v1.0.0

Recommendation v1.0.0

Diff since v0.5.0

Closed issues:

• Initial features for implicit feedback recommendation (#1)
• Support BPR loss for matrix factorization (#30)
• Support loading libsvm data format (#32)
• Reorganize unit tests with @testsets (#44)
• Add synthetic user-item interaction generator for experiments (#46)
• Add coverage, diversity, and serendipity metrics for recommendation lists (#53)

Merged pull requests:

• Port takuti/SyntheticImplicitFeedback.jl to synthetic.jl (#47) (@takuti)
• Add load_libsvm_file function to parse libsvm data (#48) (@takuti)
• Refactor testing modules with @testset (#49) (@takuti)
• Benchmark recommenders with fit! optimization and refactoring (#50) (@takuti)
• Unify predict and ranking for simplicity (#51) (@takuti)
• Add simple coverage metric between two lists (#54) (@takuti)
• Add aggregated non-accuracy metrics for diversity and novelty (#55) (@takuti)
• Implement intra-list diversity and serendipity metrics (#57) (@takuti)
• Implement BPR Matrix Factorization recommender (#59) (@takuti)
• Prepare for cross validation-based benchmarking (#60) (@takuti)
• Update/optimize evaluation modules with a benchmark script for testing multiple data-recommender-model pairs (#61) (@takuti)
• Optimize recommend() with bulk prediction (#64) (@takuti)
• Update cross validation interfaces per recent updates on evaluate() (#65) (@takuti)