面向時(shí)序感知的多類別商品方面情感分析推薦模型

丁永剛; 李石君; 付星; 劉夢(mèng)君

doi:10.11999/JEIT170938

面向時(shí)序感知的多類別商品方面情感分析推薦模型

doi: 10.11999/JEIT170938

2.
(武漢大學(xué)計(jì)算機(jī)學(xué)院武漢 430072) ②(湖北大學(xué)教育學(xué)院武漢 430062) ③(湖北大學(xué)數(shù)學(xué)與統(tǒng)計(jì)學(xué)學(xué)院武漢 430062)

基金項(xiàng)目:

國家自然科學(xué)基金(61502350)，國家自然科學(xué)基金聯(lián)合基金(U1536114)

計(jì)量
- 文章訪問數(shù): 1216
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- 被引次數(shù): 0
出版歷程
- 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: 電子商務(wù)網(wǎng)站中的評(píng)論數(shù)據(jù)隱含著商品特征和用戶情感，現(xiàn)有基于方面情感分析的推薦研究大多通過抽取同一類別商品評(píng)論數(shù)據(jù)中用戶對(duì)商品不同方面的情感來捕捉用戶方面偏好，忽略了不同類別商品有不同方面以及用戶的方面偏好隨時(shí)間變化的特點(diǎn)。對(duì)此，該文提出一種面向時(shí)序感知的多類別商品方面情感分析推薦模型，該模型對(duì)用戶、商品類別、商品、商品方面、方面情感和時(shí)間統(tǒng)一建模，以發(fā)現(xiàn)用戶對(duì)不同類別商品的方面偏好隨時(shí)間變化的特點(diǎn)，并據(jù)此做出推薦。該模型能夠推斷用戶在任意時(shí)間對(duì)商品的方面偏好，從而為用戶提供可解釋的推薦。兩個(gè)真實(shí)數(shù)據(jù)集的實(shí)驗(yàn)結(jié)果表明，與其它基于時(shí)間或方面情感分析的推薦模型相比，該文提出的模型在top-N推薦準(zhǔn)確率和召回率評(píng)價(jià)指標(biāo)上均獲得顯著改善。
- 推薦 /
- 時(shí)序感知 /
- 多類別 /
- 方面情感分析 /
- 概率潛在語義分析
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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參考文獻(xiàn)(21)

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