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模拟国际会议演讲稿

模拟国际会议演讲稿
模拟国际会议演讲稿

Recsplorer:Recommendation Algorithms Based on Precedence Mining

1. Introduction

Thank you very much, Dr. Li, for your kind introduction. Ladies and gentlemen, Good morning! I am honored to have been invited to speak at this conference. Before I start my speech, let me ask a question. Do you think recomemdations from others are useful for your internet shopping? Thank you. It is obvious that recommendations play an important role in our daily consumption decisions.

Today, my topic is about Recommendation Algorithms Based on Precedence Mining. I want to share our interesting research result on recommendation algorithms with you. The content of this presentation is divided into 5 parts: in session 1, I will intruduce the tradictional recommendation and our new strategy; in session 2, I will give the formal definition of Precedence Mining; in session 3, I will talk about the novel recommendation algorithms; experimental result will be showed in session 4; and finally, I will make a conclusion.

2. Body

Session 1: Introduction

The picture on this slide is an instance of recommemdation application on amazon.

Recommender systems provide advice on products, movies,web pages, and many other topics, and have become popular in many sites, such as Amazon. Many systems use collaborative filtering methods. The main process of CF is organized as follow: first, identify users similar to target user; second, recommend items based on the similar users. Unfortunately, the order of consumed items is neglect. In our paper, we consider a new recommendation strategy based on precedence patterns. These patterns may encompass user preferences, encode some logical order of options and capture how interests evolve.

Precedence mining model estimate the probability of user future consumption based on past behavior. And these probabilities are used to make recommendations. Through our experiment, precedence mining can significantly improve recommendation performance. Futhermore, it does not suffer from the sparsity of ratings problem and exploit patterns across all users, not just similar users.

This slide demonstrates the differences between collaborative filtering and precedence mining. Suppose that the scenario is about course selection. Each quarter/semester a student chooses a course, and rates it from 1 to 5. Figure a) shows five transcripts, a transcript means a list of course. U is our target student who need recommendations. Figure b) illustrates how CF work. Assume similar users share at least two common courses and have similar rating, then u3 and u4 are similar to u, and their common course h will be a recommendation to u. Figure c) presents how precedence mining work. For this example, we consider patterns where one course follows another. Suppose patterns occour at least two transcrips are recognized as significant, then (a,d), (e,f) and (g,h) are found out. And d, h, and f are recommendation to u who has taken a, g and e.

Now I will a probabilistic framework to solve the precedence mining problems. Our target user has selected course a , we want to compute the probability course x will follow, i.e., Pr[x|a]. howerve, what we really need to calculate is Pr[x|a﹁X] rather than Pr[x|a]. Because in our context, we are deciding if x is a good recommendation for the target user that has taken a. Thus we know that our target user’s transcript does not have x before a. For instance, the transcript no. 5 will be omitted. In more common situation, our target user has taken a list of co urses, T = {a,b,c,…} not just a. Thus, what really need is Pr[x|T﹁X]. The question is how to figure out this probability. I will answer it later.

Session 2: Precedence Mining

We consider a set D of distinct courses. We use lowercase letters (e.g., a, b, … ) to refer to courses in D. A transcript T is a sequence of courses, e.g., a -> b -> c -> d. Then the definition of Top-k Recommendation Problem is as follows. Given a set transcripts over D for n users, the extra transcript T of a target user, and a desired number of recommendations k, our goal is to:

1. Assign a score score(x) (between 0 and 1) to every course x∈D that reflects how likely it is the target student will be interested in taking x. If x ∈T , then score(x) = 0.

2. Using the score function, select the top k courses to recommend to the target user.

To compute scores, we propose to use the following statistics, where x, y ∈D: f(x): the number of transcripts that contain x.

g(x; y): the number of transcripts in which x precedes course y.

This slide shows the calculation result of f(x) and g(x,y). For example, from the table, we know that f(a) is 10 and g(a,c) is 3.

We propose a precedence mining model to solve the Top-k Recommendation Problem. Here are some notation: x﹁y, which we have memtioned in session 1, refers to transcript where x occurs without a preceding y; x﹁y refers to transcript where x occurs without y following it. We use quantities f(x) and g(x,y) to compte probabilities that encode the precedence information. For instance, from formular 1 to 7. I would not tell the detail of all formulars. We just pay attention to formular 5, note that this quantity above is the same as: Pr[x﹁y|y﹁x] which will be used to compute score(x).

