{"id":13748099,"url":"https://github.com/jfkirk/tensorrec","last_synced_at":"2025-05-15T08:05:37.450Z","repository":{"id":22399997,"uuid":"83467664","full_name":"jfkirk/tensorrec","owner":"jfkirk","description":"A TensorFlow recommendation algorithm and framework in Python.","archived":false,"fork":false,"pushed_at":"2023-05-22T21:34:54.000Z","size":641,"stargazers_count":1285,"open_issues_count":40,"forks_count":220,"subscribers_count":62,"default_branch":"master","last_synced_at":"2025-04-14T13:07:56.747Z","etag":null,"topics":["framework","machine-learning","python","recommendation-algorithm","recommendation-system","recommender-system","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/jfkirk.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2017-02-28T18:51:11.000Z","updated_at":"2025-04-06T13:05:23.000Z","dependencies_parsed_at":"2024-01-29T18:51:49.574Z","dependency_job_id":null,"html_url":"https://github.com/jfkirk/tensorrec","commit_stats":{"total_commits":302,"total_committers":9,"mean_commits":33.55555555555556,"dds":0.09933774834437081,"last_synced_commit":"80690737ac039a5b41fc99e67372c4f67d8cfc51"},"previous_names":[],"tags_count":33,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jfkirk%2Ftensorrec","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jfkirk%2Ftensorrec/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jfkirk%2Ftensorrec/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/jfkirk%2Ftensorrec/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/jfkirk","download_url":"https://codeload.github.com/jfkirk/tensorrec/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254301431,"owners_count":22047904,"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":["framework","machine-learning","python","recommendation-algorithm","recommendation-system","recommender-system","tensorflow"],"created_at":"2024-08-03T07:00:34.316Z","updated_at":"2025-05-15T08:05:37.427Z","avatar_url":"https://github.com/jfkirk.png","language":"Python","readme":"# TensorRec\nA TensorFlow recommendation algorithm and framework in Python.\n\n[![PyPI version](https://badge.fury.io/py/tensorrec.svg)](https://badge.fury.io/py/tensorrec) [![Build Status](https://travis-ci.org/jfkirk/tensorrec.svg?branch=master)](https://travis-ci.org/jfkirk/tensorrec) [![Gitter chat](https://badges.gitter.im/tensorrec/gitter.png)](https://gitter.im/tensorrec)\n\n## NOTE: TensorRec is not under active development\n\nTensorRec will not be receiving any more planned updates. Please feel free to open pull requests -- I am happy to review them.\n\nThank you for your contributions, support, and usage of TensorRec!\n\n-James Kirk, @jfkirk\n\nFor similar tools, check out:\n\n[TensorFlow Ranking](https://github.com/tensorflow/ranking/)\n\n[Spotlight](https://github.com/maciejkula/spotlight)\n\n[LightFM](https://github.com/lyst/lightfm)\n\n## What is TensorRec?\nTensorRec is a Python recommendation system that allows you to quickly develop recommendation algorithms and customize them using TensorFlow.\n\nTensorRec lets you to customize your recommendation system's representation/embedding functions and loss functions while TensorRec handles the data manipulation, scoring, and ranking to generate recommendations.\n\nA TensorRec system consumes three pieces of data: `user_features`, `item_features`, and `interactions`. It uses this data to learn to make and rank recommendations.\n\nFor an overview of TensorRec and its usage, please see the [wiki.](https://github.com/jfkirk/tensorrec/wiki)\n\nFor more information, and for an outline of this project, please read [this blog post.](https://medium.com/@jameskirk1/tensorrec-a-recommendation-engine-framework-in-tensorflow-d85e4f0874e8)\n\nFor an introduction to building recommender systems, please see [these slides.](https://www.slideshare.net/JamesKirk58/boston-ml-architecting-recommender-systems)\n\n![TensorRec System Diagram](https://raw.githubusercontent.com/jfkirk/tensorrec/master/examples/system_diagram.png)\n\n### Example: Basic usage\n```python\nimport numpy as np\nimport tensorrec\n\n# Build the model with default parameters\nmodel = tensorrec.TensorRec()\n\n# Generate some dummy data\ninteractions, user_features, item_features = tensorrec.util.generate_dummy_data(\n    num_users=100,\n    num_items=150,\n    interaction_density=.05\n)\n\n# Fit the model for 5 epochs\nmodel.fit(interactions, user_features, item_features, epochs=5, verbose=True)\n\n# Predict scores and ranks for all users and all items\npredictions = model.predict(user_features=user_features,\n                            item_features=item_features)\npredicted_ranks = model.predict_rank(user_features=user_features,\n                                     item_features=item_features)\n\n# Calculate and print the recall at 10\nr_at_k = tensorrec.eval.recall_at_k(predicted_ranks, interactions, k=10)\nprint(np.mean(r_at_k))\n```\n\n## Quick Start\nTensorRec can be installed via pip:\n```pip install tensorrec```\n","funding_links":[],"categories":["Recommender Systems","资源列表","推荐系统","5. 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