2020骞存帹鑽愮郴缁熷伐绋嬪笀鐐间腹鎵嬪唽

Posted 2021-05-01 鐐间腹绗旇

鐐间腹瑕佺粌濂斤紝椤绘湁濂戒腹鏂癸紝浣曞瀵诲ソ鐨勪腹鏂癸紝绛旀洶锛氶《浼氾紒

鏈汉涔冨垵绾х偧涓瑰笀锛岄毦鍏嶄細鏈夌喊婕忥紝杩樿鍚勪綅澶т浆鑾瑙佺瑧锛屽鏈変粈涔堥敊璇紝杩樿澶氬鎸囩偣锛屽鏂楅腑馃锛?/span>

鏈枃鎴戜滑姹囨€讳簡25绡嘇AAI2020涓庢帹鑽愮郴缁熺浉鍏崇殑璁烘枃&鍏跺搴旂殑鎽樿銆?/span>

1: Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation 

• 鎽樿锛歅OI鎺ㄨ崘锛圥oint-of-Interest锛孭OI锛夋槸涓€涓儹闂ㄧ殑鐮旂┒璇鹃锛屽畠浠庡ぇ閲忓€欓€夊満棣嗕腑涓虹敤鎴风敓鎴愭洿鍔犳柟渚跨殑涓€у寲鐨勫缓璁€傚洜涓虹敤鎴风殑绛惧埌璁板綍鍙互鐪嬩綔鏄竴涓緢闀跨殑搴忓垪锛屽熀浜庨€掑綊绁炵粡缃戠粶锛圧NNs锛夌殑鏂规硶鍦ㄨ繖鍧楁樉绀哄嚭浜嗗緢濂界殑閫傜敤鎬с€傜劧鑰岋紝鐜版湁鐨勫熀浜嶳NN鐨勬柟娉曞湪瀵圭敤鎴风煭鏈熷亸濂藉缓妯℃椂锛岃涔堝拷鐣ヤ簡鐢ㄦ埛鐨勯暱鏈熷亸濂斤紝瑕佷箞蹇界暐浜嗘渶杩戣闂殑poi涔嬮棿鐨勫湴鐞嗗叧绯伙紝浣垮緱鎴戜滑鐨勬帹鑽愮粨鏋滃苟涓嶅彲闈?/span>銆傞拡瀵逛笂杩板眬闄愭€э紝鎴戜滑鎻愬嚭浜嗕竴绉嶆柊鐨勯暱鐭湡鍋忓ソ寤烘ā鏂规硶锛圠STPM锛夈€傜壒鍒湴锛岃妯″瀷鐢变竴涓敤浜庨暱鏈熷亸濂藉缓妯＄殑闈炲眬閮ㄧ綉缁滃拰涓€涓敤浜庣煭鏈熷亸濂藉涔犵殑geo-dialted RNN缁勬垚銆傚湪涓や釜瀹為檯鏁版嵁闆嗕笂鐨勫ぇ閲忓疄楠岃〃鏄庯紝鎴戜滑鐨勬ā鍨嬬浉姣旂洰鍓嶆渶濂界殑鏂规硶寰楀埌浜嗘樉钁楃殑鎻愬崌銆?/span>

2.PEIA: Personality and Emotion Integrated Attentive Model for Music Recommendation on Social Media Platforms

• 鎽樿锛氶殢鐫€鏁板瓧闊充箰鏍煎紡鐨勮繀閫熸墿灞曪紝鎺ㄨ崘鐢ㄦ埛鍠滅埍鐨勯煶涔愭槸闈炲父閲嶈鐨勩€傚浜庨煶涔愭帹鑽愶紝鐢ㄦ埛鐨勪釜鎬у拰鎯呮劅浼氬垎鍒互闀挎湡鍜岀煭鏈熺殑鏂瑰紡褰卞搷浠栦滑鐨勯煶涔愬亸濂?/span>锛岃€屼赴瀵岀殑绀句氦濯掍綋鏁版嵁涓鸿繖浜涗俊鎭彁渚涗簡鏈夋晥鐨勫弽棣堛€傛湰鏂囬拡瀵圭ぞ浜ゅ獟浣撳钩鍙颁笂鐨勯煶涔愭帹鑽愰棶棰橈紝鎻愬嚭浜嗕竴绉嶄釜鎬т笌鎯呮劅鐩哥粨鍚堢殑娉ㄦ剰妯″瀷锛圥EIA锛夛紝妯″瀷鍏呭垎鍒╃敤绀句氦濯掍綋鏁版嵁锛屽鐢ㄦ埛鐨勯暱鏈熷枩濂斤紙涓€э級鍜岀煭鏈熷亸濂斤紙鎯呮劅锛夎繘琛岀患鍚堝缓妯°€傚叿浣撳湴锛屽畠鍏呭垎鍒╃敤浜嗕釜鎬у寲鐨勭敤鎴风壒鎬с€佹儏鎰熷鍚戠殑鐢ㄦ埛鐗规€у拰闊充箰鐗规€х殑澶氭柟闈㈠睘鎬с€傚湪鏁村悎鐢ㄦ埛涓€у拰鎯呮劅鐨勬綔鍦ㄨ〃寰佹椂锛岄噰鐢ㄥ眰娆℃敞鎰忔潵鍖哄垎閲嶈鍥犵礌銆傚湪171254涓敤鎴风殑澶у瀷鐪熷疄鏁版嵁闆嗕笂杩涜鐨勫疄楠岃瘉鏄庝簡鎴戜滑鐨凱EIA妯″瀷鐨勬湁鏁堟€э紝鎴戜滑鐨勬ā鍨嬪彲浠ヨ幏寰?.5369鐨?span class="mq-18">NDCG锛屼紭浜庣幇鏈夌殑鏂规硶銆傛垜浠繕杩涜浜嗚缁嗙殑鍙傛暟鍒嗘瀽鍜岀壒寰佽础鐚垎鏋愶紝杩涗竴姝ラ獙璇佷簡鎴戜滑鐨勬柟妗堬紝骞?/span>璇存槑浜嗙敤鎴蜂釜鎬у拰鎯呮劅鍗忓悓寤烘ā鍦ㄩ煶涔愭帹鑽愪腑鐨勬剰涔?/span>銆?/span>

3. A Knowledge-Aware Attentional Reasoning Network for Recommendation

• 鎽樿锛氳繎骞存潵锛屽熀浜庣煡璇嗗浘鐨勬帹鑽愮郴缁熻秺鏉ヨ秺鍙楀埌涓氱晫鍜屽鏈晫鐨勫叧娉ㄣ€傜幇鏈夌殑璁稿鍩轰簬鐭ヨ瘑鐨勬帹鑽愭柟娉曢兘鍙栧緱浜嗘洿濂界殑鏁堟灉锛岄€氬父鏄€氳繃瀵圭煡璇嗗浘涓敤鎴峰拰鍟嗗搧涔嬮棿鐨勮矾寰勮繘琛屾帹鐞嗘潵杩涜鎺ㄨ崘銆傜劧鑰岋紝浠栦滑蹇界暐浜嗕竴鐐癸細鐢ㄦ埛鐨勪釜浜虹偣鍑诲巻鍙插簭鍒楋紝杩欎簺搴忓垪鍙互鏇村ソ鍦板弽鏄犵敤鎴峰湪鎺ㄨ崘鏃堕棿娈靛唴鐨勫亸濂?/span>銆傛湰鏂囦腑锛屾垜浠彁鍑轰簡涓€绉嶅熀浜庣煡璇嗘劅鐭ョ殑娉ㄦ剰鍔涙帹鐞嗙綉缁淜ARN锛孠ARN铻嶅悎浜嗙敤鎴风殑鐐瑰嚮鍘嗗彶搴忓垪鍜岀敤鎴蜂笌鍟嗗搧涔嬮棿鐨勮矾寰勮繛閫氭€?/span>銆傛彁鍑虹殑KARN涓嶄粎寮€鍙戜簡涓€涓熀浜庢敞鎰忕殑RNN锛屼粠鐢ㄦ埛鐐瑰嚮鐨勫巻鍙插簭鍒椾腑鎹曟崏鐢ㄦ埛鐨勫巻鍙插叴瓒ｏ紝鑰屼笖杩樺紑鍙戜簡涓€涓眰娆″寲鐨勬敞鎰忓姏绁炵粡缃戠粶鏉ユ帹鐞嗙敤鎴峰拰鐗╁搧涔嬮棿鐨勮矾寰勶紝浠庤€屾帹鏂嚭鐢ㄦ埛瀵圭墿鍝佺殑娼滃湪鎰忓浘銆傚熀浜庣敤鎴风殑鍘嗗彶鍏磋叮鍜屾綔鍦ㄦ剰鍥撅紝KARN鍙互棰勬祴鐢ㄦ埛瀵瑰€欓€夐」鐨勭偣鍑绘鐜囥€傛垜浠湪Amazon璇勮鏁版嵁闆嗕笂杩涜浜嗗疄楠岋紝瀹為獙缁撴灉璇佹槑浜嗘垜浠彁鍑虹殑KARN妯″瀷鐨勪紭瓒婃€у拰鏈夋晥鎬с€?/span>

