Rating prediction using feature words extracted from customer reviews

Masanao Ochi, Makoto Okabe, Rikio Onai

研究成果: Conference contribution

5 被引用数 (Scopus)

抄録

We developed a simple method of improving the accuracy of rating prediction using feature words extracted from customer reviews. Many rating predictors work well for a small and dense dataset of customer reviews. However, a practical dataset tends to be large and sparse, because it often includes too many products for each customer to buy and evaluate. Data sparseness reduces prediction accuracy. To improve accuracy, we reduced the dimension of the feature vector using feature words extracted by analyzing the relationship between ratings and accompanying review comments instead of using ratings. We applied our method to the Pranking algorithm and evaluated it on a corpus of golf course reviews supplied by a Japanese e-commerce company. We found that by successfully reducing data sparse-ness, our method improves prediction accuracy as measured using RankLoss.

本文言語English
ホスト出版物のタイトルSIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval
ページ1205-1206
ページ数2
DOI
出版ステータスPublished - 2011
外部発表はい
イベント34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'11 - Beijing
継続期間: 2011 7 242011 7 28

Other

Other34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'11
CityBeijing
Period11/7/2411/7/28

ASJC Scopus subject areas

  • Information Systems

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