Conversational Recommendation System based on Sentiment Analysis
首发时间:2020-03-03
Abstract:The 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.
keywords: Artificial Intelligence, Dialogue System Recommender System Sentiment Analysis
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基于情感分析的对话推荐系统
摘要: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。
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