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12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
12/18 regular meeting paper
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12/18 regular meeting paper

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

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