4. Enhancing Personalized Trip Recommendation with Attractive Routes

• 鎽樿锛氫釜鎬у寲鍑鸿鎺ㄨ崘璇曞浘涓虹敤鎴锋帹鑽愪竴绯诲垪鍏磋叮鐐癸紙poi锛夈€傜幇鏈夌殑鐮旂┒澶у鍙牴鎹畃oi鏈韩鐨勬祦琛岀▼搴︽潵鎼滅储poi銆備簨瀹炰笂锛?/span>POI涔嬮棿鐨勭嚎璺娓稿涔熼潪甯稿叿鏈夊惛寮曞姏锛屽叾涓竴浜涚嚎璺汉姘斿緢楂樸€傛垜浠皢杩欑璺敱绉颁负鍚稿紩璺敱锛圓R锛夛紝瀹冨彲浠ュ甫鏉ラ澶栫殑鐢ㄦ埛浣撻獙銆傛湰鏂囨垜浠爺绌舵湁鍚稿紩鍔涚殑璺嚎鏉ユ敼鍠勪釜鎬у寲鐨勬梾娓告帹鑽愩€傞拡瀵笰Rs鐨勫彂鐜板拰璇勪及闂锛屾垜浠彁鍑轰簡涓€绉嶅熀浜嶱OIs鍜屽惛寮曡矾寰勭殑涓€у寲鍑鸿鎺ㄨ崘鍣紙TRAR锛夈€傝鏂规硶鍩轰簬娴佽搴﹀拰POI鐨勫熀灏肩郴鏁版潵鍙戞帢鍚稿紩璺嚎锛岀劧鍚庡埄鐢ㄧ被鍒┖闂翠腑鐨勫紩鍔涙ā鍨嬫潵浼拌鍚稿紩璺嚎鐨勮瘎鍒嗗緱鍒嗗拰鍋忓ソ銆傚熀浜庢锛孴RAR寤鸿浣跨敤ARs杩涜涓€娆℃梾琛岋紝浠ユ渶澶ч檺搴﹀湴鎻愰珮鐢ㄦ埛浣撻獙锛屽苟鍦ㄦ椂闂存垚鏈拰鐢ㄦ埛浣撻獙涔嬮棿杩涜鏉冭　銆傚疄楠岀粨鏋滆〃鏄庝簡TRAR鏂规硶涓庡叾浠栨渶鏂版柟娉曠浉姣旂殑浼樿秺鎬с€?/span>

5.Efficient Heterogeneous Collaborative Filtering without Negative Sampling for Recommendation

• 鎽樿锛氭渶杩戝叧浜庢帹鑽愮殑鐮旂┒涓昏闆嗕腑鍦ㄦ帰绱㈡彁楂樻渶鏂扮缁忕綉缁滅殑琛ㄨ揪鑳藉姏锛岃€岄€氬父涓轰簡缃戠粶鐨勯珮鏁堝涔狅紝鎴戜滑甯稿父閲囩敤璐熼噰鏍凤紙NS锛夌瓥鐣ャ€傝繖鏍风殑鏂规硶铏界劧鏈夋晥锛屼絾鏈変袱涓噸瑕佺殑闂娌℃湁寰楀埌寰堝ソ鐨勮€冭檻锛?锛塏S鐨勬尝鍔ㄦ€у緢澶э紝浣垮緱鍩轰簬鎶芥牱鐨勬柟娉曞湪瀹為檯搴旂敤涓緢闅捐幏寰楁渶浼樼殑鎺掑簭鎬ц兘锛?锛夎櫧鐒?span class="mq-42">鍚勭鍚勬牱鐨?/span>鍙嶉锛堜緥濡傦紝鏌ョ湅銆佺偣鍑汇€佺偣鍑荤瓑锛夛紝鍜岃喘涔帮級骞挎硾瀛樺湪浜庤澶氬湪绾跨郴缁燂紝澶у鏁扮幇鏈夌殑鏂规硶鍙埄鐢ㄤ竴绉嶄富瑕佺被鍨嬬殑鐢ㄦ埛鍙嶉锛屽璐拱銆傚湪杩欓」宸ヤ綔涓紝鎴戜滑鎻愬嚭浜嗕竴涓柊鐨勯潪鎶芥牱杩佺Щ瀛︿範瑙ｅ喅鏂规锛屾垜浠皢鍏跺懡鍚嶄负楂樻晥寮傛瀯鍗忓悓杩囨护锛圗HCF锛夌殑Top-N鎺ㄨ崘銆傚畠涓嶄粎鍙互瀵圭粏绮掑害鐨勭敤鎴峰晢鍝佸叧绯昏繘琛屽缓妯★紝鑰屼笖鑳藉浠ヨ緝浣庣殑鏃堕棿澶嶆潅搴︿粠鏁翠釜寮傛瀯鏁版嵁锛堝寘鎷墍鏈夋湭鏍囪鏁版嵁锛変腑楂樻晥鍦板涔犳ā鍨嬪弬鏁般€傚湪涓変釜瀹為檯鏁版嵁闆嗕笂鐨勫ぇ閲忓疄楠岃〃鏄庯紝EHCF鍦ㄤ紶缁燂紙鍗曚竴琛屼负锛夊拰寮傛瀯鍦烘櫙涓兘鏄捐憲浼樹簬鏈€鏂扮殑鎺ㄨ崘鏂规硶銆傛澶栵紝EHCF鍦ㄨ缁冩晥鐜囦笂鏈夋樉钁楁彁楂橈紝浣垮叾鏇撮€傜敤浜庡疄闄呯殑澶у瀷绯荤粺銆傛垜浠殑瀹炵幇宸茬粡鍙戝竷1锛屼互鏂逛究鍩轰簬鍏ㄩ噺鏁版嵁鐨勭缁忔柟娉曠殑杩涗竴姝ュ彂灞曘€?nbsp;

6. An Attentional Recurrent Neural Network for Personalized Next Location Recommendation

• 鎽樿锛氱幇鏈夌殑鍏充簬涓嬩竴涓綅缃帹鑽愮殑鐮旂┒澶у鎻愬嚭瀵圭鍏ュ簭鍒楃殑搴忓垪瑙勫垯鎬ц繘琛屽缓妯★紝浣?/span>鐢变簬澶у鏁颁綅缃殑鍚庣画浣嶇疆灏戜簬5涓紝鍥犳瀛樺湪涓ラ噸鐨勬暟鎹█鐤忛棶棰?/span>銆備负姝わ紝鎴戜滑鎻愬嚭浜嗕竴绉嶅熀浜庢敞鎰忔満鍒剁殑閫掑綊绁炵粡缃戠粶锛圓RNN锛夋潵鑱斿悎寤烘ā鐩镐技浣嶇疆锛堥偦灞咃級鐨勫簭鍒楄寰嬫€у拰杞Щ瑙勫緥鎬?/span>銆傜壒鍒湴锛屾垜浠鍏堣璁′簡涓€涓熀浜庡厓璺緞鐨勯殢鏈烘父璧板湪涓€涓柊鐨勭煡璇嗗浘涓婏紝浠ユ鏉ュ彂鐜板熀浜庡紓璐ㄥ洜绱犵殑浣嶇疆閭诲眳銆傜劧鍚庨噰鐢ㄤ竴涓€掑綊绁炵粡缃戠粶锛岄€氳繃鎹曟崏鎺у埗鐢ㄦ埛绉诲姩鎬х殑鍚勭涓婁笅鏂囷紝瀵瑰簭鍒楄鍒欐€ц繘琛屽缓妯°€傚悓鏃讹紝閫氳繃娉ㄦ剰鏈哄埗鏁村悎琚彂鐜伴偦灞呯殑杞Щ瑙勫緥锛屽苟灏嗗叾涓庡簭鍒楄鍒欐棤缂濆崗浣滐紝褰㈡垚涓€涓粺涓€鐨勯€掑綊妗嗘灦銆傚湪澶氫釜鐪熷疄鏁版嵁闆嗕笂鐨勫疄楠岀粨鏋滆〃鏄庯紝ARNN閮戒紭浜庢渶鏂扮殑鏂规硶銆?/span>

