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

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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.

Key words： Recommendation Temporal-aware Multi-category Aspect-level sentiment analysis Probabilistic Latent Semantic Analysis (PLSA)

Received: 11 October 2017 Published: 03 May 2018

PACS:

TP393

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

Corresponding Authors: LI Shijun E-mail: shjli@whu.edu.cn

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	DING Yonggang
	LI Shijun
	FU Xing
	LIU Mengjun

Cite this article:

DING Yonggang,LI Shijun,FU Xing等. Temporal-aware Multi-category Products Recommendation Model Based on Aspect-level Sentiment Analysis[J]. JEIT, 2018, 40(6): 1453-1460.

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http://jeit.ie.ac.cn/EN/10.11999/JEIT170938 OR http://jeit.ie.ac.cn/EN/Y2018/V40/I6/1453

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