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.
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.
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.
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 .
21.
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
22.
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 ：
23.
Targeted advertising based on cohesive subgroups
Obviously, the above linear programming problem is a special case of the fractional 0-1 knapsack problem.
24.
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.
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.
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.
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