7. Memory Augmented Graph Neural Networks for Sequential Recommendation

• 鎽樿锛氬湪璁稿鎺ㄨ崘绯荤粺涓紝鐢ㄦ埛鍟嗗搧浜や簰鐨勬椂闂撮『搴忓彲浠ユ彮绀虹敤鎴疯涓虹殑鏃堕棿婕斿寲鍜岄『搴忋€傜敤鎴峰皢涓庝箣浜や簰鐨勫晢鍝佸彲鑳藉彇鍐充簬杩囧幓璁块棶鐨勫晢鍝併€傜劧鑰岋紝鐢ㄦ埛鍜屽晢鍝佺殑澶ч噺澧炲姞浣垮緱椤哄簭鎺ㄨ崘绯荤粺浠嶇劧闈复鐫€涓嶅皬鐨勬寫鎴橈細锛?锛夌煭鏈熺敤鎴峰叴瓒ｅ缓妯″洶闅撅紱锛?锛夐暱鏈熺敤鎴峰叴瓒ｉ毦浠ユ崟鎹夛紱锛?锛夊晢鍝佸叡鐜版ā寮忕殑鏈夋晥寤烘ā銆?/span>涓轰簡瑙ｅ喅杩欎簺闂锛屾垜浠彁鍑轰簡涓€绉嶈蹇嗗寮哄浘绁炵粡缃戠粶锛圡A-GNN锛夋潵鍚屾椂鎹曟崏鐢ㄦ埛鐨勯暱鏈熷拰鐭湡鍏磋叮銆傚叿浣撳湴璇达紝鎴戜滑浣跨敤涓€涓浘绁炵粡缃戠粶鍦ㄧ煭鏈熷唴瀵?span class="mq-61">鍟嗗搧涓婁笅鏂囦俊鎭繘琛屽缓妯★紝骞跺埄鐢ㄥ叡浜唴瀛樼綉缁滄潵鎹曟崏鍟嗗搧涔嬮棿鐨勯暱鏈熶緷璧栧叧绯汇€傞櫎浜嗙敤鎴峰叴瓒ｇ殑寤烘ā澶栵紝鎴戜滑浣跨敤鍙岀嚎鎬у嚱鏁版潵鎹曟崏鐩稿叧鍟嗗搧鐨勫叡鐜版ā寮忋€傛垜浠湪浜斾釜鐪熷疄涓栫晫鐨勬暟鎹泦涓婂鎴戜滑鐨勬ā鍨嬭繘琛屼簡骞挎硾鐨勮瘎浼帮紝涓庡嚑绉嶆渶鍏堣繘鐨勬柟娉曡繘琛屼簡姣旇緝锛屽苟浣跨敤浜嗗悇绉嶆€ц兘鎸囨爣銆傚疄楠岀粨鏋滈獙璇佷簡璇ユā鍨嬪Top-K搴忓垪鎺ㄨ崘浠诲姟鐨勬湁鏁堟€с€?/span>

8. Leveraging Title-Abstract Attentive Semantics for Paper Recommendation

• 鎽樿锛氳鏂囨帹鑽愭槸涓虹敤鎴锋彁渚涗釜鎬у寲鐨勬劅鍏磋叮鐨勮鏂囩殑涓€涓爺绌惰棰樸€傜劧鑰岋紝鐜版湁鐨勫ぇ澶氭暟鏂规硶閮芥槸灏嗘爣棰樺拰鎽樿浣滀负瀛︿範璁烘枃琛ㄧ幇褰㈠紡鐨勮緭鍏ワ紝蹇界暐浜嗗畠浠箣闂寸殑璇箟鍏崇郴銆傛湰鏂囧皢鎽樿瑙嗕负涓€涓彞瀛愬簭鍒楋紝骞舵彁鍑轰竴涓袱绾ф敞鎰忕缁忕綉缁滄潵鎹曟崏锛氾紙1锛夊彞瀛愪腑姣忎釜璇嶅湪璇箟涓婃槸鍚︽帴杩戞爣棰樹腑鐨勮瘝鐨勮兘鍔涖€傦紙2锛?鎽樿涓瘡涓彞瀛愮浉瀵逛簬鏍囬鐨勭▼搴︼紝杩欓€氬父鏄鎽樿鏂囨。鐨勪竴涓緢濂界殑姒傛嫭銆傚叿浣撳湴锛屾垜浠彁鍑轰簡涓€涓湁娉ㄦ剰鐨勯暱鐭湡璁板繂锛圠STM锛夌綉缁滄潵瀛︿範鍙ュ瓙鐨勮〃绀猴紝骞跺皢涓€涓棬鎺ч€掑綊鍗曞厓锛圙RU锛夌綉缁滀笌涓€涓蹇嗙綉缁滅浉缁撳悎鏉ュ涔犱氦浜掕鏂囩殑闀挎椂椤哄簭鍙ュ瀷锛屼互渚涚敤鎴峰拰椤圭洰锛堣鏂囷級寤烘ā涔嬬敤銆傛垜浠湪涓や釜鐪熷疄鐨勬暟鎹泦涓婅繘琛屼簡澶ч噺鐨勫疄楠岋紝缁撴灉琛ㄦ槑鎴戜滑鐨勬柟娉曞湪鍑嗙‘鎬ф柟闈紭浜庡叾浠栨渶鍏堣繘鐨勬柟娉曘€?/span>

9. Diversified Interactive Recommendation with Implicit Feedback

• 鎽樿锛?/span>浜や簰寮忔帹鑽愮郴缁?/span>鑳藉瀹炵幇鐢ㄦ埛涓庢帹鑽愮郴缁熶箣闂寸殑浜や簰锛屽凡缁忓紩璧蜂簡瓒婃潵瓒婂鐨勭爺绌跺叴瓒ｃ€備互寰€鐨勬柟娉曚富瑕侀泦涓湪浼樺寲鎺ㄨ崘绮惧害涓娿€傜劧鑰岋紝瀹冧滑寰€寰€蹇界暐浜嗘帹鑽愮粨鏋滅殑澶氭牱鎬э紝浠庤€屽鑷寸敤鎴蜂綋楠屼笉灏藉浜烘剰銆傛湰鏂囬拡瀵圭敤鎴烽殣鍚弽棣堢殑浜や簰寮忔帹鑽愰棶棰橈紝鎻愬嚭浜嗕竴绉嶆柊鐨勫鏍峰寲鎺ㄨ崘妯″瀷锛屽嵆澶氭牱鍖栦笂涓嬫枃缁勫悎Bandit锛圖C2B锛夈€傚叿浣撹€岃█锛孌C2B鍦ㄦ帹鑽愯繃绋嬩腑閲囩敤浜嗗喅瀹氱偣杩囩▼锛屼互鎻愰珮鎺ㄨ崘缁撴灉鐨勫鏍锋€с€備负浜嗗涔犳ā鍨嬪弬鏁帮紝鎻愬嚭浜嗕竴绉嶅熀浜庡彉鍒嗚礉鍙舵柉鎺ㄧ悊鐨凾hompson鎶芥牱绠楁硶銆傛澶栵紝鏈枃杩樻彁渚涗簡鐞嗚閬楁喚鍒嗘瀽鏉ヤ繚璇丏C2B鐨勬€ц兘銆傚湪瀹為檯鏁版嵁闆嗕笂杩涜浜嗗ぇ閲忕殑瀹為獙锛岃瘉鏄庝簡璇ユ柟娉曞湪骞宠　鎺ㄨ崘绮惧害鍜屽鏍锋€ф柟闈㈢殑鏈夋晥鎬с€?/span>

