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CS 276/286 LING: Information Retrieval dan Web Cari Spring 2013
Chris Manning, Pandu Nayak, dan Prabhakar Raghavan
Kursus Info Silabus Coursera Piazza Buku

CS 276/286 LING: Retrieval Informasi dan Web Search
Kuliah: 3 unit, Tu / Th 4:15-05:30 di Auditorium NVIDIA (tersedia secara online melalui SCPD )
Staf e-mail: cs276-spr1213-staff@lists.stanford.edu

Kursus Deskripsi

Teknik dasar dan lanjutan untuk sistem informasi berbasis teks: pengindeksan teks efisien, Boolean dan vektor model pengambilan space, masalah evaluasi dan antarmuka, pencarian Web termasuk merangkak, algoritma berbasis link-, dan Web metadata, pengelompokan teks / Web, klasifikasi; pertambangan teks .
Kebijakan Informasi (grading, dll)
Prasyarat: CS 107, CS 109, CS 161. Idealnya seluruh CS Mayor Inti.

Online Resources

Coursera: CS 276 akan memanfaatkan video online dan kuis serta ceramah hidup kelas, presentasi, dan laboratorium. Kunjungi Coursera untuk menemukan potongan video dan latihan online. Kami akan posting tugas pemrograman yang diperlukan dan masalah set melalui Coursera, jadi mendaftar segera (dengan Stanford ID Anda)!
Piazza: Kami sangat menyarankan agar Anda posting pertanyaan tentang tugas, kuliah atau materi kuliah umum di Piazza . Ini memfasilitasi diskusi antara siswa, dan memungkinkan orang lain untuk mendapatkan keuntungan dari diskusi juga. Bahkan jika Anda pikir pertanyaan Anda adalah khusus untuk pelaksanaan Anda, Anda dapat menggunakan fitur 'pertanyaan pribadi' untuk mengatasi pertanyaan kepada staf khusus. Kami akan menggunakan Piazza untuk mengirim saja pengumuman, jadi pastikan untuk mendaftar di Piazza segera juga. Berikut adalah cepat pengenalan video .
Staf Email: Jika Anda benar-benar memiliki pertanyaan tentang situasi Anda secara khusus, bahwa Anda tidak berpikir tidak sesuai untuk forum kelas (bahkan bukan pertanyaan pribadi), silahkan email staf mailing list di cs276-spr1213-staf @ daftar. stanford.edu .
Pengumuman:
Jika Anda tidak terdaftar dalam kursus tapi ingin menerima saja pengumuman, Anda dapat berlangganan ke mailing list tamu cs276-spr1213-tamu .

CS 276 Informasi Staf

Profesor:
Chris Manning , Jam Kantor: Wednesday 3-4 PM, Gates 158
Pandu Nayak , Jam kantor: setelah kelas dengan pengaturan sebelumnya
Prabhakar Raghavan , Jam kantor: setelah kelas dengan pengaturan sebelumnya
TA: Sonal Gupta (Kepala TA), Shui Hu, Anshul Mittal, Rukmani RaviSundaram, Thang Luong

Jam TA Office

Untuk jadwal rinci, memeriksa kalender Google menggunakan ID: f0qejqeg52qo292jk3i44lqd04@group.calendar.google.com
Jam Daytime Office
  • Senin: Gates B24A 10 11:00
  • Rabu: Gates B26B 11:00-12:00
  • Jumat: Gates B26B 10 11:00
Coding Sesi
  • Selasa: Bldg 160 Room 332 6-9
  • Rabu: Bldg 160 Room 332 6-09:00
Untuk siswa SCPD, kami akan tersedia melalui Google hangout, link yang akan diposting di Piazza.

