Abstract 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 …
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 …
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. …
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 . …
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 …
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.
Given a directed social graph and a set of past informa- tion cascades observed over the graph, we study the novel problem of detecting modules of the graph (communities of nodes), that also explain the cascades. Our key observation is that both …