10. Question-driven Purchasing Propensity Analysis for Recommendation

• 鎽樿锛氱數瀛愬晢鍔＄綉绔欑殑鍟嗗鏈熸湜鎺ㄨ崘绯荤粺鑳藉鍚稿紩鏇村鐨勬秷璐癸紝鑰岃繖涓庢秷璐硅€呯殑璐拱鍊惧悜楂樺害鐩稿叧銆傜劧鑰岋紝澶у鏁扮幇鏈夌殑鎺ㄨ崘绯荤粺鍏虫敞鐨勬槸椤惧鐨勬€讳綋鍋忓ソ锛岃€屼笉鏄€氬父鐢遍【瀹㈡彁鍑虹殑闂鎵€鍐冲畾鐨勫嵆鏃堕渶姹傘€備竴涓吀鍨嬬殑鎺ㄨ崘鍦烘櫙鏄細Bob鎯充拱涓€閮ㄥ彲浠ョ帺PUBG娓告垙鐨勬墜鏈恒€備粬瀵瑰崕涓篜20寰堟劅鍏磋叮锛?span class="mq-82">鍦ㄥ畠涓嬮潰闂€淧UBG鑳藉湪杩欓儴鎵嬫満涓婇『鍒╄繍琛屽悧锛熲€溿€傜劧鍚庢垜浠殑绯荤粺鍚戜粬鎺ㄨ崘鏈€绗﹀悎鏉′欢鐨勬墜鏈恒€傜洿瑙変笂锛屼笉鍚岀殑鐢ㄦ埛闂鍙兘浼氬湪鍏朵粬鏈夌被浼奸【铏戠殑鐢ㄦ埛鎾板啓鐨勮瘎璁轰腑寰楀埌瑙ｅ喅銆備负浜嗚В鍐宠繖涓€闂锛屾垜浠彁鍑轰簡涓€绉嶆柊鐨?/span>闂椹卞姩鐨勬敞鎰忕缁忕綉缁滐紙QDANN锛夛紝鏍规嵁鐢ㄦ埛鐢熸垚鐨勮瘎璁烘潵璇勪及鎻愰棶鑰呯殑鍗虫椂闇€姹傚拰浜у搧鐨勫悎鏍兼€э紝骞舵嵁姝よ繘琛屾帹鑽?/span>銆傛病鏈夌洃鐫ｏ紝QDANN鍙互寰堝ソ鍦板埄鐢ㄨ瘎璁烘潵瀹炵幇杩欎竴鐩爣銆傛敞鎰忔満鍒跺彲浠ョ敤鏉ヤ负寤鸿鎻愪緵瑙ｉ噴銆傛垜浠湪娣樺疂缃戠殑涓変釜棰嗗煙瀵筈DANN杩涜浜嗚瘎浼般€傜粨鏋滆〃鏄庤鏂规硶鐨勬湁鏁堟€э紝鏁堟灉閮戒紭浜庡熀绾挎硶銆?/span>

11.Sequential Recommendation with Relation-Aware Kernelized Self-Attention

• 鎽樿锛氭渶杩戠殑鐮旂┒琛ㄦ槑锛屾敞鎰忔満鍒跺彲浠ユ敼鍠勯『搴忔帹鑽愩€傛牴鎹繖涓€鍙戝睍锛屾垜浠彁鍑轰簡鍏崇郴鎰熺煡鏍稿寲鑷垜娉ㄦ剰锛圧KSA锛夛紝瀹冮噰鐢ㄤ簡ransformer鐨勮嚜鎴戞敞鎰忔満鍒讹紝骞跺鍔犱簡涓€涓鐜囨ā鍨嬨€傚彉鍘嬪櫒鐨勫師濮嬭嚜鎴戝叧娉ㄦ槸涓€绉嶆病鏈夊叧绯绘剰璇嗙殑纭畾鎬ф帾鏂姐€傚洜姝わ紝鎴戜滑寮曞叆浜嗕竴涓綔鍦ㄧ┖闂存潵鎻忚堪鑷敞鎰忥紝骞朵笖娼滃湪绌洪棿灏嗘帹鑽愪笂涓嬫枃浠庡叧绯讳腑寤烘ā涓哄缁村亸姝ｆ€佸垎甯冿紝骞舵牴鎹叡鐜般€佸晢鍝佺壒寰佸拰鐢ㄦ埛淇℃伅寤虹珛浜嗘牳鍖栧崗鏂瑰樊鐭╅樀銆?/span>鏈枃閫氳繃澧炲姞鎺ㄨ崘浠诲姟缁嗚妭鐨勬鐜囨ā鍨嬶紝灏員ransformer鐨勮嚜鎴戞敞鎰忓拰椤哄簭鎺ㄨ崘鐩哥粨鍚?/span>銆傛垜浠湪鍩哄噯鏁版嵁闆嗕笂瀵筊KSA杩涜浜嗗疄楠岋紝涓庢渶杩戠殑鍩虹嚎妯″瀷鐩告瘮锛孯KSA鏄剧ず鍑轰簡鏄捐憲鐨勬彁鍗囥€傚悓鏃讹紝RKSA杩樿兘澶熶骇鐢熶竴涓綔鍦ㄧ┖闂存ā鍨嬫潵鍥炵瓟鎺ㄨ崘鐨勫師鍥犮€?br>

12. Incremental Fairness in Two-Sided Market Platforms: On Smoothly Updating Recommendations

• 鎽樿锛氫粖澶╃殑涓昏鐨勫湪绾垮钩鍙板彲浠ヨ璁や负鏄竴涓湁鍟嗗搧鍜屾湇鍔＄殑鐢熶骇鑰呭拰椤惧鐨勫弻杈瑰競鍦恒€傛湁浜烘媴蹇冿紝骞冲彴杩囧垎寮鸿皟瀹㈡埛婊℃剰搴﹀彲鑳戒細褰卞搷鐢熶骇鍟嗙殑绂忕銆備负浜嗚В鍐宠繖浜涢棶棰橈紝鏈€杩戠殑宸ヤ綔涓緢灏戞湁浜鸿瘯鍥句负鐢熶骇鍟?鍗栧)绾冲叆鍏钩鍘熷垯銆傜劧鑰岋紝杩欎簺鐮旂┒蹇界暐浜嗘绫诲钩鍙颁腑鐨勪竴涓噸瑕侀棶棰樷€斺€斾负浜嗘彁楂樺鎴锋晥鐢紝搴曞眰绠楁硶缁忓父鏇存柊锛屼粠鑰屽鑷寸敓浜у晢鐨勬洕鍏夌巼绐佺劧鍙戠敓鍙樺寲銆傚湪鏈枃涓紝鎴戜滑鍏虫敞浜庤繖绉嶉绻佹洿鏂版墍寮曡捣鐨勫叕骞虫€ч棶棰橈紝骞朵富寮犲骞冲彴绠楁硶杩涜澧為噺鏇存柊锛屼互渚跨敓浜у晢鏈夎冻澶熺殑鏃堕棿锛堝湪閫昏緫涓婂拰绮剧涓婏級閫傚簲鍙樺寲銆傜劧鑰岋紝绠€鍗曠殑澧為噺鏇存柊鍙兘瀵瑰鎴蜂笉鍏钩銆傚洜姝わ紝閽堝閮ㄧ讲鍦ㄥ弻杈瑰钩鍙颁笂鐨勫缓璁紝鎴戜滑鍒跺畾浜嗕竴涓熀浜嶪LP鐨勫湪绾夸紭鍖栵紝浠涓楠ゅ閲忛儴缃叉洿鏀癸紝鍦ㄨ繖绉嶆儏鍐典笅锛屾垜浠彲浠ョ‘淇濋」鐩殑骞虫粦杩囨浮锛屽悓鏃朵繚璇佹瘡涓鎴风殑鏁堢敤鏈€灏忋€傚澶氫釜鐪熷疄涓栫晫鏁版嵁闆嗙殑璇勪及琛ㄦ槑锛屾垜浠彁鍑虹殑骞冲彴鏇存柊鏈哄埗瀵瑰弻杈瑰钩鍙颁腑鐨勭敓浜у晢鍜屽鎴烽兘鏄湁鏁堝拰鍏钩鐨勩€?/span>

