Comparison of Two Segmentations Methods for Library Recommender Systems
Wing-Kee Ho · Carolina Digital Repository (University of North Carolina at Chapel Hill) · 2019
Building recommender systems is usually divided into two processes: (1) segmenting the dataset such that elements with similar pattern can be grouped together, and (2) performing association rules that tell how likely the two elements occur together. For the first process, between clustering method and LC subject heading classification, which segmentation method is more appropriate to build the library circulation recommender systems? Based on the association rules generated from two different simulated datasets, we consistently find that using clustering method to segment the dataset yields a higher level of support and confidence. However, consider that forming distinct clusters is not likely to happen in reality, together with patron's interest may change swiftly over time. Using clustering as the segmentation method will finally generate many irrelevant association rules. As a result, we conclude that using LC classification to segment the data is more appropriate and secure.