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Articles 1 - 3 of 3
Full-Text Articles in Physical Sciences and Mathematics
Classification Of Pixel Tracks To Improve Track Reconstruction From Proton-Proton Collisions, Kebur Fantahun, Jobin Joseph, Halle Purdom, Nibhrat Lohia
Classification Of Pixel Tracks To Improve Track Reconstruction From Proton-Proton Collisions, Kebur Fantahun, Jobin Joseph, Halle Purdom, Nibhrat Lohia
SMU Data Science Review
In this paper, machine learning techniques are used to reconstruct particle collision pathways. CERN (Conseil européen pour la recherche nucléaire) uses a massive underground particle collider, called the Large Hadron Collider or LHC, to produce particle collisions at extremely high speeds. There are several layers of detectors in the collider that track the pathways of particles as they collide. The data produced from collisions contains an extraneous amount of background noise, i.e., decays from known particle collisions produce fake signal. Particularly, in the first layer of the detector, the pixel tracker, there is an overwhelming amount of background noise that …
Alternative Methods For Deriving Emotion Metrics In The Spotify® Recommendation Algorithm, Ronald M. Sherga Jr., David Wei, Neil Benson, Faizan Javed
Alternative Methods For Deriving Emotion Metrics In The Spotify® Recommendation Algorithm, Ronald M. Sherga Jr., David Wei, Neil Benson, Faizan Javed
SMU Data Science Review
Spotify's® recommendation algorithm tailors music offerings to create a unique listening experience for each user. Though what this recommender does is highly impressive, there is always room for improvement given that these techniques are not fully prescient. This study posits that in addition to creating certain features based on audio analysis, incorporating new features derived from album art color as well as lyrical sentiment analysis may provide additional value to the end user. This team did not find that a significant difference existed between color valence and Spotify® valence; however, all other comparisons resulted in statistically significant difference of means …
Yelp’S Review Filtering Algorithm, Yao Yao, Ivelin Angelov, Jack Rasmus-Vorrath, Mooyoung Lee, Daniel W. Engels
Yelp’S Review Filtering Algorithm, Yao Yao, Ivelin Angelov, Jack Rasmus-Vorrath, Mooyoung Lee, Daniel W. Engels
SMU Data Science Review
In this paper, we present an analysis of features influencing Yelp's proprietary review filtering algorithm. Classifying or misclassifying reviews as recommended or non-recommended affects average ratings, consumer decisions, and ultimately, business revenue. Our analysis involves systematically sampling and scraping Yelp restaurant reviews. Features are extracted from review metadata and engineered from metrics and scores generated using text classifiers and sentiment analysis. The coefficients of a multivariate logistic regression model were interpreted as quantifications of the relative importance of features in classifying reviews as recommended or non-recommended. The model classified review recommendations with an accuracy of 78%. We found that reviews …