Design a Location-Time Identity-recognition Digital Signage Based Ethnic Advertising Recommendation System Using Degree of Memberships
碩士 === 中華大學 === 資訊工程學系碩士班 === 100 === The traditional recommendation system is mostly done by similarity discriminate when the data is sufficient, and use the high correlation as recommendation items. It will become very difficult to produce accurate advertisements recommendation when the data is n...
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ndltd-TW-100CHPI53920522017-02-17T16:16:32Z http://ndltd.ncl.edu.tw/handle/06379172492215718330 Design a Location-Time Identity-recognition Digital Signage Based Ethnic Advertising Recommendation System Using Degree of Memberships 設計一個使用歸屬度以區域-時間為基礎之具身分辨識的數位看板族群廣告推薦系統 CHUN-YUAN LO 羅雋元 碩士 中華大學 資訊工程學系碩士班 100 The traditional recommendation system is mostly done by similarity discriminate when the data is sufficient, and use the high correlation as recommendation items. It will become very difficult to produce accurate advertisements recommendation when the data is not enough. Therefore, some scholars explore the Cold-starting Problem: To resolve the recommendation effectively when too small amount of data is provided. In this paper, we address the Cold-starting Problem by using location and time as the initial condition to produce accurate advertisements recommendation. Also, in the paper, we use fuzzy theory to obtain the objective value by the classification results with time, location and advertising attribution calculation, we call it as LTRS (Location-Time based Recommendation System). From the experimental results, the proposed LTRS can improve the accuracy of advertising, not only to improve the traditional recommendation system but also improve the recommended advertising accuracy of the Cold-starting Problem. Kun-Ming Yu 游坤明 2012 學位論文 ; thesis 47 zh-TW |
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碩士 === 中華大學 === 資訊工程學系碩士班 === 100 === The traditional recommendation system is mostly done by similarity discriminate
when the data is sufficient, and use the high correlation as recommendation items. It will become very difficult to produce accurate advertisements recommendation when the data is not enough. Therefore, some scholars explore the Cold-starting Problem: To resolve the recommendation effectively when too small amount of data is provided.
In this paper, we address the Cold-starting Problem by using location and time as the initial condition to produce accurate advertisements recommendation. Also, in the paper, we use fuzzy theory to obtain the objective value by the classification results with time, location and advertising attribution calculation, we call it as LTRS (Location-Time based Recommendation System). From the experimental results, the proposed LTRS can improve the accuracy of advertising, not only to improve the traditional recommendation system but also improve the recommended advertising accuracy of the Cold-starting Problem.
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author2 |
Kun-Ming Yu |
author_facet |
Kun-Ming Yu CHUN-YUAN LO 羅雋元 |
author |
CHUN-YUAN LO 羅雋元 |
spellingShingle |
CHUN-YUAN LO 羅雋元 Design a Location-Time Identity-recognition Digital Signage Based Ethnic Advertising Recommendation System Using Degree of Memberships |
author_sort |
CHUN-YUAN LO |
title |
Design a Location-Time Identity-recognition Digital Signage Based Ethnic Advertising Recommendation System Using Degree of Memberships |
title_short |
Design a Location-Time Identity-recognition Digital Signage Based Ethnic Advertising Recommendation System Using Degree of Memberships |
title_full |
Design a Location-Time Identity-recognition Digital Signage Based Ethnic Advertising Recommendation System Using Degree of Memberships |
title_fullStr |
Design a Location-Time Identity-recognition Digital Signage Based Ethnic Advertising Recommendation System Using Degree of Memberships |
title_full_unstemmed |
Design a Location-Time Identity-recognition Digital Signage Based Ethnic Advertising Recommendation System Using Degree of Memberships |
title_sort |
design a location-time identity-recognition digital signage based ethnic advertising recommendation system using degree of memberships |
publishDate |
2012 |
url |
http://ndltd.ncl.edu.tw/handle/06379172492215718330 |
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