Improving News Ranking by Community Tweets
This addresses the challenge of data sparsity and privacy in personalized news ranking for users in online communities, though it is incremental as it builds on existing social context methods.
The paper tackled the problem of interpreting short, general queries in news search by leveraging users' social contexts from Twitter community data, resulting in a Community Tweets Voting Model (CTVM) that outperformed baseline rankings from Google and Yahoo for certain online communities.
Users frequently express their information needs by means of short and general queries that are difficult for ranking algorithms to interpret correctly. However, users' social contexts can offer important additional information about their information needs which can be leveraged by ranking algorithms to provide augmented, personalized results. Existing methods mostly rely on users' individual behavioral data such as clickstream and log data, but as a result suffer from data sparsity and privacy issues. Here, we propose a Community Tweets Voting Model (CTVM) to re-rank Google and Yahoo news search results on the basis of open, large-scale Twitter community data. Experimental results show that CTVM outperforms baseline rankings from Google and Yahoo for certain online communities. We propose an application scenario of CTVM and provide an agenda for further research.