Personalized news recommendation based on click behavior

By Jiahui Liu, Peter Dolan, Elin Rønby Pedersen. 2010

858 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Xing Xie
2. Jianfeng Gao
3. Mohit Bansal
4. Fangzhao Wu
5. Chuhan Wu
6. Jiawei Han
7. Weinan Zhang
8. Fuzheng Zhang

In Information Systems

1. Xing Xie
2. Fangzhao Wu
3. Chuhan Wu
4. Yongfeng Huang
5. Dietmar Jannach
6. Tao Qi
7. Xiangnan He
8. Fuzheng Zhang

Citations in the corpus

• DRN: A Deep Reinforcement Learning Framework for News Recommendation (2018, methodology): “The current methods can be generally categorized as content based methods [19, 22, 33], collaborative filtering based methods [11, 28, 34], and hybrid methods [12, 24, 25].”
• DKN: Deep Knowledge-Aware Network for News Recommendation (2018, background): “To alleviate the impact of information over-loading, it is critical to help users target their reading interests and make personalized recommendations [2, 25, 27, 32, 34, 39].”
• Offline and online evaluation of news recommender systems at swissinfo.ch (2014, background): “This led us to several open questions: Which recommender systems should be deployed in practice? Do CT recommender systems improve the clickthrough rate over baseline methods? Do recommender systems increase the time spent by the users on the news website? Is there a major difference between offline”
• Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions (2015, background): “Content-based news recommendation systems [52] , [53] require the topical analysis of news articles to recommend interesting news for users.”
• Controlling Polarization in Personalization: An Algorithmic Framework (2019, background): “Content selection algorithms consider a user’s properties and past behavior in order to produce a personalized list of content to display [21, 27].”
• The use of machine learning algorithms in recommender systems: A systematic review (2015, background): “Today, RSs are used in many information-based companies such as Google [35], Twitter [4], LinkedIn [50], and Netflix [59].”
• Content Driven User Profiling for Comment-Worthy Recommendations of News and Blog Articles (2015, methodology): “Thus, traditional collaborative filtering (CF) methods [8, 9] used to recommend articles for viewing [10] are not very effective in recommending articles to users for commenting.”
• Sequence-Aware Recommender Systems (2018, background): “other people, due to exceptional events, or when they discover something new. In the news domain, for example, individual interests change over time and are influenced by global and local news trends [75]. Another example problem is the task of modeling the dynamics of the musical taste of users [85”
• Evaluation of session-based recommendation algorithms (2018, background): “Finally, we also report coverage and popularity statistics for each algorithm.”
• A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity (2017, background): “This survey does not cover work related to learning in dynamic environments that do not have other active autonomous and automated agents, such as recommender systems of news articles (Liu et al., 2010) or online supervised learning scenarios (see Section 6.4).”
• Unbiased Recommender Learning from Missing-Not-At-Random Implicit Feedback (2019, background): “rity of the proposed approach against state-of-the-art recommendation algorithms. 1 Introduction Recommender systems are widely used in industries such as personalized advertising and video streaming [2, 8, 9]. These services utilize recommender systems to predict and provide users with items that t”
• Fair Personalization (2017, background): “Content selection algorithms take data and other information as input, and – given a user’s properties and past behavior – produce a personalized list of content to display [3, 11, 13, 17] (see Figure 1).”
• NPA: Neural News Recommendation with Personalized Attention (2019, methodology): “Existing news recommendation methods are usually based on collaborative filtering (CF) techniques and news content[20–22, 24].”