面向时序感知的多类别商品方面情感分析推荐模型

丁永刚; 李石君; 付星; 刘梦君

doi:10.11999/JEIT170938

面向时序感知的多类别商品方面情感分析推荐模型

doi: 10.11999/JEIT170938

2.
(武汉大学计算机学院武汉 430072) ②(湖北大学教育学院武汉 430062) ③(湖北大学数学与统计学学院武汉 430062)

基金项目:

国家自然科学基金(61502350)，国家自然科学基金联合基金(U1536114)

计量
- 文章访问数: 1171
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- 被引次数: 0
出版历程
- 收稿日期: 2017-10-11
- 修回日期: 2018-04-17
- 刊出日期: 2018-06-19

Temporal-aware Multi-category Products Recommendation Model Based on Aspect-level Sentiment Analysis

2.
(School of Education, Hubei University, Wuhan 430072, China)

Funds:

The National Natural Science Foundation of China (61502350), The Joint Funds of National Natural Science foundation of China (U1536114)

摘要

摘要: 电子商务网站中的评论数据隐含着商品特征和用户情感，现有基于方面情感分析的推荐研究大多通过抽取同一类别商品评论数据中用户对商品不同方面的情感来捕捉用户方面偏好，忽略了不同类别商品有不同方面以及用户的方面偏好随时间变化的特点。对此，该文提出一种面向时序感知的多类别商品方面情感分析推荐模型，该模型对用户、商品类别、商品、商品方面、方面情感和时间统一建模，以发现用户对不同类别商品的方面偏好随时间变化的特点，并据此做出推荐。该模型能够推断用户在任意时间对商品的方面偏好，从而为用户提供可解释的推荐。两个真实数据集的实验结果表明，与其它基于时间或方面情感分析的推荐模型相比，该文提出的模型在top-N推荐准确率和召回率评价指标上均获得显著改善。
- 推荐 /
- 时序感知 /
- 多类别 /
- 方面情感分析 /
- 概率潜在语义分析
Abstract: Review data in e-commerce websites implicates items features and users sentiment. Most existing recommendation researches based on aspect-level sentiment analysis capture users aspect preference for items by extracting users sentiment towards different aspects of items in the review data of a same category, ignoring that different category items have different aspects and that users aspect preference varies by time. A temporal-aware multi-category products recommendation model is proposed based on aspect-level sentiment analysis, which jointly models user, category, item, aspect, aspect-sentiment and time in order to find how users aspect preferences vary by time on different category items. This model is able to infer users aspect preferences for items at any time, which can provide users with explainable recommendations. Experiment results on two real-world data sets show that, in comparison to other recommendation models based on time or aspect-level sentiment analysis, the proposed model achieves significant improvement in the precision and recall for the top-N recommendation.
- Recommendation /
- Temporal-aware /
- Multi-category /
- Aspect-level sentiment analysis /
- Probabilistic Latent Semantic Analysis (PLSA)

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