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Contribution Details
| Type | Conference Presentation |
| Scope | Contributions to practice |
| Title | Bringing Diversity in News Recommender Algorithms |
| Organization Unit | |
| Authors |
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| Presentation Type | other |
| Item Subtype | Original Work |
| Refereed | No |
| Status | Published in final form |
| Language |
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| Event Title | ECREA 2018 - pre-conference workshop on Information, Diversity and Media Pluralism in the Age of Algorithms |
| Event Type | workshop |
| Event Location | Lugano, Switzerland |
| Event Start Date | October 31 - 2018 |
| Event End Date | October 31 - 2018 |
| Abstract Text | In major political events and discussions, recommender algorithms play a large role in shaping opinions and setting the agenda for more traditional news media. These algorithms are used pervasively in social networks, media platforms and search engines. They determine what information is shown to the user and in which order. Existing recommender systems focus on improving accuracy based on historic interaction-data, which has received criticism for being detrimental to the goals of improving user experience and information diversity. Their accuracy is measured in terms of predicting future user behavior based on past observation. The most popular algorithms suggest items to users based on the choices of similar users. However, these systems are observed to promote a narrow set of already-popular items that are similar to past choices of the user, or are liked by many users. This limits users' exposure to diverse viewpoints and potentially increases polarization. One reason for the lack of diversity in existing approaches is that they optimize for average accuracy and click rates. There are other aspects of user-experience like new information and di ering viewpoints, that are not taken into account. Similarly, a high average accuracy does not necessarily mean that the algorithm is similarly accurate for di erent groups of users and niche items. Besides this, training data may be biased because of previous recommendations, or the data collection process. Although information diversity is important, its de nition depends heavily on the speci c application and it is hard to nd a general de nition in the context of recommender systems. Nevertheless, there are de nitions that pertain to some speci c aspects of diversity, such as entropy, coverage, personalization, and surprisal. There is a need to design recommender algorithms that balance accuracy and diversity in order to deal with the aforementioned problems. To approach this problem, we rst investigated state-of-the-art algorithms and found that they promote already popular products. We also found that recommender diversity depends on the optimization parameters of the algorithms, a factor that had not received enough attention before. In this context, we developed a graph- based method that promotes niche items without sacri cing accuracy. We also developed a probabilistic latent-factor model that signi cantly improves the coverage and long-tail diversity of recommendations. Currently, we are using these insights to develop new machine learning algorithms for diverse news recommendation. We plan to and deploy and test them in a large-scale experiment involving multiple news producers and consumers. |
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