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12/18 regular meeting paper 12/18 regular meeting paper Presentation Transcript

  • Mining Social Network for Targeted Advertising
    • Wan-Shiou Yang Jia-Ben Dia
    • Hung-Chi Cheng Hsing-Tzu Lin
    • Proceedings of the 39th Annual Hawaii Internetional
    • Conference on System Sciences(HICSS’06) Track 6
    • Repoter : Che-Min Liao
  • Outline
    • Introduction
    • The problem
    • Mining cohesive subgroups from social network
    • Targeted advertising based on cohesive subgroups
    • Evaluation
    • Related work
    • Conclusion
  • Introduction
    • The effectiveness of targeting a small portion of customers for advertising has long been recognized by businesses.
    • It is desirable to help customers wade through the information to find the product/service they want.
    • Understanding the needs of current and potential customers.
  • Introduction
    • Traditional approach to targeted advertising (manually) :
      • Analyze a historical database of previous transactions.
      • Analyze the features associated with the customers.
      • Possibly with the help of some statistical tools.
    • Now, many recommender systems, whose basic idea is to advertise products according to users’ preferences :
      • Explicitly stated by the users.
      • Implicitly inferred from previous records.
  • Introduction
    • The first type of recommendation technique was called the content-based approach :
      • Characterizes recommendable products by a set of content features.
      • Represents users’ interests by a similar feature set.
      • Selects target customers whose interests have a high degree of similarity to the product content profile.
    • Limitations :
      • It is inappropriate for products whose content is not electronically available.
      • It is inappropriate to advertise products to a new user and the possibility of providing surprising advertisements is low.
  • Introduction
    • Another type of recommendation technique was called the collaborative approach : (social-based)
      • Looks for relevance among users by observing their ratings assigned to products in a training set of limited size.
      • The nearest-neighbor act as recommendation partners for the target user.
      • Advertises products to the target user that appear in the profiles of these recommendation partners but not in the target user’s own profile.
    • Limitations :
      • Failing to advertise newly introduced products that have yet to be rated by users.
      • Failing to advertise products to a new user who has yet to provide its rating data.
  • Introduction
    • In order to remedy these problems, we propose a data mining framework that utilizes the concept of social network for the targeted advertising of products.
    • To apply data mining techniques to gathered social interaction data to cluster customers into a set of cohesive subgroups,
    • Based on the set of cohesive subgroups, we infer the probabilities of customer’s liking a product category from transaction records.
  • The problem
    • A social network is a set of individuals connected through socially meaningful relationships, such as friendship or co-working.
    • Social network theory traditionally views social relationships in terms of nodes and links.
    • Nodes are the individual actors within the networks, and links are the relationships between the actors.
  • The problem
    • The strength of a tie between two actors is much greater if two actors in question have another mutual acquaintance.
    • Actors with strong ties usually have some sort of common ground and thus often form a subgroup.
    • Because of the mutual influences, customers with strong ties are apt to exhibit similar purchase behavior.
  • The problem
    • In this research, we investigate the problem of targeted advertisement in an environment with the following features :
    • A database that contains customer’s connectedness is available.
  • The problem
    • A database that contains past customers’ transaction records is available
      • To facilitate data mining task, we group transaction of the same customer resulting in aggregated transactions.
  • The problem
    • Out first goal is to identify a set of subgroups in which actors have relatively strong ties.
    • The second goal is to infer the probabilities of customer’s liking a product category from transaction records.
    • Therefore, given a product p, the problem of targeted advertising p is to identify a set of N potential customers who are most likely to purchase p based on the discovered subgroups.
  • Mining cohesive subgroups from social network
    • We define a cohesion measure as Definition 3 to evaluate the cohesion extent of a set of actors :
  • Mining cohesive subgroups from social network
    • In Figure 1, Cohesion(a 1 ,a 2 ) is equal to 1 since actors a 1 and a 2 have all neighbors in common while Cohesion(a 2 ,a 3 ) is 4/7 since only parts of their neighbors are common.
