical book resource understanding and recommendation
scenario for the first time, and puts it into practice. By building a
ChatGPT-like book recommendation system (BookGPT) framework based on ChatGPT,
this paper attempts to apply ChatGPT to recommendation modeling for three
typical tasks, book rating recommendation, user rating recommendation, and book
summary recommendation, and explores the feasibility of LLM technology in book
recommendation scenarios. At the same time, based on different evaluation
schemes for book recommendation tasks and the existing classic recommendation
models, this paper discusses the advantages and disadvantages of the BookGPT in
book recommendation scenarios and analyzes the opportunities and improvement
directions for subsequent LLMs in these scenarios.

本文介绍了一种基于 ChatGPT 的 BookGPT 书籍推荐系统，利用大型语言模型技术实现了书籍的评分、用户评分和书籍概述等三项典型任务。同时，本文还讨论了 BookGPT 在书籍推荐场景中的优缺点，并分析了后续 LLM 在这些场景中的机遇和改进方向。

BookGPT：基于语言模型的图书推荐通用框架

BookGPT: A General Framework for Book Recommendation Empowered by Large  Language Model

Collaborative filtering algorithms have the advantage of not requiring
sensitive user or item information to provide recommendations. However, they
still suffer from fairness related issues, like popularity bias. In this work,
we argue that popularity bias often leads to other biases that are not obvious
when additional user or item information is not provided to the researcher. We
examine our hypothesis in the book recommendation case on a commonly used
dataset with book ratings. We enrich it with author information using publicly
available external sources. We find that popular books are mainly written by US
citizens in the dataset, and that these books tend to be recommended
disproportionally by popular collaborative filtering algorithms compared to the
users' profiles. We conclude that the societal implications of popularity bias
should be further examined by the scholar community.

本文研究合作过滤算法中的公平性问题，特别是流行度偏见对推荐结果的影响，并利用外部来源的作者信息富化图书评分数据集。结果表明，热门的合作过滤算法存在天空偏见，推荐结果偏向于美国籍作者编写的流行书籍。本研究认为应该进一步研究流行度偏见的社会影响。