Recsys 2014勉強会資料

1. Recsysຮᙉ఍2014
Gradient
boos4ng
factoriza4on
machines
ᆤᆏṇᚿ
m.tsubosaka@gmail.com

2. Context-­‐aware
recommenda4on
• Tradi4onal
recommenda4on
– 䝴䞊䝄䛸䜰䜲䝔䝮䛾䝺䞊䝔䜱䞁䜾᝟ሗ䛜୚䛘䜙䜜
䛯≧ἣ䛷䝴䞊䝄䛸䜰䜲䝔䝮䛾ᑐ䛻ᑐ䛧䛶䝺䞊䝔䜱
䞁䜾䜢ண 䛩䜛䜘䛖䛺㛵ᩘ䜢Ꮫ⩦䛩䜛
– ௦⾲ⓗ䛺ᡭἲ䛻Matrix
factoriza4on䛜䛒䜛
• Context-­‐aware
recommenda4on
– 䝴䞊䝄䛸䜰䜲䝔䝮䛰䛡䛷䛿䛺䛟䚸䝴䞊䝄䛾䝮䞊䝗
䜔ఇ᪥䛛䛹䛖䛛䛺䛹䝺䝁䝯䞁䝗䛾䛸䛝䛾context䜒
⪃៖䛧䛶䝺䝁䝯䞁䝗䜢⾜䛖

3. Factoriza4on
machine
• Matrix
factoriza4on䛺䛹factoriza4on䛾ᡭἲ䜢୍⯡໬䛧䛯
ᡭἲ
– KDD
Cup
2012䛷SNS䛷䛾䛴䛺䛜䜚ண 䜔ᗈ࿌䛾CTRண 䛺䛹ᐇ
㝿ⓗ䛺䝍䝇䜽䛻ᑐ䛧䛶䜒㧗䛔⢭ᗘ䜢㐩ᡂ䛧䛶䛔䜛
– Rendle,
Social
networks
and
click-­‐through
predic4on
with
factoriza4on
machines,
KDDCUP
2012
yi =
• ᢅ䛖䝕䞊䝍䛿䜹䝔䝂䝸ኚᩘ䜢⪃䛘䚸䝧䜽䝖䝹䛷⾲⌧䛩䜛
– ౛䛘䜀䝴䞊䝄䛜{U1,U2,U3},䜰䜲䝔䝮䛜{I1,I2,I3,I4},䝮䞊䝗䛜
{happy,normal,sad}䛾ሙྜ
– U1䛻I2䜢䝮䞊䝗䛜happy䛺䛸䛝䛻᥎⸀䛩䜛≧ἣ䛷䛿䝧䜽䝖䝹⾲
⌧䛸䛧䛶䛿(1,0,0,
0,1,0,0,
1,0,0)䛾䜘䛖䛻⾲⌧䛩䜛
• ≉ᚩ㔞䛛䜙䛾ண 䛻䛚䛔䛶஧ḟ䛾㡯䜎䛷⪃៖䛩䜛
– ୖ䛾౛䛻⨨䛔䛶䛿U1*I2,
U1*happy,
I2
*
happy䛾୕䛴䛾┦஫
స⏝䛻䛴䛔䛶㔜䜏䜢ィ⟬䛩䜛

4. Factoriza4on
machine
• ஧ḟ䛾┦஫స⏝䜢䛭䛾䜎䜎䛩䜉䛶㔜䜏䜢ồ䜑䜘
䛖䛸䛩䜛䛸䝟䝷䝯䞊䝍䛾ᩘ䛜⭾኱䛻䛺䜛
• 䛣䛾䛯䜑஧ḟ䛾┦஫స⏝㡯w_ij䜢kḟඖ䝧䜽䝖䝹
䛾ෆ✚䛷㏆ఝ䛩䜛
– n
*
n䛾ኚᩘ䛾௦䜟䜚䛻n*k䛾ኚᩘ䜢฼⏝䛩䜛
Rendle,
Factoriza4on
machine,
ICDM
2010䜘䜚

5. Gradient
boos4ng
factoriza4on
machine
• Factoriza4on
machine䛿┦஫స⏝䜢⾲⌧䛩䜛
ୖ䛷䛿ᙉຊ䛺ᡭἲ䛷䛿䛒䜛䛜䚸䛒䜎䜚ண 䛻
ᐤ୚䛧䛺䛔┦஫స⏝䜎䛷䝰䝕䝹䛻ධ䜜䜛䛸䛔
䛖ၥ㢟䛜䛒䜛
• 䛭䛣䛷ᮏㄽᩥ䛷䛿ண 䛻ᐤ୚䛧䛶䛔䛟䜘䛖䛺
㡰␒䛷┦஫స⏝㡯䜢㏣ຍ䛧䛶䛔䛟᪉ἲ䜢ᥦ᱌
⤂௓ㄽᩥ䜘䜚ᘬ⏝

6. ᐇ㦂⤖ᯝ
• ᪤Ꮡ䛾PMF(Probabilis4c
matrix
factoriza4on),
FM(Factoriza4on
machine)䛸ẚ㍑䛧䛶䜘䛔⢭ᗘ䛜
䛷䛶䛔䜛
• 䝍䝇䜽䛿twiWer䛾䜘䛖䛺䝃䞊䝡䝇䛻䛚䛔䛶䚸䝴䞊
䝄䛜ᥦ♧䛥䜜䛯䛴䛺䛜䜚䛾䝺䝁䝯䞁䝗䜢accept䛩
䜛䛛䛹䛖䛛䜢ண
⤂௓ㄽᩥ䜘䜚ᘬ⏝

7. 䝺䝡䝳䞊
• ᪂つᛶ䞉⊂๰ᛶ
:
2
– ᡭἲ⮬య䛿[Chen+
2013,
ICML]䛾MF䛻Gradient
boos4ng
algorithm䜢㐺ᛂ䛧䛯᪉ἲ䜢FM䛻㐺ᛂ䛧
䛯䛰䛡䛷䛒䜎䜚᪂つᛶ䛿䛺䛔
• ᭷ຠᛶ䞉ᐇ⏝ᛶ㻌:
4
– 䜒䛸䜒䛸䛾FM䛜᭷ຠ䛺ᡭἲ䛷䛒䜚
– ᚑ᮶䛾FM䛷䛿䛷䛝䛺䛛䛳䛯᭷ຠ䛺஧ḟ䛾㡯䛰䛡
㑅ᢥ䛷䛝䜛䜘䛖䛻䛺䛳䛯䛸䛔䛖Ⅼ䛷ᐇ⏝ⓗ䛸䛔䛘䜛