Recommenders Versions Save

Best Practices on Recommendation Systems

1.1.1

1 year ago

New algorithms or improvements

Reduce iterations of W&D to reduce the integration tests time in https://github.com/microsoft/recommenders/pull/1698
Implementation of most frequent recommendation in https://github.com/microsoft/recommenders/pull/1666
Implement time_now for sarplus in #1719 #1721
Add a fast failure in SAR+ if the similarity metric is not within the options in https://github.com/microsoft/recommenders/pull/1743
SAR item similarity dtype correction in https://github.com/microsoft/recommenders/pull/1751
Simplify SAR test data loading functions in https://github.com/microsoft/recommenders/pull/1752
Reformat SAR+ SQL queries in https://github.com/microsoft/recommenders/pull/1772
Add new item similarity metrics for SAR in https://github.com/microsoft/recommenders/pull/1754

New utilities or improvements

Rewrite get_top_k_items() to improve runtime in https://github.com/microsoft/recommenders/pull/1748
Optimized Spark recall_at_k time performance in https://github.com/microsoft/recommenders/pull/1796

New notebooks or improvements

Fix missing import in FastAI notebook https://github.com/microsoft/recommenders/pull/1708
Review NCF notebook in #1703 #1712
Review LigthFM notebook and add test in https://github.com/microsoft/recommenders/pull/1706
Review BPR notebook in https://github.com/microsoft/recommenders/pull/1704
Review LightGCN notebook in https://github.com/microsoft/recommenders/pull/1714
Review DKN notebook in https://github.com/microsoft/recommenders/pull/1722
Review SAR notebook #1738 #1768

Other features

Enable distributed tests with AzureML #1696 #1717 #1729 #1733 #1739 #1747 #1732 #1755 #1763 #1771 #1773 #1775 #1787 #1788 #1794
Added tests for Python 3.8 and 3.9 in https://github.com/microsoft/recommenders/pull/1756
Image of contributors in https://github.com/microsoft/recommenders/pull/1692
Update README.md in #1709 #1711 #1767
Error in codeowners file in https://github.com/microsoft/recommenders/pull/1699
Add test to check if CuDNN is enabled in https://github.com/microsoft/recommenders/pull/1715
Update docker image reference to internal registry in https://github.com/microsoft/recommenders/pull/1727
Fixed a link error in data_transform.ipynb in https://github.com/microsoft/recommenders/pull/1736
Added tests for ranking function get_top_k_items() in https://github.com/microsoft/recommenders/pull/1757
Fix memory error in CPU nightly workflow in https://github.com/microsoft/recommenders/pull/1759
Update test infrastructure explanation #1776 #1777
Added time performance tests in https://github.com/microsoft/recommenders/pull/1765
Add path filter to avoid triggering unit tests when we change a markdown in https://github.com/microsoft/recommenders/pull/1791

Full Changelog: https://github.com/microsoft/recommenders/compare/1.1.0...1.1.1

1.1.0

2 years ago

New algorithms or improvements

SASRec and SSEPT in Tensorflow 2.x in https://github.com/microsoft/recommenders/pull/1530 #1621 #1678
RBM Code Cleanup, model save and other additions in #1599 #1618 #1622
Overwrite older test file in NCF deep dive to avoid bug in https://github.com/microsoft/recommenders/pull/1674
SAR+ improvement and bug fixes #1636 #1644 #1680 #1671
NCF improvement and bug fixes in #1612
Remove drop_duplicates() from SAR method fix #1464 in https://github.com/microsoft/recommenders/pull/1588
SAR literal fix in https://github.com/microsoft/recommenders/pull/1663

New utilities or improvements

Update lightfm_utils.py in https://github.com/microsoft/recommenders/pull/1624
Change formats of user_ids and item_ids arg. in LigthFM in https://github.com/microsoft/recommenders/pull/1651
Fix randomness issue in spark_stratified_split() in https://github.com/microsoft/recommenders/pull/1654
Clarification for jaccard and lift similarity measures in https://github.com/microsoft/recommenders/pull/1668
Use numpy divide in explained variance in https://github.com/microsoft/recommenders/pull/1691
Change MovieLens URL from HTTP to HTTPS in https://github.com/microsoft/recommenders/pull/1677
Remove casting of user and item IDs in Spark evaluation in https://github.com/microsoft/recommenders/pull/1686
Persist intermediate data to avoid non-determinism caused by Spark lazy random evaluation in https://github.com/microsoft/recommenders/pull/1676 #1652

