【摘要】：眾包作為一種新的社會(huì)生產(chǎn)方式,可以極大地整合社會(huì)資源,創(chuàng)造巨大的商業(yè)價(jià)值。隨著互聯(lián)網(wǎng)技術(shù)的發(fā)展,眾包不再是單純的企業(yè)行為,逐漸發(fā)展為一種全民協(xié)作的社會(huì)化行為。移動(dòng)互聯(lián)網(wǎng)的興起為眾包協(xié)作的移動(dòng)化、碎片化提供了契機(jī),但同時(shí)又提出了挑戰(zhàn)�，F(xiàn)有的眾包協(xié)作應(yīng)用都是將信息分類(lèi)之后直接呈現(xiàn),不僅造成信息過(guò)載,而且難以快速響應(yīng)用戶在移動(dòng)場(chǎng)景下的即時(shí)協(xié)作任務(wù)。本文立足現(xiàn)實(shí)需求,以協(xié)作拼單為背景,引入推薦系統(tǒng)來(lái)解決協(xié)作應(yīng)用在移動(dòng)場(chǎng)景下所面臨的問(wèn)題。設(shè)計(jì)一套既支持復(fù)雜機(jī)器學(xué)習(xí)算法,又能實(shí)時(shí)響應(yīng)用戶請(qǐng)求的推薦引擎架構(gòu)是該應(yīng)用設(shè)計(jì)的首要目標(biāo)。論文通過(guò)使用分布式消息系統(tǒng)Kafka搭配最新分布式并行計(jì)算框架Spark、高性能內(nèi)存數(shù)據(jù)庫(kù)Redis與分布式NoSQL數(shù)據(jù)庫(kù)HBase,設(shè)計(jì)并實(shí)現(xiàn)了“在線-近線-離線”三段式混合推薦引擎,離線部分進(jìn)行批量計(jì)算更新推薦模型,近線部分進(jìn)行增量計(jì)算異步推送推薦結(jié)果,在線部分響應(yīng)用戶實(shí)時(shí)請(qǐng)求對(duì)拼單任務(wù)進(jìn)行過(guò)濾和排序,通過(guò)實(shí)驗(yàn)驗(yàn)證了該技術(shù)方案的可行性與合理性�，F(xiàn)有的協(xié)作活動(dòng)推薦算法多是對(duì)傳統(tǒng)算法的直接遷移,沒(méi)有充分考慮移動(dòng)場(chǎng)景下協(xié)作活動(dòng)的特點(diǎn)。移動(dòng)場(chǎng)景提供了用戶位置、用戶社交關(guān)系等新的信息,眾包協(xié)作用戶之間構(gòu)成了基于事件的社交網(wǎng)絡(luò)(Event-based Social Network,EBSN)。論文分別從 EBSN 下的語(yǔ)義特征、位置特征以及社交影響三個(gè)方面進(jìn)行分析研究,通過(guò)分析整合上述因素對(duì)用戶參與協(xié)作活動(dòng)的影響程度,提出了 一種基于鄰域隱含因子的奇異矩陣分解算法。實(shí)驗(yàn)結(jié)果表明,相較于傳統(tǒng)的預(yù)測(cè)模型,論文提出的模型可以有效緩解冷啟動(dòng)問(wèn)題,能夠更準(zhǔn)確地預(yù)測(cè)用戶是否會(huì)參與活動(dòng),進(jìn)而為用戶個(gè)性化推薦協(xié)作活動(dòng)。基于上述推薦系統(tǒng)架構(gòu)和推薦算法,論文設(shè)計(jì)實(shí)現(xiàn)了一個(gè)提供協(xié)作拼單服務(wù)的移動(dòng)應(yīng)用。該應(yīng)用可以基于用戶興趣和實(shí)時(shí)上下文信息對(duì)拼單做智能排序和推薦,響應(yīng)用戶的即時(shí)協(xié)作需求,有效解決了移動(dòng)場(chǎng)景下信息過(guò)載的問(wèn)題。
[Abstract]:Crowdsourcing, as a new mode of social production, can greatly integrate social resources and create huge commercial value. With the development of Internet technology, crowdsourcing is no longer a simple enterprise behavior, but a socialized behavior of national cooperation. The rise of mobile Internet provides opportunities for the mobility and fragmentation of crowdsourcing collaboration, but at the same time challenges. The existing crowdsourcing collaboration applications present information directly after classification, which not only results in information overload, but also is difficult to quickly respond to users' real-time collaboration tasks in mobile scenarios. Based on the practical requirements, this paper introduces a recommendation system to solve the problems faced by collaborative applications in mobile scenarios. The primary goal of this application design is to design a recommendation engine architecture that supports complex machine learning algorithms and can respond to user requests in real time. By using distributed message system (Kafka) combined with the latest distributed parallel computing framework (Spark), high-performance memory database (Redis) and distributed NoSQL database (HBase), this paper designs and implements a three-segment hybrid recommendation engine named "On-Line, Near-Off-Line". The offline part updates the recommendation model by batch calculation, the near-line part incrementally calculates the asynchronous push recommendation results, and the online part filters and sorts the single task in response to the user's real-time request. The feasibility and rationality of the technical scheme are verified by experiments. Most of the existing collaborative activity recommendation algorithms are direct migration of traditional algorithms and do not fully consider the characteristics of collaborative activities in mobile scenarios. Mobile scene provides new information such as user location, user social relationship and so on. The crowdsourcing collaboration between users constitutes an event-based social network (EBSN). In this paper, the semantic features, location features and social impact of EBSN are analyzed, and the influence of integrating the above factors on the users' participation in collaborative activities is analyzed. A singular matrix decomposition algorithm based on neighborhood implicit factor is proposed. The experimental results show that compared with the traditional prediction model, the proposed model can effectively alleviate the cold start problem, can more accurately predict whether users will participate in the activities, and then recommend collaborative activities for users. Based on the above recommendation system architecture and recommendation algorithm, a mobile application is designed and implemented. The application can intelligently sort and recommend the order based on the user's interest and real-time context information, and can respond to the user's real-time cooperation demand, and effectively solve the problem of information overload in the mobile scene.
【學(xué)位授予單位】：北京郵電大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類(lèi)號(hào)】：TP391.3

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