Введение в рекомендательные системы. 3 case-study без NetFlix.

Ðàçðàáîòêà äàííûõ è ìàøèííîå îáó÷åíèå
Ââåäåíèå â ðåêîìåíäàòåëüíûå ñèñòåìû
Èãíàòîâ Äìèòðèé Èãîðåâè÷
♦
¾Big Data Startup Accelerator Program¿ ðàçâèòèå êîìïåòåíòíîñòåé â ñîçäàíèè
èííîâàöèîííûõ ïðîäóêòîâ è áèçíåñîâ â ñôåðå Áîëüøèõ Äàííûõ
Ñîâìåñòíàÿ èíèöèàòèâà êîðïîðàöèè SAP è innovationStudio MSU FE
♦
ÍÈÓ ÂØÝ
Ôàêóëüòåò êîìïüþòåðíûõ íàóê
Äåïàðòàìåíò àíàëèçà äàííûõ è èñêóññòâåííîãî èíòåëëåêòà
07 ìàðòà 2015
(SAP innovationStudio MSU FE) Big Data Startup Accelerator Program 07.03.2015 1 / 17

Ïëàí çàíÿòèÿ
Íà ýòîé âñòðå÷å:
1 Ââåäåíèå â ðåêîìåíäàòåëüíûå ñèñòåìû (PC). Ïîäõîäû íà îñíîâå
ñõîäñòâà ïî ïîëüçîâàòåëÿìè è íà îñíîâå ñõîäñòâà ïî îáúåêòàì
ðåêîìåíäàöèè. Îöåíêà êà÷åñòâà ðåêîìåíäàòåëüíûõ ñèñòåì (Òî÷íîñòü,
ïîëíîòà, F-ìåðà. Áèìîäàëüíàÿ êðîññâàëèäàöèÿ).
2 Áóëåâà ìàòðè÷íàÿ ôàêòîðèçàöèÿ è ñèíãóëÿðíîå ðàçëîæåíèå (SVD).
Ñðàâíåíèå ðåçóëüòàòîâ íà îñíîâå ìåòîäà áëèæàéøåãî ñîñåäà ïî MAE íà
ïðèìåðå ðåêîìåíäàöèè ôèëüìîâ.
3 Ãèáðèäíûå ðåêîìåíäàöèè íà ïðèìåðå ñåðâèñà îíëàéí-ðàäèîñòàíöèé.
Äîïîëíèòåëüíûå òåìû.
1 Ìîäåëè SVD, SVD++ è time-SVD. Ãðàäèåíòíûé ñïóñê.
2 Êîíòåêñòíûå ðåêîìåíäàöèè.
3 Ñâîáîäíî-ðàñïðîñòðàíÿåìûå áèáëèîòåêè ÐÑ.
4 Ïðèìåð PC äëÿ êðàóäñîðñèíãà íà îñíîâå áèêëàñòåðèçàöèè.
5 ÐÑ íà îñíîâå áèêëàñòåðèçàöèè è àññîöèàòèâíûõ ïðàâèë.
6 Ñèñòåìû ñîâìåñòíîãî ïîëüçîâàíèÿ ðåñóðñàìè. ÐÑ äëÿ ôîëêñîíîìèé.

Îãëàâëåíèå
1 Case-study
Ââåäåíèå. User-based è item-based ïîäõîäû. Îöåíêà êà÷åñòâà.
BMF SVD
Ðåêîìåíäàöèÿ ðàäèîñòàíöèé
2 Ïîëåçíûå ññûëêè
×òî ïî÷èòàòü?
Ñîîáùåñòâî, êîíôåðåíöèè, êóðñû
Ïðèìåðû êîìïàíèé
Èñòî÷íèêè äàííûõ
Ñâîáîäíî-ðàñïðîñòðàíÿåìûå ÐÑ
3 Äîïîëíèòåëüíûå òåìû
Ìîäåëü íà îñíîâå èäåè SVD. Ãðàäèåíòíûé ñïóñê.
Êîíòåêñòíûå ðåêîìåíäàöèè

Case-study 1: Ââåäåíèå
Ââåäåíèå â ðåêîìåíäàòåëüíûå ñèñòåìû (PC). Ïîäõîäû íà îñíîâå
ñõîäñòâà ïî ïîëüçîâàòåëÿìè è íà îñíîâå ñõîäñòâà ïî îáúåêòàì
ðåêîìåíäàöèè. Îöåíêà êà÷åñòâà ðåêîìåíäàòåëüíûõ ñèñòåì (Òî÷íîñòü,
ïîëíîòà, F-ìåðà. Áèìîäàëüíàÿ êðîññâàëèäàöèÿ).
Dmitry I. Ignatov, Jonas Poelmans, Guido Dedene, Stijn Viaene: A New
Cross-Validation Technique to Evaluate Quality of Recommender Systems.
PerMIn 2012: 195-202.
Èãíàòîâ Ä. È., Êàìèíñêàÿ À. Þ., Ìàãèçîâ Ð. À. Ìåòîä ñêîëüçÿùåãî
êîíòðîëÿ äëÿ îöåíêè êà÷åñòâà ðåêîìåíäàòåëüíûõ èíòåðíåò-ñåðâèñîâ //
ÊÈÈ-2010, Ò. 1. Ì. : Ôèçìàòëèò, 2010. Ñ. 175-182.
Ñëàéäû ïî çàïðîñó.

