Personalizing e-Commerce by Semantics-enhanced Strategies and Time-aware Recommendations

被引：4

作者：

Blanco-Fernandez, Yolanda ^{[1
]}

Lopez-Nores, Martin ^{[1
]}

Pazos-Arias, Jose J. ^{[1
]}

Gil-Solla, Alberto ^{[1
]}

Ramos-Cabrer, Manuel ^{[1
]}

机构：

[1] Univ Vigo, IDTV Lab, Vigo 36310, Spain

来源：

THIRD INTERNATIONAL WORKSHOP ON SEMANTIC MEDIA ADAPTATION AND PERSONALIZATION, PROCEEDINGS | 2008年

关键词：

D O I：

10.1109/SMAP.2008.11

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Current e-commerce recommender systems adapt the selection of commercial items suggested to the users as their preferences evolve over time. However this adaptation process misses the tune elapsed since the user has bought an item, which is an essential parameter that affects differently to each purchased product. This results in some useless recommendations, including regularly items that the users are only willing to buy sporadically In this paper We explore a new recommendation strategy that offers time-aware suggestions to e-commerce users, by enhancing reasoning techniques from the Semantic Web with item-dependent tune functions. This combination leads to suggestions adapted to the particular needs of each user at any given moment.

引用

页码：193 / 198

页数：6

共 5 条

[1]

BLANCOFERNANDEZ Y, J SYSTEMS S IN PRESS

[2]

Fernández YB, 2006, IEEE T CONSUM ELECTR, V52, P223

[3] A personalized recommendation system based on product taxonomy for one-to-one marketing online [J].