Characterizing Users in a Classified Ad Network Open Access
- Other title
Social Network Analysis
Classified Ad Network
- Type of item
- Degree grantor
University of Alberta
- Author or creator
- Supervisor and department
Rafiei, Davood (Computing Science)
- Examining committee member and department
Moore, Sarah (Business)
Rafiei, Davood (Computing Science)
Barbosa, Denilson (Computing Science)
Wong, Kenny (Computing Science)
Department of Computing Science
- Date accepted
- Graduation date
Master of Science
- Degree level
We study the problem of classifying users in a classified ad network and its applications in further analyzing the network. Specifically, we seek to classify Kijiji users into one of the two business and non-business categories. The problem is challenging due to the sparsity of the data about users, the vague separation of the two classes, and the highly imbalanced distribution of users between the classes. Our work utilizes the ad content to build a set of distinctive terms for each class (profile). Given the statistics on how an ad mentions terms from a class profile, the affinity of an ad (and subsequently a user) to a particular class is determined. Our experiments reveal that this is an effective strategy for classifying users, outperforming various baselines. We study the impact of profile size on the classification task and observe that using longer class profiles may not be helpful. Moreover, in the absence of labeled training data, we show that a simple bootstrapping technique with only a few n-grams as a seed set can give nearly good results in terms of F-measure.
We also study the same problem from a different angle: collective behavior of a user in posting ads. Using features associated with such behavior, we identify four distinct usage patterns for the users of the Kijiji network and study the association of business and non-business users with these patterns. Our experiments reveal that a sizeable number of members from both user groups validly manifest all the patterns, due to which the aforementioned features are inadequate for the classification task.
Finally, using the results of user classification, we analyze the Kijiji network from various aspects. Our results, for example, indicate that businesses are more amenable to post consistently in a particular set of categories than non-business users and that the popularity of different categories for both the user groups exhibits various seasonal trends.
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