In this paper we propose a survival factorization framework that models information cascades by tying together social influence patterns, topical structure and temporal dynamics. This is achieved through the introduction of a latent space which …
The development of platforms and techniques for emerging Big Data and Machine Learning applications requires the availability of real-life datasets. A possible solution is to synthesize datasets that reflect patterns of real ones using a two-step …
In what follows I'll try to explain my basic understanding and interepretation of the semi-supervised framework based on Variational Autoencoders, as described in [1]. I shall assume a vector notation where bold symbols $\mathbf{a}$ represent vectors, whose $j$-th component can be represented as $a_j$.
The starting point of the framework is to consider a dataset \(D = S \cup U\), where:
\(S = \{(\mathbf{x}_1, \mathbf{y}_1), \ldots, (\mathbf{x}_n, \mathbf{y}_n)\}\),
\(U = \{\mathbf{x}_{n+1}, \ldots, \mathbf{x}_{n+m}\}\), with \(\mathbf{x}_i \in \mathbb{R}^N\) and \(\mathbf{y}_i \in \{0,1\}^C\) represents a one-hot encoding of a class in \(\{1, \ldots, C\}\).
Variational autoencoders were proven successful in domains such as computer vision and speech processing. Their adoption for modeling user preferences is still unexplored, although recently it is starting to gain attention in the current literature. …
In this paper we propose a survival factorization framework that models information cascades by tying together social influence pat- terns, topical structure and temporal dynamics. This is achieved through the introduction of a latent space which …
We study the problem of detecting social communities when the social graph is not available but instead we have access to a log of user activity, that is, a dataset of tuples (u, i, t) recording the fact that user u “adopted” item i at time t. We …
The importance of accurate recommender systems has been widely recognized by academia and industry, and recommendation is rapidly becoming one of the most successful applications of data mining and machine learning. Understanding and predicting the …
User recommender systems are a key component in any on-line social networking platform: they help the users growing their network faster, thus driving engagement and loyalty. In this paper we study link prediction with explanations for user …
How can we detect communities when the social graphs is not available? We tackle this problem by modeling social contagion from a log of user activity, that is a dataset of tuples (u, i, t) recording the fact that user u "adopted" item i at time t. …