Selecting Comparative Sets of Reviews Across Multiple Items

Code

• github.com/PreferredAI/CompaReSetS
  none★ 0

Abstract

While choosing among several products, users may look up reviews from each product they are considering. Due to the large number of reviews of products, selecting representative reviews from one product alone is already a challenging problem. In this work, we further aim to conduct review selection for multiple products simultaneously for comparative purposes. We formulate objective functions that synchronize the review selection and design efficient algorithms to optimize for the objective functions. To narrow down the potentially long list of comparison items into a shorter list of more similar items, we construct a graph representing items’ similarity and design efficient algorithms to find the heaviest 𝑘-subgraph including the target item. The results are validated on real world datasets on various product categories.

Reproductions