Thursday 3:30–5:00 PM
Chair: Ramakrishnan Srikant

Competing for Users’ Attention: On the Interplay between Organic and Sponsored Search Results

Cristian Danescu-Niculescu-Mizil, Andrei Broder, Evgeniy Gabrilovich, Vanja Josifovski, Bo Pang

Queries on major Web search engines produce complex result pages, primarily composed of two types of information: organic results, that is, short descriptions and links to relevant Web pages, and sponsored search results, the small textual advertisements often displayed above or to the right of the organic results. Strategies for optimizing each type of result in isolation and the consequent user reaction have been extensively studied; however, the interplay between these two complementary sources of information has been ignored, a situation we aim to change. Our findings indicate that their perceived relative usefulness (as evidenced by user clicks) depends on the nature of the query. Specifically, we found that for navigational queries there is a clear competition between ads and organic results, while for non-navigational queries this competition turns into synergy. We also investigate the relationship between the perceived usefulness of the ads and their textual similarity to the organic results, and propose a model that formalizes this relationship. To this end, we introduce the notion of responsive ads, which directly address the user’s information need, and incidental ads, which are only tangentially related to that need. Our findings support the hypothesis that in the case of navigational queries, which are usually fully satisfied by the top organic result, incidental ads are perceived as more valuable than responsive ads, which are likely to be duplicative. On the other hand, in the case of non-navigational queries, incidental ads are perceived as less beneficial, possibly because they diverge too far from the actual user need. We hope that our findings and further research in this area will allow search engines to tune ad selection for an increased synergy between organic and sponsored results, leading to both higher user satisfaction and better monetization.

Mining Advertiser-specific User Behavior Using Adfactors

Nikolay Archak, Vahab Mirrokni, S Muthukrishnan

Consider an online ad campaign run by an advertiser. The ad serving companies that handle such campaigns record users’ behavior that leads to impressions of campaign ads, as well as users’ responses to such impressions. This is summarized and reported to the advertisers to help them evaluate the performance of their campaigns and make better budget allocation decisions. The most popular reporting statistics are the click-through rate and the conversion rate. While these are indicative of the effectiveness of an ad campaign, advertisers often seek to understand more sophisticated long-term effects of their ads on brand awareness and user behavior that leads to “conversion”, thus creating need for reporting measures that can capture duration, frequency and pathways to user conversion. In this paper, we propose an alternative data mining framework for analyzing user-level advertising data. In the aggregation step, we compress individual user histories into a graph structure, called adgraph, representing local correlations between ad events. For the reporting step, we introduce several scoring rules, called adfactors (AF), that can capture global role of ads and ad paths in the adgraph, in particular, the structural correlation between an ad impression and user conversion. We present scalable local algorithms for computing the adfactors; all algorithms were implemented using MapReduce programming model and Pregel framework. Using an anonymous dataset of user-level data for sponsored search campaigns of eight different advertisers, we evaluate our framework with different adgraphs and adfactors in terms of their statistical fit to the data, and show its value for mining and visualizing long-term patterns in the advertising data.

The Anatomy of an Ad: Structured Indexing and Retrieval for Sponsored Search

Michael Bendersky, Evgeniy Gabrilovich, Vanja Josifovski, Donald Metzler

Sponsored search ads help connect users with advertisers by matching user queries to relevant ads. Ads can be retrieved either by exact match, when their bid term is identical to the query, or by advanced match, which indexes ads as documents and is similar to standard information retrieval (IR). Recently, there has been a great deal of research into developing advanced match ranking algorithms. However, no previous research has addressed the ad indexing problem. Unlike most traditional search problems, the ad corpus is defined hierarchically in terms of advertiser accounts, campaigns, and ad groups, which further consist of creatives and bid terms. This hierarchical structure makes indexing highly non-trivial, as naively indexing all possible “displayable” ads leads to a prohibitively large and ineffective index. We show that ad retrieval using such an index is not only slow, but its precision is suboptimal as well. We investigate various strategies for compact, hierarchy-aware indexing of sponsored search ads through adaptation of standard IR indexing techniques. We also propose a new ad retrieval method that yields more relevant ads by exploiting the structured nature of the ad corpus. Experiments carried out over a large ad test collection from a commercial search engine show that our proposed methods are highly effective and efficient compared to more standard indexing and retrieval approaches.


Back to full list of papers