13.  Attention-guide Walk Model in Heterogeneous Information Network for Multi-style Recommendation Explanation

• 鎽樿锛?/span>鍙В閲婃帹鑽愮殑鐩殑涓嶄粎鍦ㄤ簬鍚戠敤鎴锋彁渚涙帹鑽愮殑鍟嗗搧锛岃€屼笖瑕佽鐢ㄦ埛鐭ラ亾涓轰粈涔堣鎺ㄨ崘杩欎簺鍟嗗搧銆傚湪涓€涓紓鏋勭殑淇℃伅缃戠粶涓紝鐢ㄦ埛鍜屽晢鍝佷箣闂存湁澶鐨勪氦浜掑洜绱犲彲浠ョ敤鏉ヨВ閲婃帹鑽愩€傜劧鑰岋紝杩欎簺鐩镐簰浣滅敤鐨勫洜绱犻€氬父鏄法澶х殑銆佸惈钃勭殑鍜屽槇鏉傜殑銆傜幇鏈夌殑鎺ㄨ崘瑙ｉ噴鏂规硶鍙€冭檻鍗曚竴鐨勮В閲婇鏍硷紝濡傛柟闈㈠眰鎴栬瘎璁哄眰銆備负浜嗚В鍐宠繖浜涢棶棰橈紝鎴戜滑閽堝寮傛瀯淇℃伅缃戠粶涓殑闅跺睘鍏崇郴鍜屼氦浜掑叧绯伙紝鎻愬嚭浜嗕竴绉嶅熀浜庢敞鎰?寮曞-琛岃蛋妯″瀷鐨勫椋庢牸鎺ㄨ崘瑙ｉ噴鐢熸垚妗嗘灦锛圡SRE锛夈€傚湪娉ㄦ剰鏈哄埗鐨勫惎鍙戜笅锛屾垜浠‘瀹氫簡鎺ㄨ崘瑙ｉ噴鐨勯噸瑕佷笂涓嬫枃锛屽苟瀛︿範浜嗗椋庢牸鐢ㄦ埛鍟嗗搧浜や簰鐨勮仈鍚堣〃绀猴紝浠ユ彁楂樻帹鑽愭€ц兘銆傞€氳繃瀵逛笁涓湡瀹炴暟鎹泦鐨勫ぇ閲忓疄楠岋紝楠岃瘉浜嗚妗嗘灦鍦ㄦ帹鑽愭€ц兘鍜屾帹鑽愯В閲婃柟闈㈢殑鏈夋晥鎬с€?nbsp;

14. Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation

• 鎽樿锛氭渶杩戯紝鎺ㄨ崘绯荤粺宸茬粡鑳藉閫氳繃鍒╃敤鐢ㄦ埛鎻愪緵鐨勮瘎璁烘潵杩涜澶уぇ鏀硅繘銆傜幇鏈夋柟娉曢€氬父灏嗙粰瀹氱敤鎴锋垨鍟嗗搧鐨勬墍鏈夎瘎璁哄悎骞跺埌涓€涓暱鏂囨。涓紝鐒跺悗浠ョ浉鍚岀殑鏂瑰紡澶勭悊鐢ㄦ埛鍜屽晢鍝佹枃妗ｃ€傜劧鑰岋紝鍦ㄥ疄璺典腑锛?/span>杩欎袱缁勮瘎璁烘槑鏄句笉鍚岋細鐢ㄦ埛鐨勮瘎璁哄弽鏄犱簡浠栦滑璐拱鐨勫悇绉嶅晢鍝侊紝鍥犳浠栦滑鐨勪富棰橀潪甯镐笉鍚岋紝鑰屼竴涓晢鍝佺殑璇勮鍙笌鍗曚釜鍟嗗搧鏈夊叧锛屽洜姝ゅ湪涓婚涓婃槸鍚岃川鐨勩€?/span>鍦ㄨ繖椤瑰伐浣滀腑锛屾垜浠紑鍙戜簡涓€涓柊鐨勭缁忕綉缁滄ā鍨嬶紝瀹冮€氳繃闈炲绉版敞鎰忔ā鍧楁伆褰撳湴瑙ｉ噴浜嗚繖涓€閲嶈宸紓銆傜敤鎴锋ā鍧楀涔犲彧鍏虫敞涓庣洰鏍?span class="mq-116">鍟嗗搧鐩稿叧鐨勯偅浜涗俊鍙凤紝鑰?span class="mq-117">鍟嗗搧妯″潡瀛︿範鎻愬彇鍏充簬鍟嗗搧灞炴€х殑鏈€鏄捐憲鐨勫唴瀹广€傛垜浠殑澶氬眰娆¤寖寮忚В閲婁簡杩欐牱涓€涓簨瀹烇細涓嶆槸鎵€鏈夌殑璇勮閮藉悓鏍锋湁鐢紝涔熶笉鏄瘡涓瘎璁轰腑鐨勬墍鏈夊彞瀛愰兘鍚屾牱鐩稿叧銆傚湪鍚勭瀹為檯鏁版嵁闆嗕笂鐨勫ぇ閲忓疄楠岀粨鏋滆瘉鏄庝簡璇ユ柟娉曠殑鏈夋晥鎬с€?/span>

15. Table2Analysis: Modeling and Recommendation of Common Analysis Patterns for Multi-Dimensional Data

• 鎽樿锛氱粰瀹氫竴涓缁存暟鎹〃锛屼汉绫讳細鍒涘缓浠€涔堟牱鐨勬柟娉曟潵浠庝腑鎻愬彇淇℃伅锛熶粠绉戝鎺㈢储鍒板晢涓氭櫤鑳斤紝杩欐槸鐭ヨ瘑鍙戠幇鍜屽喅绛栬嚜鍔ㄥ寲闇€瑕佽В鍐崇殑鍏抽敭闂銆傚湪鏈枃涓紝鎴戜滑鎻愬嚭浜員able2Analysis锛屼粠澶ч噺(Table,analysis)瀵逛腑瀛︿範甯哥敤鐨勫垎鏋愭ā寮忥紝骞舵帹鑽愬浠讳綍涓€涓互鍓嶄粠鏈杩囩殑琛ㄨ繘琛屽垎鏋愩€傚缁存暟鎹綔涓鸿緭鍏ュ鐜版湁鐨勬ā鍨嬩綋绯荤粨鏋勫拰璁粌鎶€鏈彁鍑轰簡鎸戞垬銆?/span>Table2Analysis鍩轰簬鍚彂寮忔悳绱㈢殑娣卞害Q瀛︿範锛岃繘琛岃〃鍒板簭鍒楃殑鐢熸垚锛屾瘡涓簭鍒楃紪鐮佷竴涓垎鏋?/span>銆傚湪鎴戜滑瀵规暟鎹€忚琛ㄦ帹鑽愪换鍔＄殑澶ц妯＄數瀛愯〃鏍艰鏂欏簱鐨勮瘎浼颁腑锛孴able2Analysis鐨勫墠5鍚嶅彫鍥炵巼涓?.78锛屽墠1鍚嶇殑鍙洖鐜囦负0.65

