User intent based online advertising
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User Intent Based Online Advertising. ADKDD 2010. Motivation. From the publisher/end user point of view: Most traditional approach for online advertising are content based, and those methods mainly deliver ads according to domains

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  • From the publisher/end user point of view:

    • Most traditional approach for online advertising are content based, and those methods mainly deliver ads according to domains

    • However, different task might be performed in the same domain, the following figure shows a user who want to repair his/her car, but the ads delivered have little to do with that intent


  • From the advertiser point of view:

    • In most current sponsored search system, advertisers bid on terms to have their ads delivered, however, words used in queries are numerous and hard to choose

Our approach
Our Approach

  • Definition of Intent: A certain task that a user might perform in a given domain

  • We propose to sell intent under certain domain instead of sell term in general

    • We will show that deliver ads considering intent would help improve CTR

    • We will give an algorithm that can predict user intent in a given domain

    • We will show that using our algorithm to deliver ads can help improve CTR

Related work
Related work

  • Online advertising

    • The brief history and some basic algorithms is introduced in [2], [3]

    • However, the most straightforward idea is hard to use directly due to the diversity of vocabulary in queries and queries are tend to be short

    • As a result, three kind of approach were proposed to enhance the performance

      • Design algorithms to select ads [6], [20] as well as leverage more information from single query [4],[7],[8]; however, little of those methods can be directly used in classifying user intent

      • BT, leverage user past behavior to help deliver ads [10], [11]; however, in those approaches, only bag-of-words are considered, thus it is more likely to deliver ads according to domain

Related work1
Related work

  • User Intent Classification

    • First proposed in [16] and define user intent as Navigational, Informational and Transactional. [17],[18] provided some algorithm to classify user intent. However, this helps little on advertising.

    • So, in order to help deliver ads, [9] proposed Online Commercial Intention(OCI), however, this can only tell us whether to deliver ads, but not what ads to deliver.

Related work2
Related work

  • Random walk


  • As point out by [19], it is hard to assigning labels manually even for the navigational, informational and transactional intent, so it is hard to attain the training data in a single domain.

  • Moreover, we have hundreds of domains to deal with

  • As a result, we can not manually label training data for classification. So we propose to use random walk to address this challenge.

    • The basic assumption of our proposal is that most pages can only accomplish a single task, thus user who visit similar pages may have similar intent

Details of our algorithm
Details of our algorithm

  • First, for a given domain D and intent I, we use the following algorithm to extract training data:

  • How we perform Random Walk:

1. Let

2. Let

3. Replace all entities in , and get a pattern list PAT

4. Let

5. Build a bipartite graph V=(PAT,URL), if there are k clicks between pattern pat and URL u, the weight for edge (pat,u) is k, note if query q can be represented by pattern pat, and q clicked u, we say pat click u

6. Manually select

7. Starting from , we use random walk to expend the seeds patterns, and get the final training data

Random walk
Random Walk

  • We define transition probability as:

    , where represents the weight for edge (j,k)

  • We define the probability that after t step, we can reach point i as , and we have:

  • And we define

  • Using the above formula, we can calculate the possibility for any given t and i; and we select pattern pat that have , θ is a manually defined parameter

Random walk1
Random Walk

  • However, not all URLs meet our basic assumption, so in order to get a cleaner training data set, we have to filter out some pages.

    • Filter 1: If a URL is clicked by too many different queries and the entropy is large enough, like the home page of yahoo or MSN, we filter out those URLs, in detail:

      we filter out URL u, having entropy(u) > 0

    • Filter 2: If a URL is clicked almost equally by different kind of seed pattern, we filter them out.

      • For example, if u is clicked 100 times from seed pattern of buy, and clicked 150 times from seed pattern of repair, we filter it out

Prediction model
Prediction Model

  • Using training data extracted in previous step, we get a set of patterns for each intent in a certain domain

  • Then we use those patterns to train a multi-class classification model

  • Finally, we use the classification model to classify query intent, and deliver ads according to the intent in the given domain