基于情感分析的对话推荐系统
onversational Recommendation System based on Sentiment Analysis
对话推荐系统作为对话系统和推荐系统的结合，最近受到了广泛的关注。为了解决在对话推荐系统中难以获得用户喜好的问题，在对话推荐系统模型中使用了情感分析的方法分析用户喜好。根据ReDial数据集处理并生成了电影情感分析数据集。使用训练好的情感分析模型获取用户电影喜好，并通过对话系统的形式根据用户喜好进行电影推荐，产生自然语言回复，与用户进行人机对话。在情感分析数据集进行实验对比。实验结果表明，本文方法达到了0.8362的F1分数，相比基线模型的0.7802和其他模型有更好的效果。因此，对话推荐系统推荐的电影也更加符合用户口味。
he combination of the recommender system and dialogue system which called the conversational recommendation system is a growing interest. Tosolve the problem that it is difficult to obtain users' tastes in conversational recommendation systems. A sentiment analysis method is proposed in our conversational recommendation model to get user preferences. A sentiment analysis dataset is created and the model uses a sentiment analysis approach to obtain a movie seeker\'s preferences and make a recommendation. Experimentresults show that our sentiment analysis model yields a better performance of 0.8362(F1 score) than the baseline(0.7802) and other models. Thus, the movie recommended by our system can meet the needs of users better.
李剑、李新胜
计算技术、计算机技术
人工智能对话系统推荐系统情感分析
rtificial Intelligence Dialogue SystemRecommender SystemSentiment Analysis
李剑,李新胜.基于情感分析的对话推荐系统[EB/OL].(2020-03-03)[2025-08-16].http://www.paper.edu.cn/releasepaper/content/202003-20.点此复制
评论