Social media marketing through time-aware collaborative filtering

Social media is assuming a crucial role in purchasing decisions, and most companies are using social media for marketing. Making sense of the unstructured information content shared by users along the time is an emerging challenge. We advise that the dynamic nature with which trends and user's interests evolve along the timeline requires the revising of well-assessed methods to address, for instance, recommendation provisioning, information retrieval, and so on. Time-awareness is crucial to more effectively estimate user's interests in the future to better address social media marketing attempting to increase the traction, for instance, posting the right message at the right time. This work defines time-aware collaborative filtering for estimating users' interest along the time in Twitter. It uses text analysis services to semantically annotate tweets' content and to track concepts considering post frequencies along the time. A model-based approach implementing K-Nearest Neighbors is used to estimate user's similarity representing their profile by sampling user's interest with three different techniques: Vectorial Representation, Symbolic Aggregation Approximation, and Median. We show the experimental results comparing these techniques and performing model training in different time windows. The proposed approach is used to address some social media marketing questions.