New notebooks or improvements

Fix notebook build failure on Spark 3.2 in https://github.com/microsoft/recommenders/pull/1608
Remove early stopping round from LightGBM example notebook in https://github.com/microsoft/recommenders/pull/1620

Other features

Enable Python 3.8 and 3.9 in https://github.com/microsoft/recommenders/pull/1626 #1617
Upgrade Python from 3.6 to 3.7 in ADO tests pipeline in https://github.com/microsoft/recommenders/pull/1627
Increase time out for GPU nightly tests in https://github.com/microsoft/recommenders/pull/1623
Lower LightGBM test AUC base value in https://github.com/microsoft/recommenders/pull/1619
Change timeouts for tests #1625 #1661 #1684
Scenario gaming in https://github.com/microsoft/recommenders/pull/1637
Limiting tests: reducing the time of the news recommendation GPU notebooks in https://github.com/microsoft/recommenders/pull/1656
Remove pydocumentdb in install_requires in https://github.com/microsoft/recommenders/pull/1629
Change and improve dependencies #1630 #1653
Fix Spark tuning test in https://github.com/microsoft/recommenders/pull/1635
Typos in markdown files and other files #1639 #1589 #1646 #1647 #1688
Update Dockerfile in https://github.com/microsoft/recommenders/pull/1645
Improve documentation #1648 #1669 #1682 #1690 #1672
Codecov Fix in https://github.com/microsoft/recommenders/pull/1665
Set Spark env variables in nightly test in https://github.com/microsoft/recommenders/pull/1655 #1659

Full Changelog: https://github.com/microsoft/recommenders/compare/1.0.0...1.1.0

1.0.0

2 years ago

Backwards incompatible changes

TensorFlow upgrade to 2.6.1 / 2.7 #1574 , #1565 , #1540

New algorithms or improvements

Improve algos visibility #1542
LightGBM test improvement #1531
Fix Surprise and Python 3.7 #1540
TF-IDF runtime enhancement changes #1571
Add Spark 3.x support for SARplus #1566

New utilities or improvements

Upgrade to Spark v3 #1555 , #1549 , #1543
Move scikit-surprise and pymanopt from setup.py #1602
Issue with pymanopt #1606

New notebooks or improvements

Fix bugs in RBM notebooks #1581
Remove explicit mapping of ratings to integers from RBM notebooks #1585

Other features

Fix nightly workflows #1576 , #1548
Stabilize more flaky tests #1558
Miscellaneous Pipeline Fixes #1545
Optimize Notebook Unit Tests #1538
Development status change to production/stable #1579
Update dependencies #1569, #1570
Fix Databricks installation script #1531
Adding codespace deployment #1521
Improve GitHub tests #1518, #1578, #1590, #1592
Flake8 Fixes #1552 , #1550
Improvement in documentation #1591, #1598, #1594, #1603
Update release pipeline #1596

0.7.0

2 years ago

Backwards incompatible changes

Renaming of folders #1485, #1478
Change of the PyPI package name to recommenders #1477

New algorithms or improvements

Missing import in VAE #1508

New utilities or improvements

retrying import #1487
Addition of diversity, novelty, coverage and serendipity metrics #1536, #1535, #1522, #1505, #1491, #1470, #1465

New notebooks or improvements

New notebook showcasing diversity, novelty, coverage, and serendipity metrics in Spark #1488, #1470, #1465

Other features

Enablement of LightGBM version 3 #1527
Enablement of all Python 3.7 micro versions #1474
Installation in virtualenv and venv #1520, #1476
Installation from PyPI in docker container #1509
Read the Docs builds #1529, #1528
Documentation improvements #1515, #1469, #1462
CI pipelines on GitHub workflows (WIP) #1517, #1503, #1499, #1494, #1490

