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論文中文名稱:適用於室內購物環境推薦系統之研究 [以論文名稱查詢館藏系統]
論文英文名稱:The Study of a Recommendation System on Indoor Shopping Environment [以論文名稱查詢館藏系統]
院校名稱:臺北科技大學
學院名稱:管理學院
系所名稱:資訊與運籌管理研究所
畢業學年度:101
出版年度:102
中文姓名:王庭發
英文姓名:Ting-Fa Wang
研究生學號:100938016
學位類別:碩士
語文別:中文
口試日期:2013-07-03
論文頁數:51
指導教授中文名:翁頌舜
口試委員中文名:吳瑞堯;蕭瑞祥
中文關鍵詞:推薦系統室內定位室內購物推薦關聯法則接收信號強度
英文關鍵詞:Recommended methodIndoor positioningIndoor shopping RecommendAssociation rulesReceived signal strength
論文中文摘要:隨著手機的廣泛使用,基於定位技術的手機購物推薦系統被提出來用於改善推薦的性能。但是,由於現有定位技術的限制,在室內購物這種主要的購物模式中,還沒有適用的手機購物推薦系統。在本研究中,我們探討適用於室內環境之手機購物推薦系統。這個推薦系統是利用基於接收信號強度(RSS)模型的室內定位技術。新的室內定位技術能克服現有室內定位的弊端。尤其是手機推薦系統能夠隱式的捕捉使用者的偏好。這個推薦系統通過分析使用者的位置,不需要使用者明確的輸入,結合情境感知資訊做出推薦。綜合實驗評估,新的手機購物推薦系統能夠達到較好的使用者滿意度。
論文英文摘要:With the widespread usage of mobile devices, the mobile recommender system is proposed to improve recommendation performance, using positioning technologies. However, due to restrictions of existing positioning technologies, mobile recommender systems are still not being applied to indoor shopping, which has been the main shopping mode until now. In this paper, we develop a mobile recommender system for stores under the circumstance of indoor shopping, based on the proposed novel indoor mobile positioning approach by using received signal patterns of mobile phones, which can overcome the disadvantages of existing positioning technologies. Especially, the mobile recommender system can implicitly capture users’ preferences by analyzing users’ positions, without requiring users’ explicit inputting, and take the contextual information into consideration when making recommendations. A comprehensive experimental evaluation shows our proposed mobile recommender system achieves better user satisfaction.
論文目次:中文摘要 I
英文摘要 II
誌 謝 III
目 錄 VI
表目錄 VIII
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的及範圍 3
1.3 論文架構 4
第二章 文獻探討 5
2.1 實體購物推薦系統 5
2.1.1 根據網路評級產生推薦 5
2.1.2 根據情境資訊產生推薦 6
2.1.3 根據偏好學習和情境資訊產生推薦 6
2.2 基於接收信號強度的室內定位方法 7
2.2.1 三邊測量定位技術 7
2.2.2 三角測量法 7
2.2.3 極大似然估計法 8
2.2.4 衡量定位演算法性能優劣的指標 9
2.3 資料勘探技術 11
2.3.1 關聯法則 11
第三章 研究方法 13
3.1 系統架構 13
3.1.1 手機終端 14
3.1.2 位置伺服器 14
3.1.3 推薦伺服器 14
3.2 接收信號強度(RSS)模型 14
3.3 系統流程 18
3.3.1 學習過程 18
3.3.2 推薦過程 18
3.4 推薦系統 18
3.4.1 Apriori演算法 18
3.4.2 個性化Top-N推薦商店項目 21
第四章 研究結果分析 26
4.1 環境設定 26
4.2 實驗方法與步驟 28
4.2.1 資料描述 28
4.2.2 實驗步驟 28
4.2.3 Top-N推薦商店資訊範例 29
4.3 研究結果與分析 33
4.3.1 篩選相關位置記錄 33
4.3.2 進行關聯法則探勘 35
4.3.3 進行Top-N推薦 38
4.3.4 結果分析 38
第五章 結論與建議 43
5.1 結論 43
5.2 未來方向與建議 44
5.3 研究限制 44
參考文獻 46
附 錄 51
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