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Session aware recommender system in e-commerce.

機(jī)譯：電子商務(wù)中的會(huì)話感知推薦系統(tǒng)。

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As online shopping becomes to be popular, the recommender system in e-commerce sites is an increasingly popular business tool to increase sales. Researchers and industry practitioners are looking for all possible approaches to improve recommendation performance. Even a minor improvement could lead to a big business return.;Traditional approaches of recommender systems include content-based methods and collaborative filtering methods. For example, if a user viewed some cameras in the website, the system learns that the user is interested in cameras and recommends more similar items to the user. Yet it might not match with the user's true purchase intention. In real-world applications, recommender systems could leverage more information from the user, both information within a single session and information across sessions. To solve this problem, we propose to investigate the session-aware recommender system in e-commerce. Such system can understand a user's short-term goal and long-term preference, in order to recommend appropriate items accordingly.;We first explore how to integrate the complementary information (e.g. the user's purchase information, search information and so on) within a single session to build a unified recommender system. We analyze the available information for the unified model, including the user's history-related information, the search-related information and the product's marketing-related information. Three unified models are proposed and compared to integrate different pieces of information.;To go beyond making recommendations within a single session, we then study how to make better recommendations across sessions. To make recommendations based on a user's previous behavior in earlier sessions, we need to understand how users make purchase decisions across sessions. Earlier research in economics and marketing indicates that a consumer usually makes purchase decision(s) based on the product's marginal net utility (i.e., the marginal utility minus the product price). Utility is defined as the satisfaction or pleasure a user gets when purchasing the corresponding product. A rational consumer chooses the product to purchase in order to maximize the total net utility. To better match users' purchase decisions in the real world, we explore how to recommend products with the highest marginal net utility in e-commerce sites. Inspired by the Cobb-Douglas utility function in consumer behavior theory, we propose a novel utility-based recommendation framework. The framework can be utilized to revamp a family of existing recommendation algorithms.;To further incorporate the time interval between sessions into the system, we propose and study a new problem: how to recommend the right product at the right time? We adapt the proportional hazards model in survival analysis and propose the new opportunity model in e-commerce. The new model estimates the joint probability of a user making a follow-up purchase of a particular product at a particular