Evaluation of Social, Geography, Location Effects for Point-of-Interest Recommendation

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：102

、訪客IP：3.148.145.215

姓名

鄭乃洪(Nai-Hung Cheng) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

(Evaluation of Social, Geography, Location Effects for Point-of-Interest Recommendation)

相關論文

★ 行程邀約郵件的辨識與不規則時間擷取之研究	★ NCUFree校園無線網路平台設計及應用服務開發
★ 網際網路半結構性資料擷取系統之設計與實作	★ 非簡單瀏覽路徑之探勘與應用
★ 遞增資料關聯式規則探勘之改進	★ 應用卡方獨立性檢定於關連式分類問題
★ 中文資料擷取系統之設計與研究	★ 非數值型資料視覺化與兼具主客觀的分群
★ 關聯性字組在文件摘要上的探討	★ 淨化網頁：網頁區塊化以及資料區域擷取
★ 問題答覆系統使用語句分類排序方式之設計與研究	★ 時序資料庫中緊密頻繁連續事件型樣之有效探勘
★ 星狀座標之軸排列於群聚視覺化之應用	★ 由瀏覽歷程自動產生網頁抓取程式之研究
★ 動態網頁之樣版與資料分析研究	★ 同性質網頁資料整合之自動化研究

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

近年來由於社群網路的發展，打卡軟體也漸漸成為熱門軟體，提供地點推薦系統推薦給使用者感興趣的地點就是一個很熱門的研究題材。在地點推薦系統中，地點的數量會比使用者的數量來的多很多，因此要從龐大的地點數量中成功預測使用者感興趣的地點將會是一個很大的挑戰。首先我們利用地理區域性的特色挑選合適的地點作為候選集合，並且提出個人化配置的線性組合方式整合了memory-based CF、社群網路以及地點的流行程度三個層面，能夠根據每一個使用者的特性來推薦相對應適合該使用者的地點。另外我們也實作了以分類問題為主的模型，logistic regression以及libFM，探討此二個模型在處理地點推薦系統下的效能。實驗結果顯示本篇論文所提出的個人化配置的推薦系統能夠達到最佳的效能，並且和分類問題為主的logistic regression和libFM模型相比較下，效能以及效率都較為優秀。

摘要(英)

Recently, location-based social network service has become very popular. Point of interests (POI) recommendation service is a promising and interesting research problem. In POI recommendation, numbers of locations are more than numbers of users, so it is a challenge to recommend interest locations from amount of location sets. Our idea is to personally incorporate user preference, social influence and attraction of locations in the recommendation. First, we use geographic influence for candidate selection. Furthermore, we propose a unified POI recommendation framework CLW, which fuses user preference to a POI with social influence and attraction of locations. In addition, we discuss performance of classification model for POI recommendation, logistic regression and libFM. Experimental results shows unified POI recommendation framework CLW outperforms other approaches.

關鍵字(中)

★ 地點推薦系統
★ 協同過濾

關鍵字(英)

論文目次

List of figures ii
List of tables iii
Chapter 1 Introduction 1
Chapter 2 Related work 3
Chapter 3 研究方法 6
3.1 Geographic influence 6
3.2 Basics Factors 7
3.2.1 User-based Collaborative Filtering 8
3.2.2 Item-based Collaborative Filtering 8
3.2.3 Social Collaborative Filtering 9
3.2.4 Attraction of locations 10
3.3 Customized Linear Weighting 12
3.4 Classification-based recommendation 13
Chapter 4 Experiment 15
4.1 Evaluation of basic factors 17
4.1.1 Effects of Top-N 17
4.1.2 Effects of Training Size 18
4.1.3 Effects of candidate selection 19
4.1.4 Effects of Social influence 19
4.1.5 Effects on cold start user 20
4.2 Effects of classification-based recommendation 21
4.3 Efficiency comparison 22
Chapter 5 Conclusion & Future work 24
References 25

參考文獻

[1] Jun Wang, A.P. de Vries and M.J.T. Reinders. Unifying User-based and Item-based Collaborative Filtering Approaches by Similarity Fusion. In SIGIR, pages 501-508, 2006.
[2] H. Yıldırım and M.S. Krishnamoorthy. A Random Walk Method for Alleviating the Sparsity Problem in Collaborative Filtering. In RecSys, pages 131-138, 2008.
[3] M. Jamali and M. Ester. TrustWalker: a random walk　model for combining trust-based and item-based　recommendation. In KDD, pages 397-406, 2009.
[4] X. Su and T.M. Khoshgoftaar. A Survey of Collaborative Filtering Techniques. In Advances in Artificial Intelligence, Article No. 4, 2009.
[5] X. Cai, M. Bain, A. Krzywicki, W. Wobcke, Y.S. Kim, P. Compton and A. Mahidadia. Learning Collaborative Filtering and Its Application to People to People Recommendation in Social Networks. In ICDM, pages 743-748, 2010.
[6] M. Ye, P. Yin, W.C. Lee, and D.L. Lee. Exploiting Geographical Influence for Collaborative Point-of-Interest Recommendation. In SIGIR, pages 325-334, 2011.
[7] D. Saez-Trumper and G. Comarela. Finding Trendsetters in Information Networks. In KDD, pages 1014-1022, 2012.
[8] H.P. Hsieh, C.T. Li and S.D. Lin. Exploiting Large-Scale Check-in Data to Recommend Time-Sensitive Routes. In UrbComp, pages 55-62, 2012.
[9] N.N. Liu and Q. Yang. EigenRank: A Ranking-Oriented Approach to Collaborative Filtering. In SIGIR, pages 83-90, 2008.
[10] Y. Huang and L. Bian. A Bayesian network and analytic hierarchy process based personalized recommendations for tourist attractions over the Internet. In Expert Systems with Applications, pages 933-943, 2009.
[11] E.H.C. Lu, C.Y. Lin and V.S. Tseng. Trip-Mine: An Efficient Trip Planning Approach with Travel Time Constraints. In MDM, pages 152-161, 2011.

指導教授

張嘉惠

審核日期

2013-8-29

推文