Silabus

Rincian jadwal, video, slide dan daftar bacaan akan diperbarui sebagai kuartal berlangsung.
Tanggal Dalam Kelas Tugas Siapa Bahan
Tue 2 Apr Pengantar saja: Diskusi isu dalam pencarian ditambah Web 1: pencarian Web, iklan, SEO
PN Coursera:
Boolean Retrieval (IIR Ch. 1)
Catatan:
[ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]
Bacaan:
IIR Ch. 1
MG 3.2
MIR 8.2
Drama Shakespeare
Thu 4 Apr Pembicara tamu: Jeff Dean pada evolusi pencarian Google dan sistem pencarian PA1 out Jeff Coursera:
Daftar Kosakata Syarat dan Postingan (IIR Ch. 2)
Catatan:
[ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]
Bacaan:
IIR Ch. 2
MG 3.6, 4.3
MIR 7.2
Stemmer Porter (MIR) , Porter berasal algoritma (Official)
Daftar melewatkan masak (Pugh 1990)
Frase cepat query dengan indeks gabungan (Williams, Zobel, Bahle 2004)
Frase efisien query dengan indeks tambahan (Bahle, Williams, Zobel 2002) MapReduce: pengolahan data disederhanakan pada kelompok besar (Dean dan Ghemawat 2004)
Tue 9 Apr Kelas laboratorium: Menulis algoritma merge untuk kedekatan query menggunakan indeks posisional
PrimaryPad
Bersama gdoc: http://bit.ly/HgCAdP
Persimpangan kerangka
Kerangka Positional
Jawaban Positional
[Membutuhkan sunet ID]
PS1 Out CM Coursera:
Index Konstruksi (IIR Ch. 4)
Catatan
[ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]
Bacaan
IIR Ch. 4
MG Ch. 5
Konstruksi yang efisien indeks single-pass untuk database teks (Heinz dan Zobel 2003)
Thu 11 Apr Lab kelas: Algoritma untuk daftar posting kompresi
PrimaryPad
compress.py
Hasil gdoc spreadsheet
Slide kompresi Jeff Dean
Simple-9 (Anh / Moffat)
Wikipedia: Golumb / Beras , Huffman , kode gamma Elias , kode unary

CM Coursera:
Indeks Kompresi (IIR Ch. 5)
Catatan:
[ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]
Bacaan:
IIR Ch. 5
MG 3.3, 3.4
Kompresi indeks terbalik untuk evaluasi permintaan cepat (Scholer et al 2002.)
Kompresi indeks terbalik menggunakan kode biner kata-blok (Anh dan Moffat 2005)
Tue 16 Apr Koreksi ejaan
[ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]
Karena PS1 PR Coursera:
Kamus dan Toleran Retrieval (IIR Ch. 3)
Catatan:
[ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]
Bacaan:
Bagaimana menulis korektor ejaan (Peter Norvig)
IIR Ch. 3
MG 4.2
Teknik untuk secara otomatis mengoreksi kata-kata dalam teks (Kukich 1992)
Menemukan pertandingan perkiraan dalam leksikon besar (Zobel dan Dart 1995)
Generasi dan PERINGKAT Spelling Koreksi Kesalahan (Tillenius) Efisien
Thu 18 Apr Lab Kelas: Mapreduce dengan Python
PrimaryPad
mapreduce.py
count_words.py
lmtrain.py
idf.py
index.py
anchors.py
index_with_anchors.py
PA1 karena
PA2 out
PN Coursera:
Vector Space Model (IIR Ch. 6)
Catatan:
[ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]
[ Jeff Dean Slides ]
Bacaan:
IIR 6.2 - 6.4.3
Tue 23 Apr Probabilistic IR: Binary Independence Model
[ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]
PS2 Out PN Bacaan:
IIR 11
Thu 25 Apr BM25, BM25F, dan sinyal peringkat [ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]

