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Temporal Externalities of Attention on TV Advertising.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Temporal Externalities of Attention on TV Advertising./
Author:	Bao, Shiqi.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
Description:	33 p.
Notes:	Source: Masters Abstracts International, Volume: 84-01.
Contained By:	Masters Abstracts International84-01.
Subject:	Mass communications. -
ISBN:	9798837520280

Temporal Externalities of Attention on TV Advertising.
Bao, Shiqi.

Temporal Externalities of Attention on TV Advertising. - Ann Arbor : ProQuest Dissertations & Theses, 2022 - 33 p.

Source: Masters Abstracts International, Volume: 84-01.

Thesis (M.S.)--University of California, San Diego, 2022.

This item must not be sold to any third party vendors.

In this project, I investigate temporal externalities of attention on TV Ads. I use a dataset from TVision, which is unique in that it contains both 'traditional' features (viewing histories, user and item characteristics), as well as the attention ratio of viewers on Ads. I find that viewers' attention is persistent over a short period of time. I then apply the knowledge to incorporate temporal features such as previous Ad and previous attention to the traditional latent factor techniques, resulting highly effective advertising recommendations.

ISBN: 9798837520280Subjects--Topical Terms:

3422380
Mass communications.
Subjects--Index Terms:

Commercials

Temporal Externalities of Attention on TV Advertising.
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100 1 $a Bao, Shiqi. $3 3689171
245 1 0 $a Temporal Externalities of Attention on TV Advertising.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2022
300 $a 33 p.
500 $a Source: Masters Abstracts International, Volume: 84-01.
500 $a Advisor: Wilbur, Kenneth C.
502 $a Thesis (M.S.)--University of California, San Diego, 2022.
506 $a This item must not be sold to any third party vendors.
520 $a In this project, I investigate temporal externalities of attention on TV Ads. I use a dataset from TVision, which is unique in that it contains both 'traditional' features (viewing histories, user and item characteristics), as well as the attention ratio of viewers on Ads. I find that viewers' attention is persistent over a short period of time. I then apply the knowledge to incorporate temporal features such as previous Ad and previous attention to the traditional latent factor techniques, resulting highly effective advertising recommendations.
590 $a School code: 0033.
650 4 $a Mass communications. $3 3422380
653 $a Commercials
653 $a TV advertising
653 $a Video advertising
690 $a 0338
690 $a 0708
710 2 0 $a University of California, San Diego. $b Management, Rady School of. $3 3554409
773 0 $t Masters Abstracts International $g 84-01.
790 $a 0033
791 $a M.S.
792 $a 2022
793 $a English