0.6.0

2 years ago

New utilities or improvements

Fix URL in unit tests #1447
Improve documentation #1446 #1440 #1436 #1428 #1426 #1425 #1415
Add retry to maybe_downlad function #1427

New notebooks or improvements

Notebook for diversity metrics #1416
Update evaluation notebook with new diversity metrics #1416
Fix xlearn notebook #1427

Other features

Generate package for PyPi #1445 #1442 #1441 #1429
Improve installation process #1455 #1431
Fix tests #1452 #1427
Generate pipeline for release #1427

0.5.0

2 years ago

Repo structure

Default branch renamed from master to main #1284 #1278

New dataset and competition support

Microsoft News dataset (MIND) and Microsoft News Recommendation Challenge #1247 #1236

New algorithms or improvements

Optimize GPU usage of news recommendation algorithms #1235
Optimize surprise utilities #1224
GeoIMC algorithm #1204
Standard VAE algorithm #1194
Multinomial VAE algorithm #1194

New utilities or improvements

Operationalization example for sequential models #1254
Fix bug with fastai #1288
Fix bug in affinity matrix #1243
Fix conflict with MMLSpark version #1230
Fix negative feedback smapler #1200

New notebooks or improvements

Update AzureML Designer notebooks #1286 #1253
KDD2020 tutorial: paper recommendation with Microsoft Academic Graph #1208
Update o16n notebook for real time scoring #1176
Reduce verbosity on tensorflow notebooks #1276

Other features

Upgrade papermill and scrapbook for testing #1271 #1270 #1282 #1289
Fix tests #1244 #1242 #1226 #1218
Fix issue with spark installation #1186
Update python version #1202
Notice for java dependency #1209
Reactivate CICD pipelines #1284

0.4.0

2 years ago

New algorithms or improvements

DKN fix https://github.com/microsoft/recommenders/pull/1165
GeoIMC https://github.com/microsoft/recommenders/pull/1142
LSTUR #1137 #1080
NAML #1137 #1080
NPA #1137 #1080
NRMS #1137 #1080
LighGCN #1130 #1123
NextItNet #1130 #1126
Fix SAR #1128 #1023 #1018 #991
LightFM #1096
TFIDF recommender #1088
A2SVD #1010
GRU4Rec #1010
Caser #1010
SLi-Rec #1010
SARplus #955
BPR with cornac library #950 #944 #937

New utilities or improvements

MIND dataset https://github.com/microsoft/recommenders/pull/1153
Fix Text iterator https://github.com/microsoft/recommenders/pull/1133
Fix NNI utils #1131
Azure Designer dependencies #1115 #1101 #1095 #1077 #1060
Fix tests #1057 #1004 #954 #935 #932

New notebooks or improvements

DKN notebook with MIND dataset https://github.com/microsoft/recommenders/pull/1165 https://github.com/microsoft/recommenders/pull/1137
GeoIMC notebook https://github.com/microsoft/recommenders/pull/1142
LSTUR notebook #1137 #1080
NAML notebook #1137 #1080
NPA notebook #1137 #1080
NRMS notebook #1137 #1080
LighGCN notebook #1130 #1123
NextItNet notebook #1130 #1126
Implementation of Recommenders into Azure Designer #1115 #1101 #1095 #1060 #1036
NCF hyperparameter tunning notebook #1102 #1092
LightFM notebook #1096
TFIDF recommender notebook #1088
Add timer class into notebooks 1063
Fix xlearn notebook #1006 #974
o16n notebook fix #1003 #969
A2SVD notebook #1010
GRU4Rec notebook #1010
Caser notebook #1010
SLi-Rec notebook #1010
BPR with cornac notebook #950 #944 #937

Other features

Fix installation on Databricks https://github.com/microsoft/recommenders/pull/1161 #965
Fix docker https://github.com/microsoft/recommenders/pull/1146 #1120 #1070 #1058 #1034
Fix Azure blob version #1119
Pin TensorFlow #1098
Code structure refactor #1086
Business scenarios and glossary #1086
ADO artifact #1069
Avoid pandas>1 #1052
CICD #1002 #998 #994 #980

