With the popularity of mobile devices in recent years, users can share their check-in behavior on social networks at any time or anywhere. This also makes it difficult for users to find the information they need. To solve the problem of information overload, many studies have proposed Point-of-Interest (POI) recommendation systems to predict POIs that users may be interested in the future; some of which analyze the POI tags, categories, geographic locations, and check-in times that users have visited to make recommendations. However, these features are too specific to precisely represent the characteristics of users and POIs. Therefore, this study proposes a POI recommendation method based on user reviews and geographic area features (PRRG) to predict the POIs that users may be interested in. The research framework is divided into two main parts: (1) review analysis and (2) POI area analysis. The review analysis extracts the topic, sentiment, and semantic features from user reviews to represent user preferences and POI features. The POI area analysis first divides the POI into areas, and then calculates the POI area weights according to the users’ movement trajectory. Finally, the weighted matrix factorization method is used to predict the ratings of POIs. The proposed method can extract various features to represent user preferences and POI features, and analyze the importance of POI area to users based on their movement patterns to enhance the recommendation accuracy. The experimental results show that the proposed method outperforms other methods and effectively improves the recommendation performance.
目錄
摘要 I
Abstract II
致謝 III
目錄 IV
圖目錄 VI
第1章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究貢獻 5
第2章 文獻探討 6
2.1 POI推薦系統 6
2.1.1 內容因素 6
2.1.2 地理因素 7
2.1.3 時間因素 8
2.1.4 以深度學習為基礎之POI推薦方法 8
2.3 深度學習 9
2.3.1 BERT模型 9
2.3.2 卷積神經網路 11
2.3.3 遞迴神經網路 11
2.4 主題模型 12
2.5 小結 13
第3章 研究方法 14
3.1 研究概述 14
3.2 主題分析 16
3.3 情感與語意分析 17
3.3.1 情感語意分析模型 17
3.3.2 建立情感語意向量 18
3.4 使用者喜好分析 19
3.5 POI分群 20
3.6 區域權重分析 21
3.6.1 使用者區域移動軌跡 21
3.6.2 區域權重分析 22
3.7 預測評分 23
第4章 實驗評估 25
4.1 資料蒐集與前處理 25
4.2 評估指標 26
4.3 實驗方法說明 27
4.4 實驗結果 28
4.4.1決定最佳主題數量 28
4.4.2 比較不同特徵對PRRG之影響 29
4.4.3比較不同的區域權重分析方法 31
4.4.4不同特徵對PRRG模型之影響 31
4.4.5 相關POI推薦方法之比較 32
第5章 結論與未來研究方向 34
5.1 結論 34
5.2 未來研究方向 35
參考文獻 36



圖目錄
圖1 POI推薦影響因素圖 6


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