16.Symmetric Metric Learning with Adaptive Margin for Recommendation

• 鎽樿锛氬熀浜庡害閲忓涔犵殑鏂规硶鍦ㄦ帹鑽愮郴缁熶腑寮曡捣浜嗗箍娉涚殑鍏磋叮銆傜洰鍓嶇殑鏂规硶鍦ㄥ害閲忕┖闂翠腑閲囧彇浠ョ敤鎴蜂负涓績鐨勬柟寮忥紝淇濊瘉鐢ㄦ埛涓庤礋鍟嗗搧涔嬮棿鐨勮窛绂绘瘮褰撳墠鐢ㄦ埛涓庢鍟嗗搧涔嬮棿鐨勮窛绂诲ぇ涓€瀹氱殑璺濈銆傝€屽拷瑙嗕簡姝ｅ悜鍟嗗搧涓庤礋鍚戝晢鍝佷箣闂寸殑鍏崇郴銆傚洜姝わ紝杩欎袱涓晢鍝佸彲鑳藉畾浣嶅緱寰堣繎锛屼粠鑰屽鑷翠笉姝ｇ‘鐨勭粨鏋溿€傚悓鏃讹紝涓嶅悓鐨勭敤鎴烽€氬父鏈変笉鍚岀殑鍋忓ソ锛岃繖浜涙柟娉曟墍閲囩敤鐨勫浐瀹?span class="mq-133">margin涓嶈兘閫傚簲鍚勭鐢ㄦ埛鐨勫亸宸紝浠庤€岄檷浣庝簡鎬ц兘銆傞拡瀵硅繖涓や釜闂锛屾垜浠彁鍑轰簡涓€绉嶆柊鐨勫熀浜庤嚜閫傚簲margin鐨勫绉板害閲忓涔犵畻娉曘€傞櫎浜嗗綋鍓嶇殑浠ョ敤鎴蜂负涓績鐨勫害閲忎箣澶栵紝瀹冨绉板湴寮曞叆浜嗕竴涓Н鏋佺殑浠ュ晢鍝佷负涓績鐨勫害閲忥紝璇ュ害閲忎繚鎸佷簡浠庢鍚?span class="mq-134">鍟嗗搧鍒扮敤鎴风殑璺濈锛屽悓鏃跺皢璐熷悜鍟嗗搧浠庢鍟嗗悜鍝?/span>鎺ㄧ銆傛澶栵紝鍔ㄦ€佽嚜閫傚簲margin缁忚繃鑹ソ璁粌锛屼互鍑忚交鍋忓樊鐨勫奖鍝嶃€傚湪涓変釜鍏紑鎺ㄨ崘鏁版嵁闆嗕笂鐨勫疄楠岀粨鏋滆〃鏄庯紝涓庡嚑绉嶆渶鍏堣繘鐨勬柟娉曠浉姣旓紝SML鍏锋湁绔炰簤鎬х殑鎬ц兘銆?/span>

17. Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation

• 鎽樿:Hashing鏄В鍐冲ぇ瑙勬ā鎺ㄨ崘闂鐨勪竴绉嶆湁鏁堟妧鏈紝瀹冨湪璁＄畻鐢ㄦ埛瀵瑰晢鍝佺殑鍋忓ソ鏃跺叿鏈夎緝楂樼殑璁＄畻鍜屽瓨鍌ㄦ晥鐜囥€傜劧鑰岋紝鐜版湁鐨勫熀浜嶩ashing鐨勬帹鑽愭柟娉曡繕瀛樺湪涓や釜閲嶈闂锛?锛夊叾鎺ㄨ崘杩囩▼涓昏渚濊禆浜庣敤鎴峰晢鍝佺殑浜や簰浣滅敤鍜屽崟涓€鐨勭壒瀹氬唴瀹圭壒寰併€傚綋浜や簰鍘嗗彶鎴栧唴瀹圭壒鎬т笉鍙敤鏃讹紙鍐峰惎鍔ㄩ棶棰橈級锛屽畠浠殑鎬ц兘灏嗕弗閲嶆伓鍖栥€?锛?鐜版湁鐨勬柟娉曢噰鐢ㄦ澗寮涗紭鍖栧涔犳暎鍒楃爜锛屾垨閲囩敤绂绘暎鍧愭爣涓嬮檷娉曠洿鎺ユ眰瑙ｄ簩杩涘埗鏁ｅ垪鐮侊紝杩欎細閫犳垚寰堝ぇ鐨勯噺鍖栨崯澶辨垨娑堣€楀ぇ閲忕殑璁＄畻鏃堕棿銆傞拡瀵硅繖浜涢棶棰橈紝鏈枃鎻愬嚭浜嗕竴绉嶅揩閫熷喎鍚姩鎺ㄨ崘鏂规硶锛屽嵆澶氱壒寰佺鏁ｅ崗鍚岃繃婊わ紙MFDCF锛夈€傚叿浣撳湴锛屾垜浠璁′簡涓€涓綆绉╄嚜鍔犳潈澶氱壒寰佽瀺鍚堟ā鍧楋紝鍏呭垎鍒╃敤浜岃€呯殑浜掕ˉ鎬э紝鑷€傚簲鍦板皢澶氫釜鍐呭鐗瑰緛鎶曞奖鍒颁簩鍊间俊鎭搱甯岀爜涓€傛澶栵紝鎴戜滑寮€鍙戜簡涓€涓揩閫熺殑绂绘暎浼樺寲绠楁硶鏉ョ洿鎺ヨ绠椾簩杩涘埗鍝堝笇鐮?/span>锛屾搷浣滅畝鍗曘€傚湪涓や釜鍏紑鎺ㄨ崘鏁版嵁闆嗕笂鐨勫疄楠岃〃鏄庯紝MFDCF鍦ㄥ悇涓柟闈㈤兘浼樹簬鐜版湁鐨勬妧鏈€?/span>

18.Towards Comprehensive Recommender Systems: Time-Aware Unified Recommendations Based on Listwise Ranking of Implicit Cross-Network Data

• 鎽樿锛歸eb搴旂敤绋嬪簭涓赴瀵岀殑淇℃伅浣垮緱鎺ㄨ崘瀵逛簬鐢ㄦ埛鍜屽簲鐢ㄧ▼搴忛兘鑷冲叧閲嶈銆傚敖绠＄幇鏈夌殑鎺ㄨ崘绯荤粺鏄湁鏁堢殑锛屼絾鏄垜浠彂鐜颁袱涓富瑕佺殑闄愬埗闄嶄綆浜嗗畠浠殑鏁翠綋鎬ц兘锛?/span>锛?锛変笉鑳介€氳繃鑰冭檻鐢ㄦ埛鍋忓ソ鐨勫姩鎬佺壒鎬ф潵涓烘柊鐢ㄦ埛鍜岀幇鏈夌敤鎴锋彁渚涘強鏃剁殑鎺ㄨ崘锛?/span>浠ュ強锛?锛?/span>鍦ㄤ娇鐢ㄩ殣寮忓弽棣堟椂娌℃湁瀵规帓鍚嶄换鍔¤繘琛屽厖鍒嗕紭鍖栥€?/span>鍥犳锛屾垜浠彁鍑轰簡涓€绉嶆柊鐨勫熀浜庢繁搴﹀涔犵殑缁熶竴璺ㄧ綉缁滆В鍐虫柟妗堬紝浠ョ紦瑙ｅ喎鍚姩鍜屾暟鎹█鐤忛棶棰橈紝骞朵负鏂扮敤鎴峰拰鐜版湁鐢ㄦ埛鎻愪緵鍙婃椂鐨勫缓璁€傛澶栵紝鎴戜滑灏嗛殣寮忓弽棣堜笅鐨勬帓搴忛棶棰樿涓轰竴涓垎绫讳换鍔★紝骞堕拡瀵归殣寮忔暟鎹彁鍑轰簡涓€涓€氱敤鐨勪釜鎬у寲鍒楄〃浼樺寲鍑嗗垯锛屼互鏈夋晥鍦板鍟嗗搧鍒楄〃杩涜鎺掑簭銆傛垜浠娇鐢═witter杈呭姪淇℃伅鏉ヨ鏄庢垜浠殑璺ㄧ綉缁滄ā鍨嬶紝浠ヤ究鍦╕ouTube鐩爣缃戠粶涓婅繘琛屾帹鑽愩€備笌澶氫釜鏃堕棿鎰熺煡鍩虹嚎鍜岃法缃戠粶鍩虹嚎鐨勫ぇ閲忔瘮杈冭〃鏄庯紝鎴戜滑鎵€鎻愬嚭鐨勬柟妗堝湪鍑嗙‘鎬с€佹柊棰栨€у拰澶氭牱鎬ф柟闈㈠叿鏈変紭瓒婃€с€傛澶栵紝鍦ㄦ祦琛岀殑MovieLens鏁版嵁闆嗕笂杩涜鐨勫疄楠岃〃鏄庯紝鎵€鎻愬嚭鐨勫垪琛ㄦ帓搴忔柟娉曚紭浜庣幇鏈夌殑鎺掑簭鎶€鏈€?/span>

