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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Presentation Transcript
  • 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