A Co-occurring Rule Mining Approach to Discover Viewers Watching Behavior of OTT Platform

People can watch movies at their convenience from their hectic lives in the present era of web platforms. There are many platforms that offer movie material. Yet, the user finds it quite difficult to search for and select a movie because there are such large movie databases. When I hear the phrase “OTT,” I think back to a time when CD players were still widely used and DVD players were in style. The availability of cable connections, the emergence of the pen drive, and YouTube's prominence as a significant viewing platform all come about gradually as technology improves. With these variations, OTT video streaming is one of the most recent market trends. Users are motivated to sign up for OTT video streaming services due to the availability of material, ease of access to a range of shows, user-friendliness, and consistency of the shows. The current study attempts to look into the connections between OTT platform purchase intention, consumer engagement, brand image, and digital media marketing (DMM) in the Indian context. The discovered data was applied to the Apriori algorithm to identify the co-relation components as well as any undiscovered, intriguing relationships between them. In this work, the hidden patterns of co-occurring relations are extracted with modified-Apriori algorithm and visualization techniques are used to represent the knowledge. This might help in making better use of these digital platforms.