19. Fast Adaptively Weighted Matrix Factorization for Recommendation with Implicit Feedback

• 鎽樿锛氱敱浜庣己涔忓彲闈犵殑鍙瀵熷埌鐨勮礋闈㈡暟鎹紝鏉ヨ嚜鍐呴殣鍙嶉鐨勬帹鑽愭槸涓€椤规瀬鍏锋寫鎴樻€х殑浠诲姟銆備竴绉嶆祦琛岃€屾湁鏁堢殑闅愬紡鎺ㄨ崘鏂规硶鏄皢鏈瀵熷埌鐨勬暟鎹涓鸿礋闈紝浣嗚繖浼氶檷浣庝粬浠殑缃俊搴︺€傞殣寮忔帹鑽愭ā鍨嬬殑涓や釜鍏抽敭闂鏄浣曞垎閰嶇疆淇℃潈閲嶅拰澶勭悊澶ч噺鏈娴嬪埌鐨勬暟鎹€傜劧鑰岋紝鐜版湁鐨勬柟娉曡涔堣拷姹傚揩閫熷涔狅紝鎵嬪伐鍒嗛厤绠€鍗曠殑缃俊鏉冨€硷紝缂轰箯鐏垫椿鎬э紝鍦ㄨ瘎浠风敤鎴峰亸濂芥椂鍙兘浜х敓缁忛獙鍋忓樊锛涜涔堣嚜閫傚簲鍦版帹鏂釜鎬у寲鐨勭疆淇℃潈鍊硷紝浣嗘晥鐜囪緝浣庛€?/span>涓轰簡瀹炵幇鑷€傚簲鏉冨€煎垎閰嶅拰鏈夋晥鐨勬ā鍨嬪涔狅紝鎴戜滑鎻愬嚭浜嗕竴绉嶅熀浜庡彉鍒嗚嚜鍔ㄧ紪鐮佸櫒鐨勫揩閫熻嚜閫傚簲鍔犳潈鐭╅樀鍒嗚В锛團AWMF锛?/span>銆傚埄鐢ㄥ弬鏁板寲绁炵粡缃戠粶锛堝嚱鏁帮級鑷€傚簲鍦板垎閰嶄釜鎬у寲鏁版嵁鐨勭疆淇℃潈锛屽苟鏍规嵁鏁版嵁鎺ㄦ柇鍑虹綉缁溿€傛澶栵紝涓轰簡鏀寔FAWMF蹇€熺ǔ瀹氱殑瀛︿範锛屾垜浠紑鍙戜簡涓€绉嶆柊鐨勫熀浜庢壒澶勭悊鐨勫涔犵畻娉昮BGD锛岃绠楁硶瀵规墍鏈夊弽棣堟暟鎹繘琛岃缁冿紝浣嗗叾澶嶆潅搴︿笌瑙傛祴鏁版嵁鐨勬暟閲忔垚绾挎€у叧绯汇€傚湪瀹為檯鏁版嵁闆嗕笂鐨勫ぇ閲忓疄楠岃〃鏄庝簡鎵€鎻愬嚭鐨凢AWMF鍙婂叾瀛︿範绠楁硶fBGD鐨勪紭瓒婃€с€?/span>

20.Towards Hands-free Visual Dialog Interactive Recommendation

• 鎽樿锛氶殢鐫€澶氭ā寮忎氦浜掓帹鑽愭妧鏈殑鍙戝睍锛岀敤鎴峰彲浠ラ€氳繃瀵瑰晢鍝佸浘鍍忕殑鑷劧璇█鍙嶉鏉ヨ〃杈捐嚜宸辩殑鍋忓ソ锛屼粠鑰屾壘鍒版墍闇€鐨勫晢鍝併€傜劧鑰岋紝鐜版湁鐨勭郴缁熻涔堝彧妫€绱竴涓潯鐩紝瑕佷箞瑕佹眰鐢ㄦ埛鍦ㄦ瘡娆＄敤鎴蜂氦浜掍腑浠庢帹鑽愬垪琛ㄤ腑鎸囧畾锛堜緥濡傦紝閫氳繃鐐瑰嚮鎴栬Е鎽革級璇勮鐨勬潯鐩?/span>銆傚洜姝わ紝鐢ㄦ埛涓嶈兘鍏嶆彁锛屽缓璁彲鑳戒笉鍒囧疄闄呫€傛垜浠彁鍑轰竴涓厤鎻愮殑瑙嗚瀵硅瘽鎺ㄨ崘绯荤粺锛屼互浜掑姩鏂瑰紡鎺ㄨ崘鍟嗗搧娓呭崟銆傛瘡娆★紝绯荤粺閮戒細鏄剧ず鍏锋湁瑙嗚澶栬鐨?span class="mq-168">鍟嗗搧鍒楄〃銆傜敤鎴峰彲浠ョ敤鑷劧璇█瀵瑰垪琛ㄨ繘琛屾敞閲婏紝浠ユ弿杩颁粬浠繘涓€姝ユ兂瑕佺殑鍔熻兘銆備娇鐢ㄨ繖浜涘妯″紡鏁版嵁锛岀郴缁熷皢閫夋嫨鍙︿竴涓鎺ㄨ崘鐨?span class="mq-169">鍟?/span>鍒楄〃銆備负浜嗕粠杩欎簺澶氭ā鏁版嵁涓簡瑙ｇ敤鎴风殑鍋忓ソ锛屾垜浠紑鍙戜簡绁炵粡缃戠粶妯″瀷鏉ヨ瘑鍒垪琛ㄤ腑鐨勬弿杩伴」骞惰繘涓€姝ラ娴嬫湡鏈涚殑灞炴€с€備负浜嗗疄鐜伴珮鏁堢殑浜や簰寮忔帹鑽愶紝鎴戜滑鍒╃敤鎺ㄦ柇鍑虹殑鐢ㄦ埛鍋忓ソ锛岃繘涓€姝ュ紑鍙戜簡涓€绉嶆柊鐨刡andit绠楁硶銆傚叿浣撳湴璇达紝涓轰簡閬垮厤绯荤粺杩囧害鎺㈢储锛屽埄鐢ㄦ湡鏈涘睘鎬ф潵鍑忓皯鎺㈢储绌洪棿銆傛洿閲嶈鐨勬槸锛屼负浜嗗湪杩欑鍏嶆彁鐜涓嬪疄鐜版牱鏈湁鏁堝涔狅紝鎴戜滑浠庣敤鎴风敤鑷劧璇█琛ㄨ揪鐨勭浉瀵瑰亸濂戒腑鑾峰彇棰濆鏍锋湰锛屽苟璁捐浜哹andit瀛︿範涓殑鎴愬logistic鎹熷け銆傛垜浠殑bandit妯″瀷鐢辫嚜鐒惰瑷€鍙嶉鍜屼紶缁焞ogistic鎹熷け鍏卞悓淇銆傚疄楠岀粨鏋滆〃鏄庯紝缁忚繃鍑犳鐢ㄦ埛浜や簰鍚庯紝鏈郴缁熸壘鍒版墍闇€鍟嗗搧鐨勬鐜囨槸浼犵粺浜や簰寮忔帹鑽愯€呯殑3鍊嶅乏鍙炽€?/span>

21.Contextual-Bandit Based Personalized Recommendation with Time-Varying User Interests

• 鎽樿锛氱爺绌朵簡涓€涓珮搴﹂潪骞崇ǔ鐜涓嬬殑涓婁笅鏂嘼andit闂锛岃闂鐢变簬鐢ㄦ埛鍏磋叮鐨勬椂鍙樻€ц€屾櫘閬嶅瓨鍦ㄤ簬鍚勭鎺ㄨ崘绯荤粺涓€傝€冭檻浜嗕袱涓叿鏈変笉鐩镐氦鍜屾贩鍚堟敹鐩婄殑妯″瀷鏉ユ弿杩扮敤鎴峰涓嶅悓鍟?/span>鐨勫亸濂介殢鏃堕棿鐨勫彉鍖栬€屼笉鍚岀殑鐜拌薄銆傚湪涓嶇浉浜ょ殑鏀粯妯″瀷涓紝鐜╁崟鍙墜鑷傜殑濂栧姳鐢变竴涓壒瀹氫簬鎵嬭噦鐨勫亸濂藉悜閲忓喅瀹氾紝璇ュ亸濂藉悜閲忔槸鍒嗘骞崇ǔ鐨勶紝鍦ㄤ笉鍚岀殑鎵嬭噦涓婃湁寮傛鍜屼笉鍚岀殑鍙樺寲銆傛彁鍑轰簡涓€绉嶆湁鏁堢殑閫傚簲鎶ラ叕绐佸彉鐨勫涔犵畻娉曪紝骞惰繘琛屼簡鐞嗚鍚庢倲鍒嗘瀽锛岃瘉鏄庝簡鍦ㄦ椂闂撮暱搴?T$鑼冨洿鍐呭疄鐜颁簡鍚庢倲鐨勬绾挎€ф爣搴︺€傝绠楁硶杩涗竴姝ユ墿灞曞埌涓€涓叿鏈夋贩鍚堟敹鐩婄殑鏇翠竴鑸殑鐜涓紝鐜╀竴鍙墜鑷傜殑濂栧姳鐢辨墜鑷傜壒瀹氱殑鍋忓ソ鍚戦噺鍜屾墍鏈夋墜鑷傚叡浜殑鑱斿悎绯绘暟鍚戦噺鍐冲畾銆傚湪瀹為檯鏁版嵁闆嗕笂杩涜浜嗗疄璇佸疄楠岋紝浠ラ獙璇佹墍鎻愬嚭鐨勫涔犵畻娉曠浉瀵逛簬鍩虹嚎瀛︿範绠楁硶鍦ㄤ袱绉嶆儏鍐典笅鐨勪紭瓒婃€с€?/span>

