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

In dynamic interaction graphs, user-item interactions usually followheterogeneous patterns, represented by different structural information, suchas user-item co-occurrence, sequential information of user interactions and thetransition probabilities of item pairs. However, the existing methods cannotsimultaneously leverage all three structural information, resulting insuboptimal performance. To this end, we propose TriSIM4Rec, a triple structuralinformation modeling method for accurate, explainable and interactiverecommendation on dynamic interaction graphs. Specifically, TriSIM4Rec consistsof 1) a dynamic ideal low-pass graph filter to dynamically mine co-occurrenceinformation in user-item interactions, which is implemented by incrementalsingular value decomposition (SVD); 2) a parameter-free attention module tocapture sequential information of user interactions effectively andefficiently; and 3) an item transition matrix to store the transitionprobabilities of item pairs. Then, we fuse the predictions from the triplestructural information sources to obtain the final recommendation results. Byanalyzing the relationship between the SVD-based and the recently emerginggraph signal processing (GSP)-based collaborative filtering methods, we findthat the essence of SVD is an ideal low-pass graph filter, so that the interestvector space in TriSIM4Rec can be extended to achieve explainable andinteractive recommendation, making it possible for users to actively breakthrough the information cocoons. Experiments on six public datasetsdemonstrated the effectiveness of TriSIM4Rec in accuracy, explainability andinteractivity.

Triple Structural Information Modelling for Accurate, Explainable and Interactive Recommendation