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Personalized Recommender System Based on Milvus

Prerequisites

Environment requirements

The following table lists recommended configurations, which have been tested:

Data source

The data source is MovieLens million-scale dataset (ml-1m), created by GroupLens Research. Refer to ml-1m-README for more information.

Build a personalized recommender system based on Milvus

Follow the steps below to build a recommender system:

1. Train the model.

   # run train.py
   $ python3 train.py

   This command generates a model file recommender_system.inference.model in the same folder.

2. Generate test data.

   # Download movie data movies_origin.txt to the same folder
   $ wget https://raw.githubusercontent.com/milvus-io/bootcamp/0.5.3/demo/recommender_system/movies_origin.txt
   # Generate test data. The -f parameter is followed by the movie data filename.
   $ python3 get_movies_data.py -f movies_origin.txt

   The above commands generate movies_data.txt in the same folder.

3. Use Milvus for personalized recommendation by running the following command:

   # Milvus performs personalized recommendation based on user status
   $ python3 infer_milvus.py -a <age> -g <gender> -j <job> [-i]
   # Example 1
   $ python3 infer_milvus.py -a 0 -g 1 -j 10 -i
   # Example 2
   $ python3 infer_milvus.py -a 6 -g 0 -j 16

   The following table describes arguments of infer_milvus.py.

   | Parameter | Description |
   | —————- | —————————————————————————————— |
   | -a/--age | Age distribution
   0: “Under 18”
   1: “18-24”
   2: “25-34”
   3: “35-44”
   4: “45-49”
   5: “50-55”
   6: “56+” |
   | -g/--gender | Gender
   0:male
   1:female |
   | -j/--job | Job
   0: “other” or not specified
   1: “academic/educator”
   2: “artist”
   3: “clerical/admin”
   4: “college/grad student”
   5: “customer service”
   6: “doctor/health care”
   7: “executive/managerial”
   8: “farmer”
   9: “homemaker”
   10: “K-12 student”
   11: “lawyer”
   12: “programmer”
   13: “retired”
   14: “sales/marketing”
   15: “scientist”
   16: “self-employed”
   17: “technician/engineer”
   18: “tradesman/craftsman”
   19: “unemployed”
   20: “writer” |
   | -i/--infer | (Optional) Converts test data to vectors and import to Milvus. |

   Note: -i/--infer is required when you use Milvus for personalized recommendation for the first time or when you start another training and regenerate the model.

   The result displays top 5 movies that the specified user might be interested in:

   get infer vectors finished!
   Server connected.
   Status(code=0, message='Create table successfully!')
   rows in table recommender_demo: 3883
   Top      Ids     Title   Score
   0        3030    Yojimbo         2.9444923996925354
   1        3871    Shane           2.8583481907844543
   2        3467    Hud     2.849525213241577
   3        1809    Hana-bi         2.826111316680908
   4        3184    Montana         2.8119677305221558

   Run python3 infer_paddle.py. You can see that Paddle and Milvus generate the same result.