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Temporal Externalities of Attention on TV Advertising.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Temporal Externalities of Attention on TV Advertising./
作者:	Bao, Shiqi.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2022,
面頁冊數:	33 p.
附註:	Source: Masters Abstracts International, Volume: 84-01.
Contained By:	Masters Abstracts International84-01.
標題:	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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500 $a Source: Masters Abstracts International, Volume: 84-01.
500 $a Advisor: Wilbur, Kenneth C.
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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.
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