IRanker: Query-Specific Ranking of Reviewed Items

被引：0
作者：
Shahbazi, Moloud [1 ]
Wiley, Matthew [1 ]
Hristidis, Vagelis [1 ]
机构：
[1] UC Riverside, Riverside, CA 92521 USA
来源：
2017 IEEE 33RD INTERNATIONAL CONFERENCE ON DATA ENGINEERING (ICDE 2017) | 2017年
基金：
美国国家科学基金会;
关键词：
D O I：
10.1109/ICDE.2017.77
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Item (e.g., product) reviews are one of the most popular types of user-generated content in Web 2.0. Reviews have been effectively used in collaborative filtering to recommend products to users based on similar users, and also to compute a product's star rating. However, little work has studied how reviews can be used to perform query-specific ranking of items. In this paper, we present efficient top-k algorithms to rank items, by weighing each review's rating by its relevance to the user query. We propose a non-random access algorithm and perform a comprehensive evaluation of our method on multiple datasets. We show that our solution significantly outperforms the baseline approach in terms of query response time.
引用
收藏
页码：211 / 214
页数：4
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