Search results

Online advertisement brings huge revenue to many websites. There are many types of online advertisement; this paper aims to focus on the online banner ads which are usually placed in a particular news website. The investigated news website adopts a pay-per-ad payment model, where the advertisers are charged when they rent a banner from the website during a particular period. In this payment model, the website needs to ensure that the ad pushed frequency of each ad on the banner is similar. Under such advertisement push rules, an ad-recommendation mechanism considering ad push fairness is required.

The authors proposed a novel ad recommendation method that considers both ad-push fairness and personal interests. The authors take every ad’s exposure time into consideration and investigate users’ three different usage experiences in the website to identify the main factors affecting the interests of users. Online ad recommendation is conducted on the investigated news website.

The results of the experiments show that the proposed approach performs better than the traditional approach. This method can not only enhance the average click rate of all ads in the website but also ensure reasonable fairness of exposure frequency of each ad. The online experiment results demonstrate the effectiveness of this approach.

Existing researches had not considered both the advertisement recommendation and ad-push fairness together. With the proposed novel ad recommendation model, the authors can improve the ad click-through rate of ads with reasonable push fairness. The website provider can thereby increase the commercial value of advertising and user satisfaction.

Online news websites provide diverse article topics, such as fashion news, entertainment and movie information, to attract more users and create more benefits. Recommending movie information to users reading news online can enhance the impression of diverse information and may consequently improve benefits. Accordingly, providing online movie recommendations can improve users’ satisfactions with the website, and thus is an important trend for online news websites. This study aims to propose a novel online recommendation method for recommending movie information to users when they are browsing news articles.

Association rule mining is applied to users’ news and movie browsing to find latent associations between news and movies. A novel online recommendation approach is proposed based on latent Dirichlet allocation (LDA), enhanced collaborative topic modeling (ECTM) and the diversity of recommendations. The performance of proposed approach is evaluated via an online evaluation on a real news website.

The online evaluation results show that the click-through rate can be improved by the proposed hybrid method integrating recommendation diversity, LDA, ECTM and users’ online interests, which are adapted to the current browsing news. The experiment results also show that considering recommendation diversity can achieve better performance.

Existing studies had not investigated the problem of recommending movie information to users while they are reading news online. To address this problem, a novel hybrid recommendation method is proposed for dealing with cross-type recommendation tasks and the cold-start issue. Moreover, the proposed method is implemented and evaluated online in a real world news website, while such online evaluation is rarely conducted in related research. This work contributes to deriving user’s online preferences for cross-type recommendations by integrating recommendation diversity, LDA, ECTM and adaptive online interests. The research findings also contribute to increasing the commercial value of the online news websites.