Case-study 2: BMF SVD
Áóëåâà ìàòðè÷íàÿ ôàêòîðèçàöèÿ è ñèíãóëÿðíîå ðàçëîæåíèå (SVD).
Ñðàâíåíèå ðåçóëüòàòîâ íà îñíîâå ìåòîäà áëèæàéøåãî ñîñåäà ïî MAE íà
ïðèìåðå ðåêîìåíäàöèè ôèëüìîâ.
Dmitry I. Ignatov, Elena Nenova, Natalia Konstantinova, Andrey V.
Konstantinov: Boolean Matrix Factorisation for Collaborative Filtering: An
FCA-Based Approach. AIMSA 2014: 47-58
Elena Nenova, Dmitry I. Ignatov, Andrey V. Konstantinov: An FCA-based
Boolean Matrix Factorisation for Collaborative Filtering. FCA Meets IR 2013:
P. 57-73
Ñëàéäû

Case-study 3: Ðåêîìåíäàöèÿ ðàäèîñòàíöèé
Ãèáðèäíûå ðåêîìåíäàöèè íà ïðèìåðå ñåðâèñà îíëàéí-ðàäèîñòàíöèé.
Dmitry I. Ignatov, Sergey I. Nikolenko, Taimuraz Abaev, Natalia
Konstantinova: Online Recommender System for Radio Station Hosting:
Experimental Results Revisited. WI-IAT (2) 2014: 229-236
Dmitry I. Ignatov, Andrey V. Konstantinov, Sergey I. Nikolenko, Jonas
Poelmans, Vasily Zaharchuk: Online Recommender System for Radio Station
Hosting. BIR 2012: 1-12
Ñëàéäû

1 Case-study
BMF SVD

Recommender Systems Handbook, 2011 First comprehensive handbook
dedicated entirely to the eld of recommender systems.
O.Celma. Music Recommendation and Discovery, 2010 . . . this book
presents the state of the art of all the dierent techniques used to recommend
items, focusing on the music domain as the underlying application.
Òîáè Ñåãàðàí. Ïðîãðàììèðóåì êîëëåêòèâíûé ðàçóì. Ãëàâà 2, 2012(2008)
Greg Linden et al. Amazon.com Recommendations: Item-to-Item
Collaborative Filtering. Industry Report. 2003
Y. Koren, Matrix Factorization Techniques for Recommender Systems, IEEE
Computer, 2009
Ââîäíûå ñòàòüè Ñåðãåÿ Íèêîëåíêî íà Habrahabr
. . .

Ñåðèÿ êîíôåðåíöèé RecSys
RecSys Wiki
International Society for Music Information Retrieval (ISMIR)
J.A. Konstan, M.D. Ekstrand. Introduction to Recommender Systems.
Coursera
Ñ.Íèêîëåíêî. Ðåêîìåíäàòåëüíûå ñèñòåìû. Ëåêöèè ïî ìàøèííîìó
îáó÷åíèþ â ÊÔÓ, 2014
Ê.Â. Âîðîíöîâ Êîëëàáîðàòèâíàÿ ôèëüòðàöèÿ. Êóðñ ëåêöèé ïî ìàøèííîå
îáó÷åíèþ
. . .

ÈÌÕÎÍÅÒ ðåêîìåíäàòåëüíûé ñåðâèñ
Retail Rocket ïëàòôîðìà äëÿ ìóëüòèêàíàëüíîé ïåðñîíàëèçàöèè
èíòåðíåò-ìàãàçèíà íà îñíîâå big data, ñîçäàííàÿ ðàçðàáîò÷èêàìè
ðåêîìåíäàòåëüíûõ ñèñòåì Ozon.ru è Wikimart.ru
Surngbird ðåêîìåíäàòåëüíàÿ ñèñòåìà âåá-ñòðàíèö
Gravity Rock Solid Recommendations is a fast-growing company that
helps customers with its state-of-the-art recommendation solutions.
. . .

RecSys Challenge 2015
Group Lens Datasets
ECML/PKDD Discovery Challenge 2011
BibSonomy :: dumps for research purposes
Million Song Dataset Challenge
Job Recommendation Challenge at Kaggle
. . .

MyMediaLite
Easyrec
Python-recsys
LibRec
LensKit
MRec
SVDFeature
. . .

1 Case-study
BMF SVD

Yehuda Koren, Robert M. Bell: Advances in Collaborative Filtering. Recommender
Systems Handbook 2011: 145-186
Áàçîâàÿ ìîäåëü:
rui = µ + bi + bu + qT
i pu
Öåëåâàÿ ôóíêöèÿ:
min
b,q,p
=
(u,i)∈R
(rui − µ − bi − bu − qT
i pu)2
+ λ(b2
i + b2
u + ||qi ||2
+ ||pu||2
), ãäå
R = {(u, i)| îöåíêà rui îïðåäåëåíà}
Ìåòîä ãðàäèåíòíîãî ñïóñêà:
eui = rui − ˆrui
bu ← bu + γ · (eui − λ · bu)
bi ← bi + γ · (eui − λ · bi )
qi ← qi + γ · (eui · pu − λ · qi )
pu ← pu + γ · (eui · qi − λ · pu)

Gediminas Adomavicius, Alexander Tuzhilin: Context-Aware Recommender
Systems. Recommender Systems Handbook 2011: 217-253
Ìíîãîìåðíàÿ ìîäåëü User × Item × Time ðåêîìåíäàòåëüíîãî ïðîñòðàíñòâà
(êîíòåêñòíàÿ èíôîðìàöèÿ âðåìÿ)

G. Adomavicius, A. Tuzhilin, 2011(2005)
Ñïîñîáû âñòðàèâàíèÿ êîíòåêñòà â ïðîñòðàíñòâî ðåêîìåíäàöèé

Âîïðîñû è êîíòàêòû
www.hse.ru/staff/dima
Ñïàñèáî!
dmitrii.ignatov[at]gmail.com
dignatov[at]hse.ru

Введение в рекомендательные системы. 3 case-study без NetFlix.

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Введение в рекомендательные системы. 3 case-study без NetFlix.