PN Bacaan:
IIR 11
Tue 30 Apr Evaluasi. Presisi / ingat, NDCG, dengan menggunakan rasio klik per tayang
[ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]
Karena PS2 PR Bacaan:
IIR Ch. 8
MG 4.5
MIR Ch. 3
Thu 2 Mei Masalah sistem dalam temu efisien dan scoring
[ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]
PA2 karena
PA3 out
PR Coursera:
Skor Computing (IIR Ch. 7)
Catatan:
[ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]
Bacaan:
IIR Ch. 7 , IIR 6.1
Tue 7 Mei Lucene Tutorial
[ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]
PS3 Out PN
Thu 9 Mei KLASIFIKASI 1 + 2: Naif Byes, kNN, batas keputusan
[ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]

PR Bacaan:
IIR Ch. 11
IIR Ch. 13
Mesin pembelajaran dalam kategorisasi teks otomatis (Sebastiani 2002)
Sebuah pemeriksaan ulang metode kategorisasi teks (Yang et al. 1999)
Perbandingan model event untuk naif Bayes klasifikasi teks (McCallum et al. 1998)
Tom Mitchell. Machine Learning. McGraw-Hill, 1997.
Buka Calais
Weka
Reuters-21578
Mengatasi asumsi miskin Naive Bayes classifier (Rennie et al. 2003)
IIR Ch. 14
Mesin pembelajaran dalam kategorisasi teks otomatis (Sebastiani 2002)
Tom Mitchell. Machine Learning. McGraw-Hill, 1997.
Sebuah pemeriksaan ulang metode kategorisasi teks (Yang et al. 1999)
Mengevaluasi dan mengoptimalkan sistem klasifikasi teks otonom (Lewis 1995)
Trevor Hastie, Robert Tibshirani, Jerome Friedman Elemen statistik Learning:. Data Mining, Inferensi, dan Prediksi. Springer-Verlag, New York, 2001.
Tue 14 Mei KLASIFIKASI 3. Mesin dukungan vektor
[ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]
Karena PS3 CM Bacaan:
IIR Ch. 15
Sebuah tutorial pada mesin dukungan vektor untuk pengenalan pola (Burges 1998)
Menggunakan SVM untuk teks kategorisasi (Dumais1998)
Algoritma pembelajaran induktif dan representasi untuk teks kategorisasi (Dumaiset a. 1998)
A Re-pemeriksaan metode kategorisasi teks (Yang et al. 1999)
Teks kategorisasi berdasarkan metode klasifikasi linear regularized (Zhang et al. 2001)
Trevor Hastie, Robert Tibshirani, Jerome Friedman Elemen statistik Learning:. Data Mining, Inferensi, dan Prediksi. Springer-Verlag, New York, 2001.
Reuters-21578
Thorsten Joachims. Belajar untuk Klasifikasikan Teks menggunakan Support Vector Machines. Kluwer, 2002.
Sebuah analisis fungsi kerugian untuk metode klasifikasi dalam teks kategorisasi (Li et al. 2003)
Thu 16 Mei Web 2: Belajar untuk peringkat.
[ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]
PA3 karena
PA4 out
CM Bacaan:
IIR 6.1.2-3 , IIR 15.4
Model diskriminatif untuk pencarian informasi (Nallapati 2004)
Beradaptasi peringkat SVM untuk mendokumentasikan pengambilan (Cao et al. 2006)
Sebuah metode vektor dukungan untuk mengoptimalkan presisi rata-rata (Yue et al. 2007)
Dataset LETOR patokan
Tue 21 Mei Diundang bicara: Sriram Sankar dari Facebook pada Grafik Pencarian
Sriram Sankar
Thu 23 Mei CLUSTERING 1: k-means, HAC
[ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]

PN Bacaan:
IIR Ch. 16 , IIR 17,1-3
Tue 28 May CLUSTERING 2. LSI
[ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]
PS4 Out CM Bacaan:
IIR Ch. 18
Thu 30 Mei Web 3: Analisis Link.
[ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]
PA4 karena PR Bacaan:
IIR Ch. 21
Peringkat perbatasan web (Eiron et al. 2004)
Kerangka WebGraph I: teknik kompresi (. Boldi et al 2004)
Metode ekstrapolasi untuk mempercepat perhitungan PageRank (Kamvar et al. 2003)
Mencari web kerja (Fagin et al. 2003
Tue 4 Jun Web 4: Crawling, dekat-dups PS4 karena PR Catatan:
[ powerpoint ]
[ PDF / 6 ]
[ PDF / 1 ]
Bacaan:
IIR Ch. 20
Mercator: A scalable, web crawler extensible (. Heydon et al, 1999)
Sebuah standar untuk pengecualian robot
Thu 6 Jun Tidak ada kelas.


