Effectiveness of RSS feed item duplication detection using word matching

Users of feed aggregators know that duplicated articles are found occasionally on the feeds they subscribe to. It can be time consuming to read all articles and stumble upon duplicated items they have already read. Our work here is to determine the effectiveness of using basic word matching to remove duplicated items and only show the most relevant item, thus saving readers’ time. The method described in this paper to remove duplicates involves word matching heuristics with an appropriate matching percentage. The duplicated feeds are then ranked to only display the highest ranked article. Ranking is done using the number of search items found on the titles of the news feeds where the highest number returned will be considered the highest ranked article. Using Malaysian online news feeds, our method found that with a matching percentage of 40%, our method will be able to minimize duplicates effectively with minimal errors. We did further empirical studies using 9 technology blog feeds over a longer period to provide us with a better averaging results. The matching percentage obtained is also within the same quantum. The method described here has a low overhead in terms of processing for the duplicates and with careful selection of matching percentage, the system will effectively remove the majority of duplicate

The Impact of Near-Duplicate Documents on Information Retrieval Evaluation

Near-duplicate documents can adversely affect the efficiency and effectiveness of search engines. Due to the pairwise nature of the comparisons required for near-duplicate detection, this process is extremely costly in terms of the time and processing power it requires. Despite the ubiquitous presence of near-duplicate detection algorithms in commercial search engines, their application and impact in research environments is not fully explored. The implementation of near-duplicate detection algorithms forces trade-offs between efficiency and effectiveness, entailing careful testing and measurement to ensure acceptable performance. In this thesis, we describe and evaluate a scalable implementation of a near-duplicate detection algorithm, based on standard shingling techniques, running under a MapReduce framework. We explore two different shingle sampling techniques and analyze their impact on the near-duplicate document detection process. In addition, we investigate the prevalence of near-duplicate documents in the runs submitted to the adhoc task of TREC 2009 web track

Models and Algorithms for Duplicate Document Detection

This paper introduces a framework for clarifying and formalizing the duplicate document detection problem. Four distinct models are presented, each with a corresponding algorithm for its solution derived from the realm of approximate string matching. The robustness of these techniques is demonstrated through a set of experiments using data reflecting real-world degradation effects. 1