0.3.1

2 years ago

New algorithms or improvements

Improved SAR performance #914 #922
Utils for wikidata knowledge graph #881 #902

New utilities or improvements

Fixed bug in python evaluator #863
Updated nni version and utils #856
Updated sum check #874
Changed url download util to use requests #813

New notebooks or improvements

Optimized spark notebooks #864
New notebook on knowledge graph generation with wikidata #881 #902
Wide-deep hyperdrive notebook AzureML API update #847

Other features

Added Docker support (Docker file) for all of the three (CPU/GPU/Spark) environment
Added setup.py for pip installation #851
Added sphinx documentation #859
Published documentation on readthedocs #912
Fixed spark testing issues #850
Added tests with AzureML compute target #848 #846 #839 #823
Development of Xamarin app for movies recommendation using Recommenders engine https://github.com/microsoft/recommenders_engine_example_layout

0.3.0

2 years ago

New platform support

Windows support with tests #797 #726

New algorithms or improvements

LightGBM #633 #735
RLRMC #729
Changed seed for GPU algos for reproducibility #785 #748
Added benchmark #715
Fixed bugs in SAR #697 #619

New utilities or improvements

Python evaluation improvement by memoization #713
Improved tests #706
New algos for hyperparameter tuning with NNI #687
Criteo dataloader #642
Wrapper VW #592
Added more data formats #605
New metrics #580

New notebooks or improvements

SAR remote execution through AzureML #728
SAR remote execution of notebook through AzureML #681
LightGBM with small criteo on CPU #633
LightGBM o16n on Databricks with MMLSpark #735 #714 #682 #680
Hyperparameter tuning with NNI on Surprise SVD #687
Hyperparameter tuning with Hyperdrive #546

Other features

Fixed bugs in utilities, tests and notebooks
New unit, smoke and integration tests for the new algos

0.2.0

2 years ago

New Algorithms or improvements

Vowpal Wabbit (VW) https://github.com/Microsoft/Recommenders/pull/452
xDeepFM https://github.com/Microsoft/Recommenders/pull/453
DKN https://github.com/Microsoft/Recommenders/pull/453
NCF https://github.com/Microsoft/Recommenders/pull/392
RBM https://github.com/Microsoft/Recommenders/pull/390
FastAI Embedding dot Bias https://github.com/Microsoft/Recommenders/pull/411
Optimization of SAR

New utilities or improvements

Improved the performance of python splitters https://github.com/Microsoft/Recommenders/pull/517
Added GPU utilities
Added utilities for hyperparameter tuning

New Notebooks or improvements

Improved o16n notebook with ALS, Movielens and Databricks https://github.com/Microsoft/Recommenders/pull/475
Added a deep dive notebook on VW https://github.com/Microsoft/Recommenders/pull/452
Improved notebook for hyperparameter tuning on Spark https://github.com/Microsoft/Recommenders/pull/444
New notebook on FastAI Embedding dot Bias algo https://github.com/Microsoft/Recommenders/pull/411
New notebook of deep dive on NCF https://github.com/Microsoft/Recommenders/pull/392
New quick start notebook of RBM https://github.com/Microsoft/Recommenders/pull/390
New deep dive notebook of RBM https://github.com/Microsoft/Recommenders/pull/390
New quickstart notebook of xDeepFM with synthetic data
New quickstart notebook of DKN with synthetic data
New notebook on data transformation https://github.com/Microsoft/Recommenders/pull/384

Other features

Fixed bugs in utilities, tests and notebooks
Added an installation script for Databricks https://github.com/Microsoft/Recommenders/pull/457
Changed installer from a bash to a python script https://github.com/Microsoft/Recommenders/pull/512
Added a parameter to control pyspark version in the installer https://github.com/Microsoft/Recommenders/pull/461
Optimized tests to be quicker https://github.com/Microsoft/Recommenders/pull/486
New unit, smoke and integration tests for the new algos
Added GPU test pipeline https://github.com/Microsoft/Recommenders/pull/408
Improved Github metrics tracker https://github.com/Microsoft/Recommenders/pull/400