Product Recommendations Enhanced with Reviews

被引:14
作者
Chelliah, Muthusamy [1 ]
Sarkar, Sudeshna [2 ]
机构
[1] Flipkart, Bangalore, Karnataka, India
[2] Indian Inst Technol Kharagpur, Kharagpur, W Bengal, India
来源
PROCEEDINGS OF THE ELEVENTH ACM CONFERENCE ON RECOMMENDER SYSTEMS (RECSYS'17) | 2017年
关键词
Recommender System; Review Based Recommendation; Text Mining; Sentiment Analysis; Explainable Recommendation;
D O I
10.1145/3109859.3109936
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
User-written product reviews contain rich information about user preferences for product features and provide helpful explanations that are often used by shoppers to make their purchase decisions. E-commerce recommender systems can benefit enormously by also exploiting experiences of multiple customers captured in product reviews. In this tutorial, we present a range of techniques that allow recommender systems in e-commerce websites to take full advantage of reviews. This includes text mining methods for feature-specific sentiment analysis of products, topic models and distributed representations that bridge the vocabulary gap between user reviews and product descriptions. We present recommender algorithms that use review information to address the cold-start problem and generate recommendations with explanations. We discuss examples and experiences from an online marketplace (i.e., Flipkart).
引用
收藏
页码:398 / 399
页数:2
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