Fri 7 Jun Ujian akhir (12:15-3:15)
Praktek Akhir
Praktek Solusi Akhir



Diperlukan Textbook:

IIR = Pengantar Information Retrieval, oleh C. Manning, P. Raghavan, dan H. Schütze. Cambridge University Press, 2008.
Buku ini tersedia dari toko buku Stanford (atau penyetor buku favorit Anda). Anda juga dapat men-download dan bab cetak di situs buku . (Kami akan menghargai setiap laporan kesalahan ketik atau masalah-tingkat yang lebih tinggi untuk pencetakan ketiga. Terima kasih.)

Lainnya Baik IR Books:

MG = Mengelola Gigabytes, oleh I. Witten, A. Moffat, dan T. Bell.
Irah = Information Retrieval: Algoritma dan Heuristic oleh D. Grossman dan O. Frieder.
MIR = Informasi modern Retrieval, oleh R. Baeza-Yates dan Ribeiro-Neto B..
FOA = Menemukan Out Tentang, oleh R. Belew.
MTW = Pertambangan Web, oleh S. Chakrabarti.
FSNLP = Yayasan Statistik Natural Language Processing, oleh C. Manning dan H. Schütze.
Buku-buku ini semua memiliki informasi yang berguna tentang topik yang kita bahas dan direkomendasikan sebagai referensi. MG sangat baik sebagai referensi rinci untuk IR teknis pada semester pertama tentu saja. MTW mencakup banyak topik dari bagian akhir kursus.

Lebih banyak sumber daya rinci dapat ditemukan di sini .

Dosen:

CM = Chris Manning
PN = Pandu Nayak
PR = Prabhakar Raghavan
Program terkait:
Pengantar Komputasi Advertising ( http://www.stanford.edu/class/msande239/ )

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Network technologies would give us more powerfull servers
"Did I not tell you that we of the ruling class owned all the land, all the forest, everything? Any food-getter who would not get food for us, him we punished or compelled to starve to death. And very few did that. They preferred to get food for us, and make clothes for us, and prepare and administer to us a thousand—a mussel-shell, Hoo-Hoo—a thousand satisfactions and delights. And I was Professor Smith in those days—Professor James Howard Smith. And my lecture courses were very popular—that is, very many of the young men and women liked to hear me talk about the books other men had written.

"And I was very happy, and I had beautiful things to eat. And my hands were soft, because I did no work with them, and my body was clean all over and dressed in the softest garments—
"He surveyed his mangy goat-skin with disgust.

"We did not wear such things in those days. Even the slaves had better garments. And we were most clean. We washed our faces and hands often every day. You boys never wash unless you fall into the water or go swimming."

"Neither do you Granzer," Hoo-Hoo retorted.

removing blade server in high
"I know, I know, I am a filthy old man, but times have changed. Nobody washes these days, there are no conveniences. It is sixty years since I have seen a piece of soap.

"You do not know what soap is, and I shall not tell you, for I am telling the story of the Scarlet Death. You know what sickness is. We called it a disease. Very many of the diseases came from what we called germs. Remember that word—germs. A germ is a very small thing. It is like a woodtick, such as you find on the dogs in the spring of the year when they run in the forest. Only the germ is very small. It is so small that you cannot see it—"

Hoo-Hoo began to laugh.