22.Stochastically Robust Personalized Ranking for LSH Recommendation Retrieval

• 鎽樿锛氬眬閮ㄦ晱鎰熸暎鍒楋紙LSH锛夊凡鎴愪负鏈€甯哥敤鐨勮繎浼艰繎閭绘悳绱㈡妧鏈箣涓€锛屼互閬垮厤鎵弿鎵€鏈夋暟鎹偣鐨勯珮鏄傛垚鏈€傚浜庢帹鑽愮郴缁燂紝LSH閫氳繃灏嗙敤鎴峰拰鍟嗗搧鍚戦噺缂栫爜鎴愪簩杩涘埗鏁ｅ垪鐮佹潵瀹炵幇楂樻晥鐨勬帹鑽愭绱?/span>锛?/span>闄嶄綆浜嗗鎵€鏈?span class="mq-184">鍟嗗搧鍚戦噺杩涜绌峰敖鎬ф鏌ヤ互纭畾top-k椤圭殑寮€閿€銆傜劧鑰岋紝浼犵粺鐨勭煩闃靛垎瑙ｆā鍨嬩細鍥犻殢鏈烘娊鍙朙SH鏁ｅ垪鍑芥暟鑰屽鑷存€ц兘閫€鍖栵紝鐩存帴褰卞搷鎺ㄨ崘鐨勬渶缁堣川閲忋€傛湰鏂囨彁鍑轰簡涓€涓悕涓好竨rmodel鐨勬鏋讹紝璇ユ鏋跺湪瀛︿範瀹炲€肩敤鎴峰拰鍟嗗搧娼滃湪鍚戦噺鏃讹紝鑰冭檻LSH鏁ｅ垪鍑芥暟鐨勯殢鏈烘€э紝鏈€缁堟彁楂楲SH绱㈠紩鍚庣殑鎺ㄨ崘绮惧害銆傚湪鍏紑鏁版嵁闆嗕笂鐨勫疄楠岃〃鏄庯紝璇ユ鏋朵笉浠呮湁鏁堝湴瀛︿範浜嗙敤鎴风殑棰勬祴鍋忓ソ锛岃€屼笖涓嶭SH闅忔満鎬у叿鏈夊緢楂樼殑鍏煎鎬э紝涓庢渶鍏堣繘鐨勫熀绾跨浉姣旓紝浜х敓浜嗘洿濂界殑LSH鍚庣储寮曟€ц兘銆?/span>

23.Deep Time-Stream Framework for Click-Through Rate Prediction by Tracking Interest Evolution.

• 鎽樿锛氬湪瑙嗛鎺ㄨ崘绛夊伐涓氬簲鐢ㄤ腑锛岀偣鍑荤巼棰勬祴鏄竴椤归噸瑕佺殑浠诲姟銆傛渶杩戯紝鏈変汉鎻愬嚭浜嗘繁搴﹀涔犳ā鍨嬫潵瀛︿範鐢ㄦ埛鏁翠綋鍏磋叮鐨勮〃绀猴紝鑰屽拷鐣ヤ簡鍏磋叮鍙兘闅忔椂闂村姩鎬佸彉鍖栫殑浜嬪疄銆傛垜浠涓烘湁蹇呰鑰冭檻CTR妯″瀷涓殑杩炵画鏃堕棿淇℃伅锛屼互渚夸粠涓板瘜鐨勫巻鍙茶涓轰腑璺熻釜鐢ㄦ埛鐨勫叴瓒ｈ秼鍔裤€傛湰鏂囨彁鍑轰簡涓€绉嶆柊鐨勬繁搴︽椂闂存祦妗嗘灦锛圖TS锛夛紝瀹冮€氳繃甯稿井鍒嗘柟绋嬶紙ODE锛夊紩鍏ユ椂闂翠俊鎭€侱TS浣跨敤绁炵粡缃戠粶涓嶆柇鍦版ā鎷熷叴瓒ｇ殑婕斿彉锛屼粠鑰岃兘澶熻В鍐虫牴鎹敤鎴风殑鍘嗗彶琛屼负鍔ㄦ€佽〃绀虹敤鎴峰叴瓒ｇ殑鎸戞垬銆傛澶栵紝鎴戜滑鐨勬鏋跺彲浠ユ棤缂濆湴搴旂敤鍒颁换浣曠幇鏈夌殑娣卞害CTR妯″瀷锛岄€氳繃鍒╃敤棰濆鐨勬椂闂存祦妯″潡锛岃€屼笉浼氬鍘熷鐨凜TR妯″瀷杩涜浠讳綍鏇存敼銆傚湪鍏叡鏁版嵁闆嗕笂杩涜浜嗘暟鍗佷嚎涓疄闄呮暟鎹泦鐨勫疄楠岋紝楠岃瘉浜嗘墍鎻愬嚭鏂规硶鐨勬湁鏁堟€с€?/span>

24.Improved Algorithms for Conservative Exploration in Bandits.

• 鎽樿锛氬湪璁稿棰嗗煙锛屽鏁板瓧钀ラ攢銆佸尰鐤椾繚鍋ャ€侀噾铻嶅拰鏈哄櫒浜烘妧鏈紝閫氬父鍦ㄧ敓浜т腑杩愯缁忚繃鑹ソ娴嬭瘯鍜屽彲闈犵殑鍩虹嚎绛栫暐锛堜緥濡傦紝鎺ㄨ崘绯荤粺锛夈€傜劧鑰岋紝鍩哄噯鏀跨瓥寰€寰€鏄浼樼殑銆傚湪杩欑鎯呭喌涓嬶紝闇€瑕侀儴缃蹭笌绯荤粺浜や簰鐨勫湪绾垮涔犵畻娉曪紙渚嬪锛屽鑷俠andit绠楁硶锛夛紝浠ヤ究鍦ㄥ涔犺繃绋嬩腑鐨勬€ц兘鍑犱箮涓嶄細姣斿熀绾挎湰韬殑鎬ц兘宸殑绾︽潫涓嬪涔犳洿濂?鏈€浼樼殑绛栫暐銆?/span>鏈枃鐮旂┒浜嗕笂涓嬫枃绾挎€andit鐜涓嬬殑淇濆畧瀛︿範闂锛屾彁鍑轰簡涓€绉嶆柊鐨勭畻娉曪細淇濆畧绾︽潫LinUCB锛圕LUCB2锛夈€傛垜浠緱鍒颁簡CLUCB2鐨勯仐鎲剧晫锛屽畠涓庡凡鏈夌殑缁撴灉鐩稿尮閰嶏紝骞堕€氳繃缁忛獙璇佹槑瀹冨湪璁稿缁煎悎闂鍜屽疄闄呴棶棰樹腑浼樹簬鏈€鍏堣繘鐨勪繚瀹坆andit绠楁硶銆傛渶鍚庯紝鎴戜滑鑰冭檻浜嗕竴涓洿鐜板疄鐨勭害鏉燂紝鍗虫€ц兘鍙湪棰勫厛瀹氫箟鐨勬鏌ョ偣锛堣€屼笉鏄湪姣忎釜姝ラ锛夎繘琛岄獙璇侊紝骞惰鏄庤繖绉嶆斁鏉剧殑绾︽潫鏄浣曞CLUCB2鐨勯仐鎲惧拰缁忛獙鎬ц兘浜х敓鏈夊埄褰卞搷鐨勩€?/span>

25.Deep Match to Rank Model for Personalized Click-Through Rate Prediction.

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