A Survey on Knowledge-Aware News Recommender Systems

Tracking #: 2991-4205

Andreea Iana
Mehwish Alam
Heiko Paulheim

Responsible editor: 
Dagmar Gromann

Submission type: 
Survey Article
News consumption has shifted over time from traditional media to online platforms, which use recommendation algorithms to help users navigate through the large incoming streams of daily news by suggesting relevant articles based on their preferences and reading behavior. In comparison to domains such as movies or e-commerce, where recommender systems have proved highly successful, the characteristics of the news domain (e.g., high frequency of articles appearing and becoming outdated, greater dynamics of user interest, less explicit relations between articles, and lack of explicit user feedback) pose additional challenges for the recommendation models. While some of these can be overcome by conventional recommendation techniques, injecting external knowledge into news recommender systems has been proposed in order to enhance recommendations by capturing information and patterns not contained in the text and metadata of articles, and hence, tackle shortcomings of traditional models. This survey provides a comprehensive review of knowledge-aware news recommender systems. We propose a taxonomy that divides the models into three categories: neural methods, non-neural entity-centric methods, and non-neural path-based methods. Moreover, the underlying recommendation algorithms, as well as their evaluations are analyzed. Lastly, open issues in the domain of knowledge-aware news recommendations are identified and potential research directions are proposed.
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Solicited Reviews:
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Review #1
Anonymous submitted on 12/Jan/2022
Review Comment:

The authors have thoroughly implemented all my previous comments.

Review #2
Anonymous submitted on 03/Feb/2022
Review Comment:

The authors have well addressed my concerns raised previously in the current version. I believe that the manuscript is ready for acceptance.

Review #3
By Peter Bloem submitted on 14/Feb/2022
Review Comment:

I have reviewed the paper, and find it again much improved. I hold to my previous position that it should be accepted.

I think the extra paragraph at the start of section 8 is a great addition, that captures the larger problems of these kinds of systems very well.