Читать книгу Advanced Analytics and Deep Learning Models - Группа авторов - Страница 73

In this research activity, they used two practical datasets where ratings are scaled between 1 and 5. The TripAdvisor data had used. In this dataset, it has more than 22,000 ratings provided by more than 1,500 clients with around 14,000 plus hotels. Every client rated at least 10 ratings. These ratings relate to multi-criteria ratings on seven criteria. Those criteria are cost-effective, convenience, quality of rooms, check-in, and cleanliness of the hotel and general standard of facility and specific business facilities. The Yahoo! Movies dataset was used here. There are more than 62,000 ratings given by more than 2000 clients on around 3,100 movies. Every client rates minimum 10 ratings. These ratings are related with multiple-criteria ratings on furrieries. Those critters are acting, direction, stories, and visual effects. They compared their utility-based models with some approaches. The approaches are MF, linear aggregation model (LAM), hybrid context model (HCM), and criteria chain model (CCM) [3].

They evaluated the efficiency of recommender form on the top 10 recommendations by using accuracy and NDCG to calculate the efficiency. To calculate the utility scores, they used three measures. By applying Pearson correlation, they get little improved results rather than applying cosine similarity. They found that Euclidean distance was the bad choice. They represented the best outcome by using Pearson correlation [3].