How Time-Based Recommendation Impacts Viewership

Time-based recommendation algorithms can significantly enhance the service quality of streaming platforms.

FREMONT, CA: Streaming service providers are already leveraging the potential of recommendation algorithms for quite some time now. While there has been a significant improvement in the user experience in the past few years, the recommendation algorithms have also evolved simultaneously. Powerful recommendation algorithms are leveraging a wide range of customer data to offer relevant and personalized content. A time-based recommendation algorithm is a crucial addition to the capabilities of the recommendation algorithms.

A time-based recommendation algorithm has been a major plus for e-commerce as well as streaming business. Apart from discovering the relevant product for individual customers, a time-based recommendation algorithm also serves the content to the users at the most appropriate time. For instance, a user is more likely to see the recommendations of an upcoming series on weekends than during the weekdays. While personalized recommendations are associated with almost every recommendation engine, the data source for a time-based recommendation engine can be obtained by tracking the time when there is maximum user activity.

Major streaming service providers are considering time-based recommendation as a crucial aspect of their recommendation services. For instance, firms are using machine learning (ML) and deep learning to analyze users’ service usage patterns. While most of the users might scroll through the streaming platform during the evening, some of the users might be active in the morning or afternoon. The time-based algorithm will speculate the user time constraints based on his or her usage pattern and trigger recommendations accordingly. While content consumption habits and browsing patterns haven’t been decoded completely by the streaming platform owners, there are various obvious patterns that can be leveraged by the platform owners. For instance, devotional programs are more likely to attract users’ attention during the morning. Thus, time-based recommendation engines can be equipped with obvious viewing patterns.

Time-based recommendation engines are constantly growing in essence as the streaming service providers look to gain an edge in the competitive market space.

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