As we know, the target user usually has taken a list of courses rather than a course, so we need to extent our probability calculation formulars. For example, suppose T={a,b}, Pr[x﹁T] the probability x occurs without either an a or b preceding it; Pr[x﹁T] the probability x occurs without either an a or b following it. This probability can be calculated exactly. So how to calculate it?

Session 3: Recommendation Algorithms

Let’s review session 2. The main goal of the recommendation algorithms is to calculate the score(x), and then select the top k courses based on these scores. Traditional recommendation algorithms compute a recommendation score for a course x in D only based on its frequency of occurence. It does not take into account the courses taken by the target user.

Our recommendation algorithms called SingleMC conquer the shortcoming of the traditional ones. It computes the score(x) using the formular 5. The detail is as follows: a student with a transcrip T of taken courses, for the course y ∈T, if y and x appear together in transcripts satisfies the threshold θ, then compute the Pr[x﹁y |y﹁x], reflecting the likelihood the student will take course x and ignoring the effect of the other courses in T; finally the maximum of Pr[x﹁y |y﹁x] is choosen as the score(x).

Here is the calculation formular of score(x) of SignleMC. For example, with the higer score, d will be recommended.

Another new recommendation algorithm named Joint Probabilities algorithm, JointP for short, is proposed. Unlike SingleMC, JointP takes into account the complete set of courses in a transcript. In formular 12, we cannot compute its quantity exactly, Remember this problem we have mentioned. Our solution is to use approximations. This slide is about the first approximating formular. And this the second approximating formular.

The system is courseRand, and data set for experiment contains 7,500 transcripts.

This slide shows the new recommendation algoritms with black color and the traditional ones with blue color.

The chart on this slide indicates our new recommendation algorithms beat the traditional ones in precision, because the former ones exploit patterns across all users, while the latter ones just use the similar users.

The chart on this slide points out our new recommendation algorithms also beat the traditional ones in coverage for the same reason.

Session 5: Conclusion and Summary

In conclusion, we proposed a novel precedence mining model, developed a probabilistic framework for making recommendations and implemented a suite of recommendation algorithms that use the precedence information. Experimental result shows that our new algorithms perform better than the traditional ones, and our recommendation system can be easily generalized to other scenarios, such as purchases of books, DVDs and electronic equitment.

To sum up, first, I introduced the motivation and the outline of work; second, I gave the definition of precedence mining model; third, I described some new recommendation algorithms using precedence information; forth, I showed our experimental results to compare the new algorithms with traditional ones. Finally, I made a conclusion of our work..

That’s all. Thank you! Are there any questions?

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非常有意义的事情。 席学易国际易学研究院是专业从事易学的学习培训和策划服务的专业机构,即培养培训一般的易学人才,同时也培养一批具有掌握易学术数高端技术的专业高端人才,十七年来,席学易国际易学研究院始终站在实用易学的前沿。在此,我代表本院向大家发出邀请,诚望诸位同仁将席学易国际易学研究院当成你们的交流学习园地,一如既往的支持我们,让我们携起手来,为易学的研究及易学事业在不远的将来出现更加美好的未来而共同努力。 各位领导,各位来宾,各位专家学者和各位易学同仁,本次会议的召开,必将使我们的易学事业向前更推进一步。我们满怀信心的认为,只要各位同仁齐心协力,并切实有效地发挥易学在现代社会中的作用,未来的易学研究事业,一定会更加辉煌。“乾道变化,各正性命,保合太和,乃利贞”;“君子黄中通理,正位居体,美在其中,而畅于四支,发于事业,美之至也。”我们相信,本次大会在国际易学联合会的主办下,在北京联合大学易学与经济发展研究中心的学术支持下,我们各位代表秉持“修辞立其诚”的学术精神,就易学的各种问题和研究“发己之所欲发,言己之所欲言”,会议必将取得丰硕成果,以学聚、问辩、宽居、仁行的精神,园满完成本届大会的使命。 祝各位领导、各位来宾、各位与会代表身体健康,精神愉快,自天佑之,吉无不利。 祝大会取得园满成功! 谢谢大家!

