In this assignment, we investigate temporal externalities of attention on TV Ads. We 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. We find that viewers' attention is persistent over a short period of time. We 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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