A ranking method based on users' contexts for information recommendation

Authors:
Kenta Oku;Shinsuke Nakajima;Jun Miyazaki;Shunsuke Uemura;Hirokazu Kato
Affiliations:
Nara Institute of Science and Technology, Ikoma City, Nara, Japan;Nara Institute of Science and Technology, Ikoma City, Nara, Japan;Nara Institute of Science and Technology, Ikoma City, Nara, Japan;Nara Sangyo University, Sango-cho, Ikoma-gun, Nara, Japan;Nara Institute of Science and Technology, Ikoma City, Nara, Japan
Venue:
Proceedings of the 2nd international conference on Ubiquitous information management and communication
Year:
2008

Citing 3
Cited 2

Support-Vector Networks

Machine Learning
Extracting important sentences with support vector machines

COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Context-Aware SVM for Context-Dependent Information Recommendation

MDM '06 Proceedings of the 7th International Conference on Mobile Data Management

A recommendation method considering users' time series contexts

Proceedings of the 3rd International Conference on Ubiquitous Information Management and Communication
Modified map search engine: geographical features extraction for ranking of modified maps

Proceedings of the 5th International Conference on Ubiquitous Information Management and Communication

Quantified Score

Hi-index	0.01

Visualization

Abstract

We propose a ranking method using a Support Vector Machine for information recommendation. By using the SVM, a recommendation method can determine suitable items for a user from enormous item sets. However, it can decide based on just two classes: whether the user likes a thing or not. When there is a large number of recommended items, it is not easy for the user to find the best item by herself. To resolve this issue, it is desirable to rank the items based on the user's preferences. Moreover, the user's preferences change depending on the context. Based on the above problem, we propose a context-aware ranking method for information recommendation. Our method considers a user's context when ranking items. Our method consists of the following two steps: (1) Predicting important feature parameters for the user. (2) Calculating a ranking score of each item in recommendation candidates. In this paper, we describe our method and show experimental results.