Recommendation Engine for Course Selection with TigerGraph

Background

An educational organization wanted to improve their participants’ experience by using a recommendation engine for course selection. To efficiently handle the large amount of data and relationships involved in the recommendation process, they chose TigerGraph as their database solution.

Challenges

The educational organization faced multiple challenges, including:

• Managing a large amount of data from multiple sources, such as participant demographics, enrollment history, and course evaluations.
• Personalizing recommendations for each participant based on their preferences and goals.
• Providing real-time recommendations based on the latest data.

Solution

To overcome these challenges, the organization chose TigerGraph for their recommendation engine due to its native graph processing capabilities. With TigerGraph, they managed the large amount of data and relationships involved in the recommendation process effortlessly. They loaded data from different sources into TigerGraph and created a graph model to represent the data.

The recommendation engine was built using TigerGraph’s GSQL query language, which provided a powerful and expressive way to query and analyze the data. Moreover, GSQL allowed the organization to personalize recommendations for each participant based on their preferences and goals. TigerGraph’s real-time indexing and querying capabilities allowed the organization to offer real-time recommendations based on the latest data.

Results

The educational organization implemented a successful recommendation engine for course selection using TigerGraph. The engine delivered personalized and real-time recommendations, improving participants’ course selection experience. Furthermore, TigerGraph’s native graph processing and GSQL made it easy to manage the large amount of data and relationships involved in the recommendation process.

As a result, the organization noticed a significant improvement in participant satisfaction with the course selection process and a rise in participant retention. The recommendation engine identified participants at risk of dropping out and provided personalized recommendations, keeping them on track.

Conclusion

Finally, TigerGraph demonstrated its effectiveness as a solution for the educational organization’s recommendation engine. Its native graph processing and GSQL query language enabled the effortless handling of large amount of data and relationships involved in the recommendation process. In conclusion, the recommendation engine provided personalized and real-time recommendations to participants, improving their course selection experience and increasing participant retention.

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