Ad Allocation for Browse Sessions

A user’s session of information need often goes well beyond his search query and first click on the search result page and therefore is characterized by both search and browse activities on the web. Such a session can be effectively represented by the browse graph over the nodes visited by the user in the session. Thus as the user transitions between pages in the browse graph, the effectiveness of ads (clickto-conversion ratio) he sees on these pages could change. On the other hand, the advertisers valuation for a user also depends upon past events in the browse session (a.k.a. an externality), e.g. a shoe company may value a user differently in a browse session if he has not been shown any other shoe ads. In another instance, his valuation may be concave in the number of times the ad is shown to the user in the same session. We note that the advertiser’s valuation is derived from the conversion that a click might lead to. Often, this is not correlated with the click for which the advertiser typically pays. The first contribution of our study is to show that the click to conversion ratio (CtoC) of a user depends on the past events in the session. To this end, we analyze logs of user activity over a period of one month from Microsoft AdCenter Delivery Engine to identify the source, nature and the extent of externality present in the CtoC ratio as a function of past events. Specifically, we address externalities from past exposure of the user to self and competing advertisers. We then propose a new bidding language that allows the advertiser to specify his valuation of a user’s click as a function of these externalities. We show the hardness of computing an optimal ad allocation in this setting and give efficient algorithms under some practical assumptions. Finally, we conduct an extensive empirical analysis on real data to measure effectiveness of our proposed allocation schemes using the Bing AdCenter delivery engine logs. Work done while the author was an intern at Microsof