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研究生： 黃璽合
Huang, Hsi-Ho
論文名稱： 誰可以是我的聯合促銷合作夥伴?
Who can be my joint promotion partners?
指導教授： 李強
Lee, Chiang
學位類別： 碩士
Master
系所名稱： 電機資訊學院 - 資訊工程學系
Department of Computer Science and Information Engineering
論文出版年： 2015
畢業學年度： 103
語文別： 英文
論文頁數： 90
中文關鍵詞： 聯合促銷 、多重因素 、適地性社群網路 、打卡資料
外文關鍵詞： joint promotion, multiple factors, location-based social network, check-ins.
相關次數： 點閱：103  下載：0 
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    • 聯合促銷是商業行為中，最重要的應用之一，因為他們能夠幫助業者以較低的成本快速增加來客數。但是，傳統上要找聯合促銷的合作夥伴是非常困難的。因為通常得透過問卷調查的方式來作分析，但是此方法的可信度可能很低。此外，一個
    城市中，通常會有上千個可能的合作夥伴，若要一一考慮是非常耗時的。
    因此本篇論文提出了Joint Promotion Partners Finding (JPPF) Framework，藉由適地性社群網路資料來自動的替業者找出合適的合作夥伴並因此解決以往聯合促銷在找合作夥伴時的問題。為了能規劃出適合的合作夥伴，本篇論文考慮了六種因素共同市場、關連性、評價與知名度、價錢與星級、距離及促銷活動策略。並提出了有效率的演算法，來幫助某一個特定的業者快速的找出適合它的專屬合作對象。最終，本論文使用了Foursquare dataset及真實的聯合促銷案例來驗證本論文演算法的有效性及效率。

    Joint promotion is a valuable business strategy that enables companies to attract more customers at lower operational cost. However, fi nding a suitable partner can be extremely difficult. Conventionally, one of the most common approaches is to conduct a survey-based analysis; however, this method can be unreliable as well as time-consuming, considering that there are likely to be thousands of potential partners in a city.
    This study developed the Joint Promotion Partners Finding (JPPF) framework, which uses data from location-based social networks to automatically identify partners. We considered six factors in determining the suitability of a partner (common market, association, rating and awareness, prices and star ratings, distance, and promotional strategy) and developed efficient algorithms to perform the required calculations. Lastly, the effectiveness and efficiency of our algorithms were verfi ed using the Foursquare dataset and real-life joint promotion case studies.

    Inside Cover ..............i 口試合格證明.............ii 中文摘要.............iv Abstract ..............v Acknowledgment .............vi Table of Contents .............vii List of Tables .............ix List of Figures ............x 1 Introduction ............1 2 Related Work .............6 2.1 Location-based social networks (LBSNs) .......6 2.2 Recommendation systems .........6 2.2.1 Collaborative ltering recommendation systems ...6 2.2.2 Content-based recommendation systems .....7 2.2.3 Knowledge-based recommendation systems ....7 2.3 POI recommendations and trip recommendations ....8 3 Problem De nition ............10 4 Methodology ............12 4.1 System Framework ...........12 4.2 Offline Processing ..........13 4.2.1 Evaluating partner-related score .......13 4.2.2 POI Graph and Category Graph .......23 4.3 Online Query ............26 4.3.1 Promotion Strategy Selection .......26 4.3.2 Finding Candidate Partners ........31 4.3.3 Partner Score Calculation .......33 5 Experiments ............36 5.1 Experiment Settings ..........36 5.2 Conditions of algorithm under different response strategies ...37 5.2.1 Numbers of candidate partners identi ed by different response strategies ..........38 5.2.2 Probability of each strategy combination being executed during queries ............39 5.2.3 Execution time of algorithm with different strategy combinations 40 5.3 Overall algorithm performance .........42 5.4 Performance comparison with other relevant algorithms ...44 5.4.1 Relevance of results to query POI q ......45 5.4.2 Average distance between q and identi ed partners ...47 5.5 Case study to verify accuracy of our algorithm ......49 6 Conclusions and Future Work ..........54 Reference .............56 Appendix .............61

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