implicit_recs.py

import implicit
from implicit.datasets.movielens import get_movielens
from pgvector.sqlalchemy import Vector
from sqlalchemy import create_engine, insert, select, text, Integer, String
from sqlalchemy.orm import declarative_base, mapped_column, Session

engine = create_engine('postgresql+psycopg://localhost/pgvector_example')
with engine.connect() as conn:
    conn.execute(text('CREATE EXTENSION IF NOT EXISTS vector'))
    conn.commit()

Base = declarative_base()


class User(Base):
    __tablename__ = 'user'

    id = mapped_column(Integer, primary_key=True)
    factors = mapped_column(Vector(20))


class Item(Base):
    __tablename__ = 'item'

    id = mapped_column(Integer, primary_key=True)
    title = mapped_column(String)
    factors = mapped_column(Vector(20))


Base.metadata.drop_all(engine)
Base.metadata.create_all(engine)

titles, ratings = get_movielens('100k')
model = implicit.als.AlternatingLeastSquares(factors=20)
model.fit(ratings)

users = [dict(factors=factors) for i, factors in enumerate(model.user_factors)]
items = [dict(title=titles[i], factors=factors) for i, factors in enumerate(model.item_factors)]

session = Session(engine)
session.execute(insert(User), users)
session.execute(insert(Item), items)

user = session.get(User, 1)
items = session.scalars(select(Item).order_by(Item.factors.max_inner_product(user.factors)).limit(5))
print('user-based recs:', [item.title for item in items])

item = session.scalars(select(Item).filter(Item.title == 'Star Wars (1977)')).first()
items = session.scalars(select(Item).filter(Item.id != item.id).order_by(Item.factors.cosine_distance(item.factors)).limit(5))
print('item-based recs:', [item.title for item in items])