Autumn CS 224 w Social and Information Network Analysis Final
نویسندگان
چکیده
User generated online reviews on products or services are valuable to other users to make informed decisions. Thus, identifying experts in reviewers becomes important. We tackle the problem of reviewer expertise ranking in online review datasets by analyzing the bipartite graphs of reviewers and reviewed items, an approach that has not been considered in previous studies of the problem. We use Co-HITS as our primary ranking algorithm, and compare it with other ranking metrics such as degree and pagerank in reviewer graphs. We evaluate the rankings using Kendall rank correlation coefficient, and validate our results against the real datasets.
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