国际会议作报告英语发言稿

Thank you, prof. …. My name is ….. I’m from ….. I am very pleased to be here to join this forum. The topic of my presentation is properties of rapid construction materials for soil pavement of field airfield. As is shown in the picture, the main parts of my research are about soil pavement. My presentation will include these four parts: First, some background information about this research; second, the main work we have done; third, some conclusions we have got and the last: innovation and presentation of our published papers. Why I choose this item? I think it can be illustrated from the following four parts. First, the existing quantity of airfields is still not sufficient and the airfields have many shortcomings especially in war time. Second, the complementary facilities, such as highway runways are far less than airfields, however, have more weakness. Third, a certain amount of field airfield is quite necessary considering some emergencies such as rescue and disaster relief. Forth, the field airfield can fill the void of airfield and they can be combined to be airfield network. The meaning and aim of this research contains three parts. Fast, convenient and validity, fast means the field airfield must be constructed

模拟国际会议演讲稿

1. Introduction Thank you very much. Mr. Jiao, for your kind introduction. Ladies and gentlemen, Good afternoon! My name is Lijia, came from Harbin Engineering University. I am honored to have been invited to speak at this conference. Before I start my speech, let me ask a question. Do you know what can affect the properties of foam concrete? Do you think how to reinforced the properties of foam concrete?Most of the investigations on foam concrete in the past have been con?ned(被限于)to neat cement paste, cement paste with partial replacement with admixtures and to cement–sand mixes. Today, my topic is about In?uence of ?ller type on the properties of foam concrete. I want to share our interesting research result on reinforced concrete frame with you. The content of this presentation is divided into 4 parts: In section 1, I will introduce what is the foam concrete. In section 2, I will talk about Parameters investigated and mix compositions.In section 3, I will give E?ect of water–solids ratio on design density. And finally, I will make a conclusion. 2. Body Section 1: the foam concrete Now, I will introduce the foam concrete. Pre-formed(成型的) foam concrete is manufactured(加工)by adding foam, prepared by aerating (充气)a foaming agent solution, to cement paste or cement mortar(灰

国际会议发言稿-英文(精选多篇)复习过程

国际会议发言稿-英文(精选多篇)

国际会议发言稿英文(精选多篇) 第一篇:国际会议--英文 表示欢迎:good morning, ladies and gentlemen, i’m privileged to welcome you all to “the current status and future development of cotton machinery” conference. 自我介绍:let me introduce myself i am dr lifrom beijing, china, and i am going to be the chair for this morning’s session. 介绍议题: this conference will focus on the discussion of the various aspects of cotton machinery. it includes the application of cotton machinery and the current status.and we will also discuss the prospect of cotton machinery. 介绍报告人: now it gives me great pleasure to introduce today’sparticipants. they are :han dan, the director general of the united nations industrial development organization(联合国工业发展组织总干事) zhang hui, president of china power investment group company. shang fengjiao, professor of physics ,head of american nuclear energy association. now, we welcome the opening of han dan do for us(现在,我们欢迎aa 为我们做致开幕词) aa上台

keynote speech 会议演讲词 英文

A Keynote Speech at the 2nd Symposium on Environmental Pollution Respected President, Ladies and Gentlemen, It's my great honor to make the keynote speech at this conference. Nowadays, PM2.5 has become an environmental problem we can’t neglect any more.A recent research shows very high levels of PM2.5 in a broad swath stretching from the Saharan Desert in Northern Africa to Eastern Asia. PM2.5 refers to the atmosphere diameter less than or equal to the particles 2.5 microns. Although PM2.5 only group content of a few earth atmospheric composition points, but it has an important influence on air quality and visibility, especially in winter. Besides, PM2.5, which has a small particle size, contains a lot of toxic and harmful substances. As a result, longtime exposure to PM2.5 which can go directly to the alveoli of the lungs, is a major health hazard. In fact, the American Heart Association estimates that in the United States alone, PM2.5 air pollution spark some 60, 000 deaths a year. Recently, PM2.5 has influenced our life obviously. There are fog and hazy weather in big city from time to time, which even causes many protest haze activities in China. And many factors may contribute to it. Firstly, the large amount of pollutant emission, including coal-fired pollution, motor vehicle emission, industrial pollution and dust is the root

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