"You're a queer un, Granser, talking about things you can't see. If you can't see 'em, how do you know they are? That's what I want to know. How do you know anything you can't see?"
"A good question, a very good question, Hoo-Hoo. But we did see—some of them. We had what we called microscopes and ultramicroscopes, and we put them to our eyes and looked through them, so that we saw things larger than they really were, and many things we could not see without the microscopes at all. Our best ultramicroscopes could make a germ look forty thousand times larger. A mussel-shell is a thousand fingers like Edwin's. Take forty mussel-shells, and by as many times larger was the germ when we looked at it through a microscope. And after that, we had other ways, by using what we called moving pictures, of making the forty-thousand-times germ many, many thousand times larger still. And thus we saw all these things which our eyes of themselves could not see. Take a grain of sand. Break it into ten pieces. Take one piece and break it into ten. Break one of those pieces into ten, and one of those into ten, and one of those into ten, and one of those into ten, and do it all day, and maybe, by sunset, you will have a piece as small as one of the germs." The boys were openly incredulous. Hare-Lip sniffed and sneered and Hoo-Hoo snickered, until Edwin nudged them to be silent.

server in high
The incubators are built in remote fastnesses, where there is little or no likelihood of their being discovered by other tribes. The result of such a catastrophe would mean no children in the community for another five years. I was later to witness the results of the discovery of an alien incubator.
The community of which the green Martians with whom my lot was cast formed a part was composed of some thirty thousand souls. They roamed an enormous tract of arid and semi-arid land between forty and eighty degrees south latitude, and bounded on the east and west by two large fertile tracts. Their headquarters lay in the southwest corner of this district, near the crossing of two of the so-called Martian canals.

As the incubator had been placed far north of their own territory in a supposedly uninhabited and unfrequented area, we had before us a tremendous journey, concerning which I, of course, knew nothing.

After our return to the dead city I passed several days in comparative idleness. On the day following our return all the warriors had ridden forth early in the morning and had not returned until just before darkness fell. As I later learned, they had been to the subterranean vaults in which the eggs were kept and had transported them to the incubator, which they had then walled up for another five years, and which, in all probability, would not be visited again during that period.

Jobs at Piazza

Piazza is an online Q&A platform that hundreds of thousands of students at all the top universities are using. Professors and students at MIT, Stanford, Princeton, Berkeley and Harvard were the first to adopt it when we launched in 2011.
We're backed by top tier venture firms like Sequoia Capital and Bessemer, we offer flexible work schedules, awesome stuff to work on, competitive salaries, great benefits, and we're based in sunny downtown Palo Alto, CA.
Piazza users spend an average of 2-3 hours a night on the site. Piazza is truly changing the way students learn.
Software Engineer

Software Engineer Craftsperson

Sure, every kid had Legos. But most kids would stop building in order to eat. You were so focused that sometimes adults wondered whether there might be something wrong with you. But there was never anything wrong that a little bit of time alone reassembling something wouldn't cure. Maybe you made your way through model airplanes and electronics kits. We're pretty sure you built a robot.
At school you were probably an amazing student -- when you remembered to do your schoolwork. Sometimes teachers would tell your parents that if you applied yourself, there's nothing you couldn't do. By high school you'd probably figured out that you didn't need to work very hard to get A's, and that left you time to do what you wanted to do.
Was it then that you discovered code?
Whenever it was, your encounter with code changed you. You've always loved building things, but when you coded you felt like you could build a universe. Not that you're an egotist. But in code you found a mode of expression that was less constrained than any craft you'd ever known. And as you did it more, you developed not just technical skill, but an aesthetic sense. When people started talking about "code smells," you knew exactly what they meant.
Now you're looking for your next adventure. Maybe you've come to realize that perfect code is impossible, but beautiful code that serves its users well creates a feeling of supreme delight in you. And you've probably decided that having others coding with you increases the pleasure you take in it. Not hundreds of people, or even dozens. But a few great ones.
Sound like you?
Designer

