Wals Roberta Sets Upd [WORKING]

The WALS algorithm requires periodic updates of its latent factor matrices. Here’s how to perform a standard update:

from implicit.als import AlternatingLeastSquares
import torch.nn as nn
class HybridRecoModel(nn.Module):
def init(self, wals_factors_dim=50, roberta_dim=768):
super().init()
self.wals_proj = nn.Linear(wals_factors_dim, 128)
self.roberta_proj = nn.Linear(roberta_dim, 128)
self.score = nn.DotProduct()
def forward(self, user_wals_vec, item_roberta_vec):
    u = self.wals_proj(user_wals_vec)
    i = self.roberta_proj(item_roberta_vec)
    return (u * i).sum(dim=1)

from transformers import AutoTokenizer, AutoModel
import torch
model_name = "roberta-base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
roberta = AutoModel.from_pretrained(model_name) wals roberta sets upd
def get_roberta_embedding(text):
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
with torch.no_grad():
outputs = roberta(**inputs)
# Use CLS token embedding or mean pooling
cls_embedding = outputs.last_hidden_state[:, 0, :].numpy()
return cls_embedding

pip install tensorflow  # or PyTorch
pip install transformers  # Hugging Face for RoBERTa
pip install implicit     # Fast WALS implementation (Python)
pip install numpy pandas scikit-learn

movies = [
"title": "Inception", "description": "A thief who steals secrets...", "movie_id": "1",
"title": "The Matrix", "description": "A computer hacker learns...", "movie_id": "2"
]

Key parameters for WALS sets upd: