  Recommendation system services have become crucial for users to access
personalized goods or services as the global e-commerce market expands. They
can increase business sales growth and lower the cost of user information
exploration. Recent years have seen a signifi-cant increase in researchers
actively using user reviews to solve standard recommender system research
issues. Reviews may, however, contain information that does not help consumers
de-cide what to buy, such as advertising or fictitious or fake reviews. Using
such reviews to offer suggestion services may reduce the effectiveness of those
recommendations. In this research, the recommendation in e-commerce is
developed using passer learning optimization based on Bi-LSTM to solve that
issue (PL optimized Bi-LSTM). Data is first obtained from the product
recommendation dataset and pre-processed to remove any values that are missing
or incon-sistent. Then, feature extraction is performed using TF-IDF features
and features that support graph embedding. Before submitting numerous features
with the same dimensions to the Bi-LSTM classifier for analysis, they are
integrated using the feature concatenation approach. The Collaborative Bi-LSTM
method employs these features to determine if the model is a recommended
product. The PL optimization approach, which efficiently adjusts the
classifier's parameters and produces an extract output that measures the
f1-score, MSE, precision, and recall, is the basis of this research's
contributions. As compared to earlier methods, the pro-posed PL-optimized
Bi-LSTM achieved values of 88.58%, 1.24%, 92.69%, and 92.69% for dataset 1,
88.46%, 0.48%, 92.43%, and 93.47% for dataset 2, and 92.51%, 1.58%, 91.90%, and
90.76% for dataset 3.
