کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
402566 676965 2010 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A new collaborative filtering metric that improves the behavior of recommender systems
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
A new collaborative filtering metric that improves the behavior of recommender systems
چکیده انگلیسی

Recommender systems are typically provided as Web 2.0 services and are part of the range of applications that give support to large-scale social networks, enabling on-line recommendations to be made based on the use of networked databases. The operating core of recommender systems is based on the collaborative filtering stage, which, in current user to user recommender processes, usually uses the Pearson correlation metric. In this paper, we present a new metric which combines the numerical information of the votes with independent information from those values, based on the proportions of the common and uncommon votes between each pair of users. Likewise, we define the reasoning and experiments on which the design of the metric is based and the restriction of being applied to recommender systems where the possible range of votes is not greater than 5. In order to demonstrate the superior nature of the proposed metric, we provide the comparative results of a set of experiments based on the MovieLens, FilmAffinity and NetFlix databases. In addition to the traditional levels of accuracy, results are also provided on the metrics’ coverage, the percentage of hits obtained and the precision/recall.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 23, Issue 6, August 2010, Pages 520–528
نویسندگان
, , ,