推荐技术随谈

1 .വគದ๞ᵋ᧨ ᘃၴ 

2 .ፓ୯ • ᳯ᷌ਧԎ • ՗ᓌ‫ܔ‬ጱ୏ত-CF • Ꭵᴣ‫ړ‬ᥴ(MF) • Beyond MF • വគཛྷࣳԏक़ 

3 .ᳯ᷌ਧԎғᎥᴣऴ꧌ ᠗ᜋғአಁጱ಑‫ړ‬ ᅍᜋғአಁ๚ၨᥦᬦ task: ᕳᅍᜋᶼၥӞӻ‫ړ‬ ꧊҅դᤒአಁጱࡅཻᑕଶ 

4 . ՗ᓌ‫ܔ‬ጱ୏ত • ‫ݶܐ‬ᬦᄁ • UserCF޾ItemCF੒ྲ • ತ‫ڊ‬ፘ֒ጱᇔߝ/አಁ UserCF ItemCF • ᇔߝฎ‫ݻڜ‬ᰁᓒፘ֒ ӻ௔۸ ؇ᗭ֛௔ ӻ௔۸অ • አಁฎᤈ‫ݻ‬ᰁᓒፘ֒ ‫ۖސٯ‬ ᥴ٬itemጱ‫ۖސٯ‬ ᥴ٬አಁጱ‫ۖސٯ‬ • ೲᆙፘ֒ଶ‫ے‬๦ ಭᐥ ࣋ว ෛ᳼ ኪ୽ᒵ ‫ي‬᪁ಘ઀ Y N ਫሿஉፘ֒҅ᕮຎ‫ܩ‬ӧॡፘ֒҅why 

5 .ᳯ᷌: ‫ݶܐ‬ᬦᄁԅՋԍᚆड़work • च๜‫ᦡ؃‬ғইຎӞӻՈAࣁӞӻᳯ᷌Ӥ޾‫ݚ‬ӞӻՈB೮ፘ‫ݶ‬ᥡᅩ҅ ᮎԍ੒ԭ‫ݚ‬क़Ӟӻᳯ᷌҅ྲ᩸ᵋ๢ᭌೠጱӞӻ᪠Ոከ҅Aๅํ‫ݢ‬ᚆ ‫ݶ‬B೮ፘ‫ݶ‬ᥡᅩ̶ • ᤈ‫ݻ‬ᰁ᮱‫ړ‬ፘ‫ٌ҅ݶ‬՜᮱‫ړ‬Ԟ‫ݢ‬ᚆፘ‫ݶ‬ • ᕚ௔դහጱ๞᧍ғ֗ᑉ • ֗ᑉ->ٞ֟҅ٞ֟->ܴᖽ҅य़Ꭵᴣ‫ݢ‬զᤒᐏ౮ੜᎥᴣ 

6 .Ꭵᴣ‫ړ‬ᥴ = ᕪḵഖ० ᕮ຅ᷚᴾҁྋ‫ڞ‬۸ᶱ҂ 

7 .‫ݶܐ‬ᬦᄁ޾Ꭵᴣ‫ړ‬ᥴྲ᫾ ྲ᫾ᶱ CF LFM ᇙ஄ᖌଶ ጯӡ ጯ ᇙ஄ᤒᐏ ᶋ਍ԟ ਍ԟ ੴ᮱/ෆ֛௔ ੴ᮱ᇙ஄ഔ഍অ ෆ֛௔ 

8 .Ꭵᴣ‫ړ‬ᥴғALS޾W2V ALS W2V ྋ໏๜ ‫ق‬ੴ ෸ଧ ੴ᮱ ᨮ໏๜ ‫ق‬ੴ ‫ݻ؁‬ṛ᷇ ໏๜੒ཛྷࣳጱපຎํด៪ጱ୽ߥ 

9 .Ꭵᴣ‫ړ‬ᥴғᄍ۸Ӟ • 

10 .Ꭵᴣ‫ړ‬ᥴғᄍ۸ԫ • Item‫׆‬ᣟ‫ف‬side information • w2v՗๢࢏਍ԟ᥯ଶ๶፡҅ฎग़‫ړ‬ᔄ ཛྷࣳ҅෫ᶋ੪ฎ‫ړ‬ᔄᥢཛྷྲ᫾य़ᘒ ૪ • ಩d3‫ݒ‬౮ग़ӻᇙ஄҅ྺ෫ᵙଶ • ᇙ஄‫ڊ‬ԧitemid҅keyword҅ category҅authorᒵᕹᕹ‫ݢ‬զᕑ‫ٌف‬ Ӿ 

11 .Ꭵᴣ‫ړ‬ᥴғᄍ۸ӣ • ԅuser޾item᮷ൊ‫ف‬ᇙ஄ᤒᐏጱᗸᚺ 

12 .MFጱ‫س‬୞--FM Wij • FMጱ‫܎ݸ‬᮱‫ړ‬੪ฎग़ӻMF • ᇙ஄ԅӷᔄғᤒᬡU޾ᤒᬡI • Uٖ᮱ᇙ஄Ի԰޾Iٖ᮱ᇙ஄Ի԰੒ཛྷࣳපຎ୽ߥᬱӧইU޾IጱԻ԰ • ‫ض‬dot-product҅ٚ‫ = ޾ے‬ᇙ஄‫ݻ‬ᰁ‫҅޾ےض‬ٚdot-product 

13 .ॠӥྎ‫ڊۑ‬Ӟਹ 

14 .ဌํਠᗦጱཛྷࣳ—ᵞ౮ • W&D • FM • Itemᕆ‫ݩמڦ‬ • አಁኮ‫؟‬ • Score=G(W&D,FM,item,አಁኮ‫)؟‬ • GBDTྲ᫾ᭇ‫ݳ‬denseᇙ஄ԏᳵጱԻ԰ 

15 .Ӟӻ೰ຽӧड़—ᅩ‫޾ڋ‬᫨۸ • 

16 .ཛྷࣳጱ‫ل‬ଘ௔ • ഭଧཛྷࣳฎӞӻᨸ৕ཛྷࣳ • ᤒሿғ੒ԭ઀ሿӧ᪃ጱitem҅உᵙᶼ֌‫ٵ‬Ꮯ • ᥴ٬ොໜғ • UCBᓒဩ • ӻ௔۸ጱUCBᓒဩ 

17 .൤ᔱ޾വគ ൤ᔱ വគ ᤒᬡᳪད ṛ ֗ ᳩੲ‫ݎړ‬ অ ૧ ᤒᬡེ᷇ ṛ ֗ 

18 .ᴫ୯ • ୟ‫ג‬຋ ̽വគᔮᕹ‫ࢥࢧݟ‬ཛྷࣳԏғ‫ق‬ᚆጱFMཛྷࣳ̾ • Google ̽Deep Neural Networks for YouTube Recommendations̾ • ᴨ᯾૬૬ ̽୩۸਍ԟࣁᴨ᯾ጱದ๞ᄍᬰ޾ӱ‫ڠۓ‬ෛ̾ • IRGAN • Lessons from the Netflix Prize Challenge