شماره ركورد كنفرانس :
144
عنوان مقاله :
Analiysis of Self-Similarity in Recommender Systems
پديدآورندگان :
Hosseinzadeh Aghdam Mehdi نويسنده , Analoui Mortaza نويسنده Department of Computer Engineering, Iran University of Science and Technology. Tehran, Iran. , Kabiri Peyman نويسنده
كليدواژه :
self-similarity , rating sequence , Hurst Factor , Recommender System
عنوان كنفرانس :
مجموعه مقالات دوازدهمين كنفرانس سيستم هاي هوشمند ايران
چكيده فارسي :
The objective of recommender systems is to estimate
the unknown ratings. This paper presents an efficient method to
generate the self-similarity rating matrix for recommender
systems. We show that the rating behavior of users is statistically
self-similar that none of the commonly used recommender system
models is able to detect this fractal behavior. This behavior can
be used to predict the unknown ratings. The experimental results
showed that the proposed method obtains similar accuracy in
comparison to the traditional recommender system method with
much less computational cost.
شماره مدرك كنفرانس :
3817034
