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| Title: | 應用象徵性資料分析法於電影推薦系統之研究
The application of symbolic data analysis to movie recommendation systems |
| Authors: | 張順益
CHANG, SHUN-YI |
| Contributors: | 吳漢銘
Wu, Han-Ming
張順益
CHANG, SHUN-YI |
| Keywords: | 推薦系統
象徵性資料分析法
分群演算法
遺失值補值
Recommendation System
Symbolic Data Analysis
Clustering Algorithm
Missing Value Imputation |
| Date: | 2023 |
| Issue Date: | 2023-09-01 14:57:45 (UTC+8) |
| Abstract: | 推薦系統（Recommendation System）如今已廣泛應用於商業行銷，涵蓋範疇包括電影、音樂、新聞、書籍、餐廳、3C 商品以及金融服務等產品的推薦。推薦系統能為用戶提供精確的個性化推薦，從而提高商家的營利。協同過濾算法（collaborative filtering）\\citep{Resnick} 是推薦算法中最常見的一種，其根據用戶對商品的評分進行協同過濾，以便找出合適的產品進行推薦。該演算法的理論基礎在於消費行為相近的用戶應該會偏好類似的商品。然而，協同過濾算法面臨新用戶冷啟動（亦稱新商品問題）和稀疏矩陣等問題。在本研究中，我們針對電影推薦系統，根據用戶群的特徵將其對電影的評分依照電影類型轉換成多值模態象徵性資料（multi-valued modal symbolic data）。此轉換方法考慮到每部電影可能具有多種類型的特點，旨在克服新用戶冷啟動問題並減少缺失值導致的稀疏矩陣問題。我們進行了模擬實驗並分析了實際的電影評分資料，以驗證我們提出的新方法。結果顯示，應用象徵性資料分析法不僅可以提升推薦的效果，更為推薦系統的發展開創了一條新的思考途徑和方法。
Recommendation systems are now widely used in business marketing, spanning various domains such as movies, music, news, books, restaurants, 3C products, and financial services. Collaborative filtering, the most common recommendation algorithm, utilizes user ratings on products to perform collaborative filtering and identify suitable items for recommendations. The theoretical basis of this algorithm is that users with similar consumption behaviors are likely to prefer similar items. However, collaborative filtering algorithms face challenges such as the cold start problem for new users (also known as the new item problem) and the sparsity issue in matrices. In this study, we focus on a movie recommendation system and transform user ratings for movies into multi-valued modal symbolic data based on user group characteristics. This transformation method takes into account the multiple genres or characteristics that a movie may have, aiming to overcome the cold start problem for new users and reduce the sparsity issue caused by missing values in the matrix. We conducted simulation experiments and analyzed real movie rating data to validate the proposed approach. The results showed that the symbolic data analysis method not only improves recommendation effectiveness but also provides a new approach and method for the development of recommendation systems. |
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| Description: | 碩士
國立政治大學
統計學系
110354026 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0110354026 |
| Data Type: | thesis |
| Appears in Collections: | [統計學系] 學位論文
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