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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.