Designer Artistic Altruist

You've never quite understood that stereotype of the tortured artist. For you, art has always been an outlet, a pleasure, a way you could share something of yourself with the world. And you've always enjoyed sharing because you like the effect your creativity has on others.
You've done a lot of different kinds of art in your life. Maybe it's sewing, or illustration, or music. Maybe you have a thing for cake decoration or woodcarving or chalking sidewalks.
In your heart, you know that there's kind of a show-off quality to designing things. Not that your creations are flashy, but ever since you were a little kid you've loved the feeling of having someone smile and say, "Wow! How did you do that?"
Maybe you don't quite remember when you decided that designing user experiences for computers would be interesting. You probably saw something on a screen that was just wrong and determined you could do better. Or maybe you used something that was just so right that you began to understand how much influence you could have on people's everyday lives.
Now you see the world differently from the way most people do. You see the harmonies that create beauty. You see how simple forms delight people. When you see something that looks wrong, you fix it in your mind, just because.
You're picky about the things you work on these days. There's a lot of flashy stuff, but not a lot of stuff that really makes people's lives better. And that matters to you, because you learned long ago that the ability to create something that helps someone is your deepest inspiration.
Sound like you?
Extroverted Polymath

Biz Ops Extroverted Polymath

When you were in college, you had a hard time choosing a major because everything was so interesting. And, let's not beat around the bush here – you were good at everything, too.
Maybe you made up your own major because nothing on offer was broad enough to contain your rampaging intellectual curiosity. Or maybe you majored in something weird where people said, "What are you going to do with that?" and you answered, "I'm going to live with it."
Maybe you’ve done a few different things in your career. You’ve excelled, but the biggest difference between when you were happy and when you weren’t was your level of passion. And you can’t get passionate about narrow jobs.
Now you're looking for the next big thing. You want to use your left brain and your right brain -- your technical aptitude and your creative side. A great day for you is when you get to solve technical problems in the morning, and then lead a book club discussion in the afternoon.
And you want to share. Because one thing you've discovered in your life is that you like to be out there, in front of people, talking about things that matter to you. You're not just a talker, though. You can tell when people have substance behind them and when they don't, and you're not the wheeler-dealer type. Whatever you do next, you're going to be sincere, knowledgeable, and passionate.
Because that's how you've always been.
Sound like you?

Culture Fit

We're looking for successful people who are looking not only to succeed, but to do something important. We're looking for talented people who believe that education can be more meaningful, more collaborative, and more democratic. In a word: better. We're looking for people who get really excited by products that fit their function beautifully, spread by word of mouth because they're sweet, and engage their users for hours at a time. We're looking for people who love to challenge themselves: to move faster; to code cleaner; to take leadership roles; to make big decisions. We're looking for people with interests. We spend a lot of time together, and we like to have fun. And engage. We are singers, and artists, and historians. We all love learning. We're looking for people who love our product. We're fortunate that thousands of talented people use Piazza every day and want to improve it. It would be great if you felt that way.
Here's what we're not looking for.
We're not looking for big egos, because education and collaboration both demand a measure of humility. We're not looking for people seeking a quick exit, because we believe that doing something truly meaningful takes time. We're not looking for people who want everything figured out for them, because we don't know everything and we believe that the journey is the reward.
If you're still reading at this point, you're probably interested. Here's some more to think about.
We're less interested in what you studied and more interested in what you’ve achieved. Of course, if you're an engineer you need to understand programming deeply, but the best proof of that is working code. If you didn't study computer science but you built something brilliant, show us! We like risk-takers. You'll get equity in Piazza. It's an early-stage business, and you'll have a big opportunity to shape it. We offer the standard benefits, plus a fun Palo Alto location and amazing catered food. We go out together. We think the fun quotient here is pretty high.
Still interested? Apply now! Don't think of these roles as hard and fast. As you can probably guess, we're still flexible enough that there are no job classifications here, and we like people who are happy about that.

