4 research outputs found
Ultra accurate collaborative information filtering via directed user similarity
A key challenge of the collaborative filtering (CF) information filtering is
how to obtain the reliable and accurate results with the help of peers'
recommendation. Since the similarities from small-degree users to large-degree
users would be larger than the ones opposite direction, the large-degree users'
selections are recommended extensively by the traditional second-order CF
algorithms. By considering the users' similarity direction and the second-order
correlations to depress the influence of mainstream preferences, we present the
directed second-order CF (HDCF) algorithm specifically to address the challenge
of accuracy and diversity of the CF algorithm. The numerical results for two
benchmark data sets, MovieLens and Netflix, show that the accuracy of the new
algorithm outperforms the state-of-the-art CF algorithms. Comparing with the CF
algorithm based on random-walks proposed in the Ref.7, the average ranking
score could reach 0.0767 and 0.0402, which is enhanced by 27.3\% and 19.1\% for
MovieLens and Netflix respectively. In addition, the diversity, precision and
recall are also enhanced greatly. Without relying on any context-specific
information, tuning the similarity direction of CF algorithms could obtain
accurate and diverse recommendations. This work suggests that the user
similarity direction is an important factor to improve the personalized
recommendation performance.Comment: 6 pages, 4 figure
Evolution properties of online user preference diversity
Detecting the evolution properties of online user preference diversity is of significance for deeply understanding online collective behaviors. In this paper, we empirically explore the evolution patterns of online user rating preference, where the preference diversity is measured by the variation coefficient of the user rating sequence. The statistical results for four real systems show that, for movies and reviews, the user rating preference would become diverse and then get centralized finally. By introducing the empirical variation coefficient, we present a Markov model, which could regenerate the evolution properties of two online systems regarding to the stable variation coefficients. In addition, we investigate the evolution of the correlation between the user ratings and the object qualities, and find that the correlation would keep increasing as the user degree increases. This work could be helpful for understanding the anchoring bias and memory effects of the online user collective behaviors