  Temporal Point Processes (TPPs) are often used to represent the sequence of
events ordered as per the time of occurrence. Owing to their flexible nature,
TPPs have been used to model different scenarios and have shown applicability
in various real-world applications. While TPPs focus on modeling the event
occurrence, Marked Temporal Point Process (MTPP) focuses on modeling the
category/class of the event as well (termed as the marker). Research in MTPP
has garnered substantial attention over the past few years, with an extensive
focus on supervised algorithms. Despite the research focus, limited attention
has been given to the challenging problem of developing solutions in
semi-supervised settings, where algorithms have access to a mix of labeled and
unlabeled data. This research proposes a novel algorithm for Semi-supervised
Learning for Marked Temporal Point Processes (SSL-MTPP) applicable in such
scenarios. The proposed SSL-MTPP algorithm utilizes a combination of labeled
and unlabeled data for learning a robust marker prediction model. The proposed
algorithm utilizes an RNN-based Encoder-Decoder module for learning effective
representations of the time sequence. The efficacy of the proposed algorithm
has been demonstrated via multiple protocols on the Retweet dataset, where the
proposed SSL-MTPP demonstrates improved performance in comparison to the
traditional supervised learning approach.