Stanford University September-Desember 2010
 
MS & E 239: Pengantar Komputasi Iklan
September-Desember 2011 - Stanford University, California



Isi


Informasi Kursus

Ikhtisar
Iklan komputasi adalah muncul baru ilmiah sub-disiplin, di persimpangan pencarian skala besar dan analisis teks, pencarian informasi, pemodelan statistik, pembelajaran mesin, klasifikasi, optimasi, dan ekonomi mikro. Masalah utama dari iklan komputasi adalah untuk menemukan "paling cocok" antara pengguna tertentu dalam konteks tertentu dan iklan yang cocok. Konteksnya bisa menjadi pengguna memasukkan query di mesin pencari ("pencarian disponsori"), pengguna membaca sebuah halaman web ("match konten" dan "iklan display"), pengguna menonton film pada perangkat portabel, dan sebagainya on. Informasi tentang pengguna dapat bervariasi dari scarily rinci untuk praktis nihil. Jumlah iklan potensial mungkin berada di miliaran. Jadi, tergantung pada definisi "paling cocok" masalah ini mengarah ke berbagai optimasi dan pencarian masalah besar, dengan kendala yang rumit, dan menantang representasi data dan masalah akses. Solusi untuk masalah ini memberikan dasar-dasar ilmiah dan teknis untuk $ 20000000000 industri periklanan online.

Kursus ini bertujuan untuk memberikan pengenalan yang baik untuk masalah algoritmik utama dan solusi dalam iklan komputasi, saat ini diterapkan pada bangunan platform untuk berbagai format iklan online. Pada saat yang sama kami berniat untuk survei singkat ekonomi dan aspek pasar industri, serta beberapa dari batas-batas penelitian. Audiens yang dituju adalah mahasiswa yang tertarik pada aspek praktis dan teoritis dari iklan web.

Daftar tentatif topik meliputi: The landscape secara online periklanan; Marketplace dan ekonomi; representasi data dan optimasi tantangan dalam iklan online, Informasi Pendekatan pengambilan tekstual seleksi iklan, pencarian Sponsored; pertandingan Konteks; Tampilan iklan, perilaku penargetan, Muncul format dan teknologi : mobile, aps, games, dll

Tidak ada prasyarat formal, tetapi beberapa keakraban dengan konsep dasar probabilitas, ekonomi, pembelajaran mesin, dan optimasi diharapkan dan keterampilan web yang baik diperlukan. Kursus ini kemungkinan akan mencakup proyek "kehidupan nyata" di mana siswa akan memiliki anggaran untuk mengiklankan bisnis tertentu dan akan diminta untuk menganalisis dan membenarkan pilihan mereka.

Pengajaran Staf: Cara terbaik untuk menghubungi kami adalah via email di: msande239-aut1112-staff@lists.stanford.edu
Instruktur TA
  • Krishnamurthy Iyer (kriyer AT Stanford)
    Jam kerja: Selasa, 06:00 -7:30 pm, Huang 304

Pertemuan Waktu / Lokasi
Fri 10:00-12: 50 pm, Hewlett Pengajaran Center, Rm 101


Jadwal Kursus

  • 30/9 Ikhtisar dan Pendahuluan
  • 07/10 Marketplace dan Ekonomi
  • Cari Sponsor: 10/14 Tekstual Iklan 1
  • 10/21 Tekstual Iklan 2: Periklanan Kontekstual
  • 10/28 Tampilan Iklan 1
  • 4/11 Tampilan Iklan 2
  • 11/11 Penargetan
  • 11/18 Sistem Rekomendasi
  • 02/12 Mobile, Video dan Format Muncul lainnya
  • 09/12 Presentasi Proyek

Handout Kuliah

Bacaan & Other Links


Tugas

Kebijaksanaan
  • Tugas harus dilakukan secara individual. Ini adalah pelanggaran kode kehormatan untuk berkolaborasi dalam bentuk apapun pada tugas.
  • Menyadari bahwa siswa mungkin menghadapi kondisi yang tidak biasa dan membutuhkan fleksibilitas dalam perjalanan kuartal, setiap siswa akan memiliki total tiga gratis akhir (kalender) hari untuk digunakan sebagai s / ia melihat cocok. Setelah hari-hari akhir habis, pekerjaan rumah berbalik terlambat akan dikenakan sanksi 50% per akhir hari.
  • Semua pekerjaan rumah harus disampaikan dalam slot ditandai "MS & E 239" dalam lemari kayu dekat kamar 064 dan 036 di ruang bawah tanah Huang.
Tugas
Proyek

Proyek Iklan
Uraian tentang proyek iklan di sini .

