A search index-enhanced feature model for news recommendation.

General news recommendations are important but have received limited attention because of the difficulties of measuring public interest. In public search engines, the objects of search terms reflect the issues that interest or concern search engine users. Because of the popularity of search engines, search indexes have become a new measure for describing public interest trends. With the help of a public search index provided by search engines, we construct a news topic search feature and a news object search feature. These features measure the public attention on key elements of the news. In the experiment, we compare various feature models with machine learning algorithms with respect to financial news recommendations. The results demonstrate that the topic search features perform best compared with other feature models. This research contributes to both the feature generation and news recommendation domains. [ABSTRACT FROM AUTHOR]