%A Hui Nie %T Modeling Users with Word Vector and Term-Graph Algorithm %0 Journal Article %D 2019 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2019.0494 %P 30-40 %V 3 %N 12 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4746.shtml} %8 2019-12-25 %X

[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 TextRank 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.