87,283 research outputs found
A Simple Generative Model of Collective Online Behaviour
Human activities increasingly take place in online environments, providing
novel opportunities for relating individual behaviours to population-level
outcomes. In this paper, we introduce a simple generative model for the
collective behaviour of millions of social networking site users who are
deciding between different software applications. Our model incorporates two
distinct components: one is associated with recent decisions of users, and the
other reflects the cumulative popularity of each application. Importantly,
although various combinations of the two mechanisms yield long-time behaviour
that is consistent with data, the only models that reproduce the observed
temporal dynamics are those that strongly emphasize the recent popularity of
applications over their cumulative popularity. This demonstrates---even when
using purely observational data without experimental design---that temporal
data-driven modelling can effectively distinguish between competing microscopic
mechanisms, allowing us to uncover new aspects of collective online behaviour.Comment: Updated, with new figures and Supplementary Informatio
A probabilistic model to resolve diversity-accuracy challenge of recommendation systems
Recommendation systems have wide-spread applications in both academia and
industry. Traditionally, performance of recommendation systems has been
measured by their precision. By introducing novelty and diversity as key
qualities in recommender systems, recently increasing attention has been
focused on this topic. Precision and novelty of recommendation are not in the
same direction, and practical systems should make a trade-off between these two
quantities. Thus, it is an important feature of a recommender system to make it
possible to adjust diversity and accuracy of the recommendations by tuning the
model. In this paper, we introduce a probabilistic structure to resolve the
diversity-accuracy dilemma in recommender systems. We propose a hybrid model
with adjustable level of diversity and precision such that one can perform this
by tuning a single parameter. The proposed recommendation model consists of two
models: one for maximization of the accuracy and the other one for
specification of the recommendation list to tastes of users. Our experiments on
two real datasets show the functionality of the model in resolving
accuracy-diversity dilemma and outperformance of the model over other classic
models. The proposed method could be extensively applied to real commercial
systems due to its low computational complexity and significant performance.Comment: 19 pages, 5 figure
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