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Full-Text Articles in Software Engineering
Scalable Collaborative Filtering Recommendation Algorithms On Apache Spark, Walker Evan Casey
Scalable Collaborative Filtering Recommendation Algorithms On Apache Spark, Walker Evan Casey
CMC Senior Theses
Collaborative filtering based recommender systems use information about a user's preferences to make personalized predictions about content, such as topics, people, or products, that they might find relevant. As the volume of accessible information and active users on the Internet continues to grow, it becomes increasingly difficult to compute recommendations quickly and accurately over a large dataset. In this study, we will introduce an algorithmic framework built on top of Apache Spark for parallel computation of the neighborhood-based collaborative filtering problem, which allows the algorithm to scale linearly with a growing number of users. We also investigate several different variants …
Colormoo: An Algorithmic Approach To Generating Color Palettes, Joshua Rael
Colormoo: An Algorithmic Approach To Generating Color Palettes, Joshua Rael
CMC Senior Theses
Selecting one color can be done with relative ease, but this task becomes more difficult with each subsequent color. Colormoo is an online tool aimed at solving this problem. We implement three algorithms for generating color palettes based off of a starting color. Data is collected for each palette that is generated. Our analysis reveals two of the algorithms are preferred, but under different circumstances. Furthermore, we find that users prefer palettes containing colors that are compatible, but not too similar. With refined heuristics, we believe these techniques can be extended and applied beyond the field of graphic design alone.