Search

隨著移動設備的普及和O2O(Online-To-Offline)商業模式的快速發展,越來越多的空間眾包平臺融入人們的日常生活中,例如滴滴出行、餓了么等等.空間眾包中的一個核心問題是任務分配,主要研究如何將空間任務分配給合適的眾包工人.任務分配方式主要分為服務器分配模式(Server Assigned Task, SAT)和用戶選擇模式(Worker Selected Task, WST)兩種模式,目前多數統一規范化的眾包服務采用SAT模式,即系統主動將任務分配給任務請求位置附近的眾包工人.在此任務分配模式下,眾包工人和任務之間的旅行成本變得至關重要,較少的旅行成本意味著較少的響應時間和較高的任務接受率.因此提出了基于位置預測的任務分配方式,該方式不僅考慮任務和眾包工人的當前位置,還考慮未來任務可能出現的位置,從而降低旅行成本和相應時間.首先設計了貪婪方法(Greedy Approach),然后在貪婪方法的基礎上通過貝葉斯、支持向量機、決策樹等方法預測未來任務的分布來輔助分配任務,最后在真實數據上進行的實驗表明,該方法減小了在長時間內的總旅行成本,具有較好的性能. With the rapid development of mobile devices and the popularity of Online-To-Online (O2O) business models, more and more spatial crowdsourcing platforms play a significant role in our daily life, such as DiDi taxis, Eleme meal-ordering service, etc. A core issue in spatial crowdsourcing is task assignment, which is to assign real-time tasks to suitable crowd workers. There are two types of task assignment, namely worker selected task(WST)and server assigned task(SAT). The most current unified and standardized crowdsourcing services adopt the SAT mode, by which the system proactively assigns tasks to workers in proximity of requested locations. Under this task assignment mode, the travel cost between workers and tasks becomes of vital importance, because less travel cost means less response time and higher task acceptance ratio. This paper proposes a task assignment method based on location prediction to reduce the cost and response time. This task assignment method considers not only the current location of tasks and workers, but also the locations which tasks may appear in the future. This paper proposes Greedy approach, and based on which leverages a list of methods, such as Bayes, SVM and decision-making tree, for predicting the distribution of further tasks to assist the tasks’ assignment. And the final experimentation based on the real data verifies the performance and effectiveness of the method proposed in this paper, which shortens the overall travelling cost in the long term.

网络搜索分析在优化搜索引擎方面具有举足轻重的作用，而且对用户个人搜索特性进行分析能够提高搜索引擎的精准度。目前，大多数已有模型（比如点击图模型及其变体），注重研究用户群体的共同特点。然而，关于如何做到既可以获取用户群体共同特点又可以获取用户个人特点方面的研究却非常少。本文研究了基于个人用户网络搜索分析新问题，即通过研究用户搜索的突发性现象，获取个人用户搜索查询的主题分布情况。提出了两个搜索主题模型，即搜索突发性模型（SBM）和耦合敏感搜索突发性模型（CS-SBM）。SBM假设查询词和URL主题是无关的，CS-SBM假设查询词和URL之间是有主题关联的，得到的主题分布信息存储在偏Dirichlet先验中，采用Beta分布刻画用户搜索的时间特性。实验结果表明，每一个用户的网络搜索轨迹都有多种基于用户的独有特点。同时，在使用大量真实用户查询日志数据情况下，与LDA、DCMLDA、TOT相比，本文提出的模型具有明显的泛化性能优势，并且有效地描绘了用户搜索查询主题在时间上的变化过程。Web search analysis plays a critical role in improving the performance of contemporary search engines. In addition, search engine accuracy can be improved by analyzing the individual search properties of users. Most existing models, such as the click graph and its variants, focus on the common characteristics of the group. However, as yet, there has been little investigation of a model that would obtain both the collective group characteristics and the unique characteristics of individual users. In this paper, we investigate user-specific web search analysis, whereby we obtain the topic distributions of the search queries of individual users by determining the burstiness of user searches. We propose two topic models, i.e., the search burstiness model (SBM) and the coupling-sensitive search burstiness model (CS-SBM). The SBM adopts the assumption that the query words and URL are topically independent, The CS-SBM supposes that the query words and URL are topically relevant. The obtained topic distribution information is stored in skewed Dirichlet priors and a beta distribution is used to capture the temporal properties of the user searches. Our experimental results show that each user's web search trail has unique characteristics, and that in the case of there being a large amount of real query log data, in comparison to the latent Dirichlet allocation (LDA) and topic over time (TOT) models, our proposed models have advantages with respect to generalized performance and effectively describes the temporal change process of user search

郭玉美., 論文(哲學碩士) -- 香港中文大學硏究院中國藝朮史哲學碩士課程, 1996., 參考文献 : leaves 184-190., Guo Yumei., 論文撮要, 緖論 --- p.1, Chapter 第一章 --- 居巢生平 --- p.6, Chapter 第一節： --- 時代背景 --- p.6, Chapter 第二節： --- 生平傳略及家族背景 --- p.10, Chapter 第三節： --- 生平經歷 --- p.18, Chapter -- --- 廣画時期 --- p.18, Chapter -- --- 東莞時期 --- p.32, Chapter -- --- 隔山時期 --- p.43, Chapter 第二章 --- 居巢的畫藝歷程 --- p.63, Chapter 第一節： --- 師承淵源 --- p.64, Chapter 第二節： --- 繪畫風格探硏 --- p.75, Chapter -- --- 前期：1847年以前 --- p.75, Chapter -- --- 中期：（1847-1856 ) --- p.79, Chapter -- --- 後期：(1856-1865 ) --- p.93, Chapter 第三章 --- 詩詞與畫藝 --- p.113, Chapter 第一節： --- 居巢的詩與詞 --- p.113, Chapter 第二節： --- 居巢的詩與畫 --- p.126, Chapter 第三節： --- 從題畫詩中探討居巢之藝術理論 --- p.131, Chapter 第四章 --- 居巢與居廉 --- p.143, Chapter 第一節： --- 親屬情誼 --- p.143, Chapter 第二節： --- 畫藝傳承 --- p.144, Chapter 第三節： --- 二居畫藝比較 --- p.155, Chapter 第四節： --- 成就及影響 --- p.164, 結語 --- p.179, 參考書目 --- p.184, 附錄（一）居巢年譜 --- p.191, 附錄（二） 居巢作品編年圖錄 --- p.198, 圖版 --- p.2.1--2.45, 附錄（三）各章繪畫插圖目錄 --- p.201, 插圖圖版 --- p.3.1--3.19, 附錄（四）生活照片插圖目錄- --- p.205, 插圖圖版 --- p.4.1--4.14, 謝辭 --- p.208, http://library.cuhk.edu.hk/record=b5888698, Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)