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 human-computer interaction, understanding user behaviors and tailoringsystems accordingly is pivotal. To this end, general-purpose userrepresentation learning based on behavior logs is emerging as a powerful toolin user modeling, offering adaptability to various downstream tasks such asitem recommendations and ad conversion prediction, without the need tofine-tune the upstream user model. While this methodology has shown promise incontexts like search engines and e-commerce platforms, its fit for instantmessaging apps, a cornerstone of modern digital communication, remains largelyuncharted. These apps, with their distinct interaction patterns, datastructures, and user expectations, necessitate specialized attention. Weexplore this user modeling approach with Snapchat data as a case study.Furthermore, we introduce a novel design and evaluation framework rooted in theprinciples of the Measurement Process Framework from social science researchmethodology. Using this new framework, we design a Transformer-based user modelthat can produce high-quality general-purpose user representations for instantmessaging platforms like Snapchat.

Designing and Evaluating General-Purpose User Representations Based on  Behavioral Logs from a Measurement Process Perspective: A Case Study with  Snapchat