Proyek Algorithmic
Deskripsi proyek algoritmik adalah di sini (pdf).
Bios pendek

    Andrei Broder adalah Yahoo! Fellow dan Wakil Presiden untuk Komputasi Advertising. Sebelumnya ia adalah seorang IBM Distinguished Engineer dan CTO dari Institute for Search dan Analisis Teks di IBM Research. Dari tahun 1999 sampai 2002 ia adalah Wakil Presiden Riset dan Kepala Ilmuwan di AltaVista Company. Dia lulus Summa cum Laude dari Technion, dan memperoleh gelar M.Sc. dan Ph.D. di Ilmu Komputer di Stanford, di bawah Don Knuth. Minat penelitiannya saat ini berpusat pada iklan komputasi, pencarian web, pasokan informasi konteks-driven, dan algoritma acak.

    Broder adalah co-pemenang penghargaan Kertas Terbaik di WWW6 (untuk karyanya pada duplikat penghapusan halaman web) dan pada WWW9 (untuk karyanya pada pemetaan web). Dia telah menulis lebih dari seratus makalah dan dianugerahi tiga puluh paten. Dia adalah anggota National Academy of Engineering, seorang rekan dari ACM dan IEEE, dan kursi masa lalu dari Komite IEEE Teknis Yayasan Matematika Komputasi.
    Vanja Josifovski adalah Research Scientist Principal dan Lead Kinerja Iklan Group di Yahoo! Research. Dia bergabung dengan Yahoo! Penelitian pada akhir tahun 2005 dan telah menghabiskan sebagian besar waktunya merancang dan generasi berikutnya platform iklan online bangunan Yahoo! 's. Sebagai memimpin teknis, Vanja telah memberikan kontribusi untuk membangun kembali Yahoo! 's tumpukan iklan kontekstual serta platform Sponsored Search Advanced Match. Dia saat ini memimpin tim peneliti dan insinyur dalam mengembangkan generasi Yahoo! 's berikutnya Penargetan platform. Minat penelitiannya mencakup penargetan perilaku, seleksi iklan untuk pencarian disponsori, pertandingan konten dan advertsing grafis, mesin pencari adaptasi untuk pemilihan iklan, data mining dan teknik pencarian informasi untuk meningkatkan kualitas iklan, dan klik dan permintaan analisis data log. Sebelumnya, Vanja adalah Anggota Staf Penelitian di IBM Almaden Research Center mengerjakan beberapa proyek dalam database runtime dan optimasi, database federasi, dan pencarian perusahaan.

    Vanja telah menerbitkan lebih dari 60 peer review publikasi dan telah menulis lebih dari 40 aplikasi paten. Dia telah menjadi anggota dari organisasi dan Program komite WWW, WSDM, SIGIR, SIGKDD, VLDB dan konferensi besar lainnya dalam pencarian informasi, pencarian dan daerah basis. Beliau meraih gelar MSc dari University of Florida dan gelar PhD dari Linkopings University di Swedia.

Program Kuliah Terkait

  • CS 276/286 LING Information Retrieval dan Web Mining ( http://www.stanford.edu/class/cs276/ )
  • MS & E 237: The Social data Revolusi: Data Mining dan Electronic Business (yang akan ditawarkan pada musim semi 2011)

Pengakuan

Kami mengakui syukur dukungan keuangan dari perusahaan-perusahaan berikut terhadap proyek siswa:
  • Google
  • Olahraga Lateral
  • Microsoft
  • Yahoo!


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