Big Data

Yelp User Analysis
With the rise of deep learning and machine learning, many retailers adopt recommendation systems to increase their competitive ability in the market. Yelp platform has published an extensive dataset about its user and business profiles (around 9 GB). Many researchers have explored the dataset, but few of them focus on friend recommendations with users. In this project, the k-hop sub-graph or the ego-net of one specific user will be analyzed to provide diverse recommendations
Code
Yelp User Analysis
Music Recommendation with Spark

This is a project for music recommendations with Spark.

  • Accelerated parallel breadth-first search with Spark 10x faster than that with MapReduce in the self-deployed cluster after compressing the dataset with a 2% ratio by Apache Avro.
  • Employed the PageRank algorithm within ego-nets to generate divers.
Music Recommendation with Spark