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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Classical recommender systems often assume that historical data arestationary and fail to account for the dynamic nature of user preferences,limiting their ability to provide reliable recommendations in time-sensitivesettings. This assumption is particularly problematic in finance, wherefinancial products exhibit continuous changes in valuations, leading tofrequent shifts in client interests. These evolving interests, summarized inthe past client-product interactions, see their utility fade over time with adegree that might differ from one client to another. To address this challenge,we propose a time-dependent collaborative filtering algorithm that canadaptively discount distant client-product interactions using personalizeddecay functions. Our approach is designed to handle the non-stationarity offinancial data and produce reliable recommendations by modeling the dynamiccollaborative signals between clients and products. We evaluate our methodusing a proprietary dataset from BNP Paribas and demonstrate significantimprovements over state-of-the-art benchmarks from relevant literature. Ourfindings emphasize the importance of incorporating time explicitly in the modelto enhance the accuracy of financial product recommendation.

Adaptive Collaborative Filtering with Personalized Time Decay Functions for Financial Product Recommendation