使用者行為分析與商品推薦應用於集點App;User Behavior Analysis and Commodity Recommendation for Point-Earning App

NCUIR > College of Electrical Engineering & Computer Science > Graduate Institute of Computer Science and Information Engineering > Electronic Thesis & Dissertation > Item 987654321/74791

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/74791

Title:	使用者行為分析與商品推薦應用於集點App;User Behavior Analysis and Commodity Recommendation for Point-Earning App
Authors:	陳昱瑾;Chen, Yu-Ching
Contributors:	資訊工程學系
Keywords:	推薦系統;機器學習;時間序列性推薦;Structure Learning
Date:	2017-08-24
Issue Date:	2017-10-27 14:39:28 (UTC+8)
Publisher:	國立中央大學
Abstract:	近年來網路普及，電子商務網站發展成熟，隨之而生的推薦系統發展蔚為風潮，如何幫助消費者從成千上萬的商品中，找到他所需要的商品，並在最短時間內將「對的商品」在「對的時間」推薦給「對的人」，是很重要的。另一方面，對於商家而言，能將推薦資訊精準的投放給有需要的顧客，為其首要目標。然而，推薦系統背後仰賴的是大量的數據和有效的分析，對於初創發展不久的企業來說是很大的問題。本研究的資料來自一款以集點任務作為設計導向的App，我們透過消費者使用App的紀錄與登入時綁定的FB帳號來了解使用者的消費行為與基本資訊。此集點App主要合作的廠商為台灣某連鎖美妝零售商，其販售的商品有美妝、保養品、零食以及生活用品等，共2,177項商品。對於這種消費型商品的推薦與以往電影推薦不同，消費型商品與時間有很大的關係，而且會被使用者重複購買。在本論文中，我們篩選出消費次數較高的使用者來分析，以解決資料不足的問題。此外，透過設計使用者特徵、商品特徵、使用者與商品的交互特徵以及時間相關的特徵，將資料透過Machine Learning的方法訓練，並建立預測模型，再對以時間切分後的測試資料進行預測。實驗數據顯示在Top5的F-measure由原本的0.0507提升至0.4413，藉此提升推薦系統的效能並進行較為有效的推薦。;The E-commerce website is well developed due to the internet become more popular for the past few year. It is a trend in recommendation system. It is the most important thing, how can we recommend the right thing for the right person at the right moment in the short time. On the other side, the first topic for the store is how can we give an exact information for the customers who needed. However, recommendation system depends on a huge data and analysis. It will be a big problem for a new company. This research data is from an App needs to collect point. We find out users’ profile and behavior from APP records and Facebook account. The App is mainly cooperate with a Taiwan makeup chained retailer, that it sells beauty, skin care products and some necessities, they have around 2,177 kinds of item. This kind of consumption recommended is different from the movie recommendation. Consumer product is related to the time and will be bought again by the user. In my paper, we select some user, who purchase times is high to resolve the data is not enough. Otherwise, through user feature, item feature, feature between with user and item and related to time. Using Machine Learning to train and built up the forecast model to predicted the testing data by the time. Resulting in F-measure from 0.0507 to 0.4413, Structure leaning with time greatly enhances the efficiency of our recommendation system.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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