How to convert items into embeddings and recommend…

To convert items (such as products, articles, movies) into embeddings and recommend based on them, the following steps are usually performed:

Feature selection: gather data about items — text descriptions, categories, characteristics, images, etc.
Conversion to vector space:
- For textual data, models like Word2Vec, FastText, BERT, Sentence Transformers can be used to convert text into fixed-dimensional vectors.
- For images — use pre-trained CNNs (e.g., ResNet) to extract features.
- For categorical and numerical features — apply one-hot encoding, normalization, and/or train embeddings.
Feature merging: if there are multiple data types, combine them into a single embedding vector.
Training an embedding model (optional): train a model that considers interactions between users and items (e.g., matrix factorization, neural networks with triplet loss or contrastive loss).
Recommendations:
- For a new user or query, find embeddings of the items of interest.
- Use similarity metrics (cosine similarity, Euclidean distance) to find nearest neighbors in the embedding space.
- Recommend items with the closest embeddings.

Example in Python using the sentence-transformers library for text descriptions:

from sentence_transformers import SentenceTransformer
from sklearn.metrics.pairwise import cosine_similarity
import numpy as np

items = ["Red cotton T-shirt", "Blue jeans", "Black leather boots"]
model = SentenceTransformer('all-MiniLM-L6-v2')
embeddings = model.encode(items)

# Find an item similar to "Blue T-shirt"
query = "Blue T-shirt"
query_emb = model.encode([query])

similarities = cosine_similarity(query_emb, embeddings)[0]
most_similar_idx = np.argmax(similarities)
print(f"Recommended item: {items[most_similar_idx]}")

Thus, embeddings allow representing items in a convenient form for comparison and building recommendations based on proximity in vector space.

How to convert items into embeddings and recommend based on them?