Project 35 : Product Search Engine Using Machine Learning

AI Data Scientist Expert

The integration of cosine similarity in product recommendation systems highlights the importance of effective data representation and similarity metrics in enhancing user experience. However, the challenge remains in ensuring the dataset is clean and well-structured to yield accurate recommendations, as inconsistencies can significantly distort the search engine's effectiveness. Known industry practices encourage thorough data preprocessing, which can increase the reliability of the models built on these datasets.

AI Ethics and Governance Expert

Utilizing datasets from platforms like Kaggle poses significant ethical considerations, particularly regarding data privacy and the potential biases inherent in product recommendations. It is crucial to establish governance frameworks ensuring that the algorithms developed are fair, transparent, and accountable. By prioritizing ethical AI practices, developers can mitigate risks associated with making biased or unethical product suggestions that may affect user trust and engagement.

Cosine Similarity

In the video, cosine similarity is applied to determine product similarities which drives the recommendation engine.

Natural Language Processing (NLP)

Its application in the video includes processing product descriptions for better search capabilities.

Data Preprocessing

In the video, preprocessing involves checking for duplicates and cleaning textual data to improve search efficiency.

Kaggle

In this context, Kaggle is utilized as the primary source for the Amazon product dataset used in model training.

Mentions: 3

Python

Its libraries, like pandas and scikit-learn, are critical for implementing the data processing and modeling discussed in the video.

Mentions: 5