How Dable Recommends Contents

How Dable Recommends Contents

1 . Personalized contents Recommendation

Dable analyzes a visitor’s interests on a real-time basis. After searching for and matching contents within the site that corresponds with the visitor’s interests, Dable recommends appropriate contents tailored to an individual visitor’s interests. For instance, if someone who has a passion for cooking and has recently been interested in cars visits the site, Dable will recommend contents related to “cooking” and “cars.”

2.  Related Articles Recommendation

Contents that are similar to contents that a visitor is currently reading are recommended. By recommending contents that has the highest similarity with contents a visitor is reading based on the body of the contents, it entices the visitor with a particular interest on an issue to stay on the site.

3. Hot articles Recommendation

The most popular contents among the site’s contents on a real-time basis are recommended. By recommending hourly or daily popular contents to a visitor without particular interest, the time spent on site can be increased.

4. Category best articles Recommendation

The most popular article within the category that is being read by a visitor is recommended. For instance, a visitor reading contents in the politics category is recommended the most popular contents within the politics category, while a visitor reading contents in the economy category is recommended the most popular contents within the economy category.

5. Popular by gender Recommendation

Most popular contents by gender are recommended. If a visitor reading a certain content is presumed to be female, contents that are widely read by females are recommended, and vice versa for male. Using tab-based widgets, article tabs that are popular with females are provided first for presumed females, while additional tabs provide contents that are popular with males.

6. Popular by age Recommendation

Contents that are most popular by age group are recommended. Tabs are categorized into Younger than 20s, 30s, 40s and Older than 50s with “Popular by age” to allow browsing of diverse contents, as well as providing “Popular by target age” to show popular contents for an appropriate age group.

7. Perused articles Recommendation

An algorithm that recommends contents with the highest rate of perusal by a visitor within a site. It recommends contents with the greatest contributions to a visitor’s time of stay on a website. The recommended item is selected by comprehensively considering the number of visitors, length of the article and the time of stay.