{"id":13532010,"url":"https://github.com/datamllab/AutoRec","last_synced_at":"2025-04-01T20:31:13.785Z","repository":{"id":39739315,"uuid":"214070555","full_name":"datamllab/AutoRec","owner":"datamllab","description":null,"archived":false,"fork":false,"pushed_at":"2023-03-24T22:54:23.000Z","size":37237,"stargazers_count":49,"open_issues_count":3,"forks_count":11,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-03-24T07:55:25.848Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/datamllab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2019-10-10T02:42:13.000Z","updated_at":"2024-05-04T18:41:11.000Z","dependencies_parsed_at":"2024-01-12T17:35:42.124Z","dependency_job_id":"6e780b87-3579-42df-bed9-5aabf5944af9","html_url":"https://github.com/datamllab/AutoRec","commit_stats":{"total_commits":263,"total_committers":7,"mean_commits":37.57142857142857,"dds":0.6197718631178708,"last_synced_commit":"2dbc8778cfb597402d8b0337186bf9152663b20a"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datamllab%2FAutoRec","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datamllab%2FAutoRec/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datamllab%2FAutoRec/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/datamllab%2FAutoRec/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/datamllab","download_url":"https://codeload.github.com/datamllab/AutoRec/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246709923,"owners_count":20821297,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-01T07:01:07.580Z","updated_at":"2025-04-01T20:31:08.766Z","avatar_url":"https://github.com/datamllab.png","language":"Python","readme":"# AutoRec\n\n\nAutoRec is a Keras-based implementation of automated recommendation algorithms for both rating prediction and Click Through Rate task. \n\n\nFor more details, see the [Documentation](http://autorec.ai).\n\n\n## Installation\nInstall from `pip`:\n```\npip install autorec\n```\n\n\n## Quickstart\nBuild an rating prediction model which can search the model architecture automatically  on the MovieLens  dataset is very easy as follows:\n```python\n# -*- coding: utf-8 -*-\nimport tensorflow as tf\nfrom autorecsys.auto_search import Search\nfrom autorecsys.pipeline import Input, LatentFactorMapper, RatingPredictionOptimizer, ElementwiseInteraction\nfrom autorecsys.pipeline.preprocessor import MovielensPreprocessor, NetflixPrizePreprocessor\nfrom autorecsys.recommender import RPRecommender\n\n# load dataset\n#Movielens 1M Dataset\ndata = MovielensPreprocessor(\"./examples/datasets/ml-1m/ratings.dat\")\ndata.preprocessing(val_test_size=0.1, random_state=1314)\ntrain_X, train_y = data.train_X, data.train_y\nval_X, val_y = data.val_X, data.val_y\ntest_X, test_y = data.test_X, data.test_y\nuser_num, item_num = data.user_num, data.item_num\n\n# build the pipeline.\ninput = Input(shape=[2])\nuser_emb = LatentFactorMapper(column_id=0,\n                              num_of_entities=user_num,\n                              embedding_dim=64)(input)\nitem_emb = LatentFactorMapper(column_id=1,\n                              num_of_entities=item_num,\n                              embedding_dim=64)(input)\noutput = ElementwiseInteraction(elementwise_type=\"innerporduct\")([user_emb, item_emb])\noutput = RatingPredictionOptimizer()(output)\nmodel = RPRecommender(inputs=input, outputs=output)\n\n# AutoML search and predict\nsearcher = Search(model=model,\n                  tuner='greedy',  # hyperband, greedy, bayesian\n                  tuner_params={\"max_trials\": 5}\n                  )\n\nsearcher.search(x=train_X,\n                y=train_y,\n                x_val=val_X,\n                y_val=val_y,\n                objective='val_mse',\n                batch_size=1024,\n                epochs=10,\n                callbacks=[tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=1)])\n```\n","funding_links":[],"categories":["Uncategorized"],"sub_categories":["Uncategorized"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatamllab%2FAutoRec","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdatamllab%2FAutoRec","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdatamllab%2FAutoRec/lists"}