Summary of Data-driven Portfolio Management For Motion Pictures Industry: a New Data-driven Optimization Methodology Using a Large Language Model As the Expert, by Mohammad Alipour-vaezi et al.
Data-Driven Portfolio Management for Motion Pictures Industry: A New Data-Driven Optimization Methodology Using a Large Language Model as the Expert
by Mohammad Alipour-Vaezi, Kwok-Leung Tsui
First submitted to arxiv on: 11 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Optimization and Control (math.OC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach to optimizing portfolios in the Motion Pictures Industry (MPI) is proposed. The method involves predicting box office performance by considering the effect of celebrities involved in each project. A large language model is used to determine the fame score of celebrities, and projects are classified to account for the asymmetric nature of MPI data. Box office predictions are made for each class of projects, and a hybrid multi-attribute decision-making technique is employed to calculate the preferability of each project for distributors. Finally, a bi-objective optimization model is used to design an optimal portfolio. The proposed method outperforms existing expert-based methods in predicting box office performance and designing optimal portfolios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Motion Pictures Industry (MPI) has a problem: choosing which movies to invest in. To help with this decision, scientists developed a new way to predict how well each movie will do at the box office. This prediction takes into account the popularity of the actors involved in the movie. The method is different from previous ways of making predictions because it accounts for the fact that some data is more important than others (for example, the success or failure of movies starring a particular actor). By combining this new approach with other decision-making tools, the scientists were able to create an optimal portfolio of movies that will be most profitable. |
Keywords
» Artificial intelligence » Large language model » Optimization