  • Mining cohesive subgroups from social network
    • Our goal is to identify a set of subgroups, in which actors have certain extent of cohesion.
  • Mining cohesive subgroups from social network
    • The cohesion measure decreases as the size of the subgroup increases. (i.e., Cohesion(a 1 ,a 2 ) = 1, Cohesion(a 1 ,a 2 ,a 3 ) = 4/7)
    • Similar to the support measure of the association rule discovery problem, the cohesion measure exhibits the downward closure property.
  • Mining cohesive subgroups from social network
    • We adopted an iterative procedure similar to that in the Apriori algorithm.
  • Mining cohesive subgroups from social network
    • In our algorithm, the nodes in a subgroup are kept sorted in their lexicographic order.
    • In order to improve the efficiency of the algorithm, two cohesive subgroups of size k-1 are joined if they differ only in one node.
    • The detailed algorithm for generating candidate subgroups is sketched as Figure 3.
  • Mining cohesive subgroups from social network
  • Mining cohesive subgroups from social network
    • Continuing with the previous example and assume the cohesion threshold is 0.5.
    • Calling the candidate generation function with L 1 gives 21 candidate subgroups in C 2 .
    • The procedure is repeated until the cohesive subgroups {a 1 ,a 2 ,a 3 ,a 7 } and {a 3 ,a 4 ,a 5 ,a 6 } are generated in L 4 .
  • Targeted advertising based on cohesive subgroups
    • We then estimate the liking of each product category on each cohesive subgroup.
    • To calculate the likings for cohesive subgroup g j , we simple count the number of times each product category c i has appeared in the aggregated category transactions of the customers who belongs to g j .
    • We then normalize the liking Like(c i ,g j ) of each product category c i on cohesive group g j so that
  • Targeted advertising based on cohesive subgroups
    • Suppose product p belong to category c i .The problem of selecting the set of targeted customers can be formulated as the following linear program :
  • Targeted advertising based on cohesive subgroups
    • Obviously, the above linear programming problem is a special case of the fractional 0-1 knapsack problem.
  • Targeted advertising based on cohesive subgroups
    • For a targeted new user, advertisements will be selected according to the likings of the cohesive subgroups that the targeted user belongs to.
    • Given a newly introduced product item, it will be advertised according to the category it belongs.
  • Evaluation
    • We tested the effectiveness of our approach to the targeted advertising of library books in our university.
    • We collected the from/to email logs of all faculties between April 2005 and June 2005.
    • The library uses an online public access catalog (OPAC) system from INNOVATIVE.
    • The circulation data were also collected between April 2005 and June 2005.
  • Evaluation
    • We adopted the first two levels of the Chinese Classification Scheme for the category of Chinese books.
    • We used five-way cross validation for conducting our experiments.
    • We randomly partitioned the set of books that appear in the transaction into five subsets of equal size.
  • Evaluation
  • Evaluation
  • Evaluation
    • We then compare the following four targeted advertising approaches :
      • Group-based.
      • Single-based, which differs from group-based approach in regarding each user as a subgroup.
      • Neighbor-based, which differs from group-based approach in regarding each user and his direct neighbors as a subgroup.
      • Random, which randomly chooses users.
    • The hit rate for p is the ratio of the number of user in target set who had been issued with p to the total number of users who had been issued with p.
  • Evaluation
  • Related work
    • Referral Web system
    • The SNACK system
    • Viral marketing
    • Overtargeting problem
    • Multi-impressions and overselling.
    • Advertisement placement problem.
  • Conclusion
    • The results from applying this approach to library-circulation data have demonstrated the effectiveness of the proposed targeted advertising algorithms.
    • It is essential to apply the proposed approach to a longer period library-circulation data.
    • The effectiveness of the proposed targeted advertising approach is influenced by the availability of a product category.
    • The adoption of a product may propagate through a social network. Integrating the effect of influence propagation to the targeted advertising approach is an interest direction.