1. ParaRec Final Report


  2. CUDA-based Approximate Nearest Neighbor Search with Locality Sensitive Hashing


  3. Data Compression


    One of the biggest challenge for improving the performance of a recommendation engine is how to reduce the data access time. By the nature of collaborative filtering algorithm, the data access pattern is very random. What’s more, since the user rating matrix is very sparse, multiple memory loads are very likely accessing very different addresses. Therfore, a compressed data structure that exploits memory locality will be extremely helpful. …

  4. CUDA Optimization

    Cuda Implementation of Collaborative Filtering

  5. Checkpoint 1

    Instead of focusing on collaborative filtering then matrix factorization, we decided to implement both serial versions so that we can parallelize our work after the checkpoint. Here is a list of things we have already finished:

  6. ParaRec Project Proposal