This low-rank, collaborative movie recommendation engine yielded 10 movie recommendations of which 70% were consistent with user preferences versus a hierarchical clustering approach that produced only 3 recommendations out of 10 that were deemed watchable. The low-rank approach relies on the family of matrix decomposition/factorization techniques popularized during the Netflix recommendation engine challenge of 2006. Technology used included Python, SciPy, NumPy, Pandas, Seaborn, Matplotlib, Jupyter.
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He developed an AI agent that successfully handled 70% of customer inquiries, cutting response time in half and improving client satisfaction.
