Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Using twitter to recommend real-time topical news
Proceedings of the third ACM conference on Recommender systems
Ranking mechanisms in twitter-like forums
Proceedings of the third ACM international conference on Web search and data mining
TwitterRank: finding topic-sensitive influential twitterers
Proceedings of the third ACM international conference on Web search and data mining
Short and tweet: experiments on recommending content from information streams
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Time is of the essence: improving recency ranking using Twitter data
Proceedings of the 19th international conference on World wide web
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Micro-blog's handiness is besieging users with overloaded short snippets of tweets surging into their page. How to evaluate quality of tweets with limited content and rank them to direct user attention is a new significant topic. In this paper, we study the problem of user-specific tweet evaluation and ranking. We propose a comprehensive, personalized tweet ranking mechanism (Tweet Rank) with a technique of AHP (Analytic Hierarchy Process) in operational research. Based on mathematics and psychology, the AHP can quantify the weight of each impact factor and model user blur preference precisely. Case study in Chinese micro-blog platform of T.sina showed that Tweet Rank greatly outperformed time-based ranking used in T.Sina, improving percentage of interesting content in Top10 to 60% from 20%.