ÉϺ£ÖÎÀíÂÛ̳µÚ549ÆÚ
±êÌ⣺Online Advertisement Allocation Under Customer Choices and Algorithmic Fairness£¨¿Í»§Ñ¡ÔñºÍË㷨ƽÕýϵÄÔÚÏ߸æ°×·ÖÅ䣩
Ñݽ²ÈË£ºÖ £»¶½ÌÊÚ £¬ÉϺ£½»Í¨´óѧ°²Ì©¾¼ÃÓëÖÎÀíѧԺ
Ö÷³ÖÈË£ºÍôͦËɽÌÊÚ £¬CA88ÖÎÀíѧԺ
¹¦·ò£º2025Äê6ÔÂ23ÈÕ£¨ÖÜÒ»£© £¬ÉÏÎç9:00-12:00
µØÖ·£ºCA88У±¾²¿¶«Çø1ºÅÂ¥ÖÎÀíѧԺ420»áÒéÊÒ
Ö÷°ìµ¥Ôª£ºCA88ÖÎÀíѧԺ¡¢CA88ÖÎÀíѧԺÇàÀÏ´óʦÁªÒê»á
Ñݽ²È˼ò½é£º
Ö £»¶½ÌÊÚÏÖÈÎÉϺ£½»Í¨´óѧÖÎÀí¿ÆѧϵϵÖ÷ÈΡ¢½ÌÊÚ¡¢²©Ê¿Éúµ¼Ê¦ £¬È«¹ú³ÛÃûר¼Ò £¬Ö÷³Ö¹ú¶È×Ô¿ÆÇàÄê»ù½ðAÀࣨԹú¶È׿ԽÇàÄê»ù½ðÏîÄ¿£©¡¢¹ú¶ÈÌìÈ»¿Æѧ»ù½ð³ÁµãÏîÄ¿µÈ¡£ÖØÒª×êÑÐÐËÖÂÔ̺¬¹©¸øÁ´ÖÎÀíºÍÓÅ»¯¡¢Êý¾Ý·ÖÎöÓëóÒ×¾ö²ßµÈ £¬Óжàƪ×êÑгɾͰ䷢ÓÚOperations Research, Manufacturing & Service Operations ManagementµÈѧÊõÆÚ¿¯ÉÏ¡£
Ñݽ²ÄÚÈݼò½é£º
Advertising is a crucial revenue source for e-commerce platforms and a vital online marketing tool for their sellers. In this paper, we explore dynamic ad allocation with limited slots upon each customer arrival for an e-commerce platform, where customers follow a choice model when clicking the ads. Motivated by the recent advocacy for the algorithmic fairness of online ad delivery, we adjust the value from advertising by a general fairness metric evaluated with the click-throughs of different ads and customer types. The original online ad-allocation problem is intractable, so we propose a novel stochastic program framework (called two-stage target-debt, TTD) that first decides the click-through targets then devises an ad-allocation policy to satisfy these targets in the second stage. We design a debt-weighted offer-set (DWO) algorithm and demonstrate that, as long as the problem size scales to infinity, this algorithm is (asymptotically) optimal under the optimal first-stage click-through target. Compared to the Fluid heuristic and its re-solving variants, our approach has better scalability and can deplete the ad budgets more smoothly throughout the horizon, which is highly desirable for the online advertising business in practice.
Ó½Ó¿í´óʦÉú²ÎÓ룡