time. This joint purchase probability can be leveraged by recommender systems in various scenarios, including a zero-query pull-based scenario or a proactive push-based email promotion scenario.;We evaluate the soundness of our models with multiple metrics. Experimental results with a real-world e-commerce website (shop.com) show that they have decent predictability of the user's purchase behavior within a session and across sessions. In addition, the models significantly improve the conversion rate in pull-based systems and the user satisfaction/utility in push-based systems. In this dissertation, we first introduce the motivation of the work. Secondly, we report some state-of-art related work of this topic. Thirdly, models are proposed to tackle the problem, followed by the experimental results. Then we summarize our contribution in both the research field of recommender systems and the e-commerce domain.
機(jī)譯：隨著在線購(gòu)物變得越來(lái)越流行，電子商務(wù)站點(diǎn)中的推薦系統(tǒng)已成為一種日益流行的增加銷售額的商業(yè)工具。研究人員和行業(yè)從業(yè)人員正在尋找所有可能的方法來(lái)改善推薦績(jī)效。即使是很小的改進(jìn)也可以帶來(lái)巨大的業(yè)務(wù)回報(bào)。推薦系統(tǒng)的傳統(tǒng)方法包括基于內(nèi)容的方法和協(xié)作過(guò)濾方法。例如，如果用戶查看了網(wǎng)站中的某些相機(jī)，則系統(tǒng)得知該用戶對(duì)相機(jī)感興趣，并向該用戶推薦更多類似物品。但這可能與用戶的真實(shí)購(gòu)買意圖不符。在實(shí)際應(yīng)用中，推薦系統(tǒng)可以利用來(lái)自用戶的更多信息，包括單個(gè)會(huì)話中的信息和跨會(huì)話中的信息。為了解決這個(gè)問(wèn)題，我們建議研究電子商務(wù)中的會(huì)話感知推薦器系統(tǒng)。這樣的系統(tǒng)可以了解用戶的短期目標(biāo)和長(zhǎng)期偏好，以便相應(yīng)地推薦合適的項(xiàng)目。我們首先探討如何在單個(gè)信息中整合補(bǔ)充信息（例如用戶的購(gòu)買信息，搜索信息等）。會(huì)議以建立統(tǒng)一的推薦系統(tǒng)。我們分析統(tǒng)一模型的可用信息，包括與用戶的歷史記錄有關(guān)的信息，與搜索有關(guān)的信息以及與產(chǎn)品的營(yíng)銷有關(guān)的信息。提出并比較了三個(gè)統(tǒng)一的模型，以整合不同的信息。為了超越在單個(gè)會(huì)話中提出建議，我們?nèi)缓笱芯咳绾卧诟鱾€(gè)會(huì)話中提出更好的建議。為了根據(jù)用戶在較早會(huì)話中的先前行為提出建議，我們需要了解用戶如何在各個(gè)會(huì)話中做出購(gòu)買決定。較早的經(jīng)濟(jì)學(xué)和市場(chǎng)營(yíng)銷研究表明，消費(fèi)者通常根據(jù)產(chǎn)品的邊際凈效用（即邊際效用減去產(chǎn)品價(jià)格）做出購(gòu)買決定。效用定義為用戶購(gòu)買相應(yīng)產(chǎn)品時(shí)獲得的滿足感或愉悅感。理性的消費(fèi)者選擇要購(gòu)買的產(chǎn)品，以使總凈效用最大化。為了更好地匹配現(xiàn)實(shí)世界中用戶的購(gòu)買決策，我們探索了如何在電子商務(wù)站點(diǎn)中推薦邊際凈效用最高的產(chǎn)品。受消費(fèi)者行為理論中Cobb-Douglas效用函數(shù)的啟發(fā)，我們提出了一種新穎的基于效用的推薦框架。該框架可用于改進(jìn)一系列現(xiàn)有的推薦算法。為了將會(huì)話之間的時(shí)間間隔進(jìn)一步納入系統(tǒng)，我們提出并研究了一個(gè)新問(wèn)題：如何在正確的時(shí)間推薦正確的產(chǎn)品？我們?cè)谏娣治鲋胁捎帽壤L(fēng)險(xiǎn)模型，并提出了電子商務(wù)中的新機(jī)會(huì)模型。新模型估計(jì)用戶在特定時(shí)間進(jìn)行特定產(chǎn)品的后續(xù)購(gòu)買的聯(lián)合概率。推薦系統(tǒng)可以在各種情況下利用這種聯(lián)合購(gòu)買的可能性，包括基于零查詢拉式的情況或基于主動(dòng)式推送的電子郵件促銷方案。;我們使用多個(gè)指標(biāo)評(píng)估模型的穩(wěn)健性。真實(shí)世界的電子商務(wù)網(wǎng)站（shop.com）的實(shí)驗(yàn)結(jié)果表明，它們?cè)谝粋€(gè)會(huì)話內(nèi)和會(huì)話之間對(duì)用戶的購(gòu)買行為具有可預(yù)測(cè)的可預(yù)測(cè)性。此外，這些模型顯著提高了基于拉式系統(tǒng)的轉(zhuǎn)換率和基于推式系統(tǒng)的用戶滿意度/效用。本文首先介紹了工作動(dòng)機(jī)。其次，我們報(bào)告了與該主題相關(guān)的一些最新技術(shù)。第三，提出了解決該問(wèn)題的模型，然后給出了實(shí)驗(yàn)結(jié)果。然后，我們總結(jié)了我們?cè)谕扑]系統(tǒng)研究領(lǐng)域和電子商務(wù)領(lǐng)域中的貢獻(xiàn)。

著錄項(xiàng)

  • 作者

    Wang, Jian.;

  • 作者單位

    University of California, Santa Cruz.;

  • 授予單位 University of California, Santa Cruz.;
  • 學(xué)科 Computer Science.
  • 學(xué)位 Ph.D.
  • 年度 2013
  • 頁(yè)碼 145 p.
  • 總頁(yè)數(shù) 145
  • 原文格式 PDF
  • 正文語(yǔ)種 eng
  • 中圖分類
  • 關(guān)鍵詞

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