Contextual Recommendation based on Text Mining

The potential benefit of integrating contextual information for recommendation has received much research attention recently, especially with the ever-increasing interest in mobile-based recommendation services. However, context based recommendation research is limited due to the lack of standard evaluation data with contextual information and reliable technology for extracting such information. As a result, there are no widely accepted conclusions on how, when and whether context helps. Additionally, a system often suffers from the so called cold start problem due to the lack of data for training the initial context based recommendation model. This paper proposes a novel solution to address these problems with automated information extraction techniques. We also compare several approaches for utilizing context based on a new data set collected using the proposed solution. The experimental results demonstrate that 1) IE-based techniques can help create a large scale context data with ...

• Type: Journal
• Year: 2010
• Where: COLING
• Authors: Yize Li, Jiazhong Nie, Yi Zhang, Bingqing Wang, Baoshi Yan, Fuliang Weng
• Keywords: COLING 2010, Computational Linguistics, Context Based Recommendation, Contextual Information, Mobile-based Recommendation Services