English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Compared with only pursuing recommendation accuracy, the explainability of arecommendation model has drawn more attention in recent years. Many graph-basedrecommendations resort to informative paths with the attention mechanism forthe explanation. Unfortunately, these attention weights are intentionallydesigned for model accuracy but not explainability. Recently, some researchershave started to question attention-based explainability because the attentionweights are unstable for different reproductions, and they may not always alignwith human intuition. Inspired by the counterfactual reasoning from causalitylearning theory, we propose a novel explainable framework targeting path-basedrecommendations, wherein the explainable weights of paths are learned toreplace attention weights. Specifically, we design two counterfactual reasoningalgorithms from both path representation and path topological structureperspectives. Moreover, unlike traditional case studies, we also propose apackage of explainability evaluation solutions with both qualitative andquantitative methods. We conduct extensive experiments on three real-worlddatasets, the results of which further demonstrate the effectiveness andreliability of our method.

Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation