论文标题
通过基于及时的情感分析增强协作过滤推荐人
Enhancing Collaborative Filtering Recommender with Prompt-Based Sentiment Analysis
论文作者
论文摘要
协作过滤（CF）推荐人是在线市场和电子商务中的关键应用程序。但是，CF推荐人已被证明会遇到与用户评级稀疏有关的持续问题，这将进一步导致寒冷的问题。现有方法通过应用令牌级别的情感分析来解决数据稀疏问题，该分析将文本评论转化为情感分数作为用户评分的补充。在本文中，我们试图通过包括Bert和Roberta在内的高级NLP模型来优化情感分析，并实验是否已进一步增强了CF建议者。我们在Amazon US评论数据集上构建了推荐人，并通过传统的微调范式以及新的基于及时的基于及时的学习范式来调整经过验证的Bert和Roberta。实验结果表明，推荐人通过微调的罗伯塔（Roberta）预测的情感评级增强了最佳性能，并且通过比较映射，NDCG和k的精度与基线推荐者的映射，NDCG和精度可实现30.7％的总增长。迅速的学习范式虽然在纯情感分析中优于传统的微调范式，但无法进一步改善CF的推荐人。
Collaborative Filtering(CF) recommender is a crucial application in the online market and ecommerce. However, CF recommender has been proven to suffer from persistent problems related to sparsity of the user rating that will further lead to a cold-start issue. Existing methods address the data sparsity issue by applying token-level sentiment analysis that translate text review into sentiment scores as a complement of the user rating. In this paper, we attempt to optimize the sentiment analysis with advanced NLP models including BERT and RoBERTa, and experiment on whether the CF recommender has been further enhanced. We build the recommenders on the Amazon US Reviews dataset, and tune the pretrained BERT and RoBERTa with the traditional fine-tuned paradigm as well as the new prompt-based learning paradigm. Experimental result shows that the recommender enhanced with the sentiment ratings predicted by the fine-tuned RoBERTa has the best performance, and achieved 30.7% overall gain by comparing MAP, NDCG and precision at K to the baseline recommender. Prompt-based learning paradigm, although superior to traditional fine-tune paradigm in pure sentiment analysis, fail to further improve the CF recommender.
