Hivemall v0.4.2-rc.1

This is the first release candidate for Hivemall v0.4.2. We skipped v0.4.1 since there are tremendous changes in this release.

We would like to thank all contributors of this release.

Caution: FFM and BPRMF implementations are still experimental and subject to change (so not documented). We DO NOT recommend to use this version in production yet.

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Changes since v0.4.1-alpha.6 are summarized as follows:

• Major Enhancement
  • Initial support for Hivemall on Spark #274
  • Implemented Field-aware Factorization Machines #284 #286 #288 #290 #292 #293 #295 #297
  • Implemented BPR-MF (Matrix Factorization for Implicit Feedbacks) #278 [d427bd8]
    • Steffen Rendle, Christoph Freudenthaler, Zeno Gantner, Lars Schmidt-Thieme. "BPR: Bayesian Personalized Ranking from Implicit Feedback", Proc. UAI, 2009.
• Minor Enhancement
  • Added bits_collect UDAF [c78b75b][117474f]
  • Added to_bits and unbits UDF [4da12d5]
  • Added bits_or UDF [42f3db2]
    • Bitset functions usage
  • Added ndcg UDAF [4340194]
  • Added array_intersect UDF [cb7f0f7]
  • Added logloss Logarithmic Loss UDAF to evaluation functions #289
  • Added feature_hashing UDF #300
• Major Change
  • Changed the default learning rate 'eta0' of Factorization Machines [24fc383]
  • Changed the default max_init_value option value of FM/FFM [208ed62]
  • Changed the default V initialization scheme for FM/FFM classification from random to guassian [8ec5199]
• Minor Change
  • Renamed concat_array UDF to array_concat [f9561d6]
  • Fixed each_top_k to accept non-constant k for the first argument [2f18011]
  • Fixed to_order_map to support reverseOrder option [ac005fd]
  • Fixed extract_weight UDF to support FFM format [f954bad]
• Bug Fixes
  • Fixed a bug in model_id generation scheme of RandomForest #299