【目的】提出一個基于評論的用戶建模算法,實現評論資訊的個性化推薦。【方法】借助預訓練詞向量從評論觀點句中提取細粒度的產品特征,基于語義關聯構建特征詞圖,并運用TextRank關鍵詞抽取算法計算用戶對產品特性的關注度,構建用戶興趣模型。【結果】結果顯示,結合詞向量和詞圖算法生成的用戶模型與人工歸納的用戶模型吻合度較高,語義關聯度近90%。模型評測指標F1為0.5505,優于基于詞頻的傳統詞袋模型(特征詞模型F1為0.5269,詞項模型F1為0.3322)。【局限】通過人工標注的評測語料偏少;基于通用語料獲得的詞向量對解決領域相關問題存有一定局限。【結論】對于形式表達不規范的評論語言,信息凝聚與語義分析技術的有機結合能夠有效提升用戶建模的質量,為評論質量的評價及評論在推薦系統中的有效利用提供了新思路。

[Objective] This paper proposes a review-based user modeling method, aiming to improve the personalized information pushing services. [Methods] Firstly, we identified product feature-specific terms from reviews with the help of pre-trained word embedding model. Then, we built a term-specific graph based on semantic correlation among feature-specific words. Finally, we used the Text Rank algorithm to compute user’s interest in product features, and model their preferences for products. [Results] User model generated by our new algorithm was consistent with the manually created ones(with nearly 90% semantic correlation). Our F1-score was 0.55, better than those of the classic TF-based word bag models. [Limitations] More manually labeled data and research is needed to improve the domain-specific analysis. [Conclusions] The proposed model helps us better analyze online reviews and develop new application for recommendation system.