원문정보
초록
영어
The rapid growth in the popularity of social networking and microblogging has led to a new way of finding and broadcasting information in the recent years. The real-time microblog filtering emerges as the times require. The task of real-time microblog filtering is to decide if subsequently posted tweets are relevant to a given query which represents the special information needs. One-side feedback is one of the most difficult problems in microblog filtering. This paper focuses on exploiting the time profile of relevant microblogs to address this problem. A temporal microblog filtering based on retrieval model is proposed. Specifically, similarity threshold achieved by the language model is adjusted according to temporal burst. Evaluated on the TREC 2012 microblog real-time filtering track dataset, the experimental results show that the proposed model is significantly better than several baselines.
목차
1. Introduction
2. Related Work
2.1. Real-Time Microblog Filtering Task
2.2. Microblog Filtering Based on Retrieval Model
3. Temporal Microblog Filtering Model
3.1. Temporal Microblog Filtering Model Framework
3.2. Estimating γ(t)
3.3. Estimating θq
3.4. Estimating θm
3.5. Estimating Sim (θq,θm)
4. Experiments
4.1. Datasets and Settings
4.2. Measures
4.3. Parameters Setting
4.4. Baselines
4.5. Experiment Results
5. Conclusions
References