soc2seq: Social Embedding meets Conversation Model

TL;DR

This paper introduces soc2seq, a novel personalized conversation model that integrates social graph data and user preferences to generate more meaningful and tailored textual replies in social applications.

Contribution

The paper presents a new approach combining social graph information with content-based models for personalized conversation generation, advancing beyond existing auto-reply systems.

Findings

Models effectively incorporate social and content data for personalized replies

Enhanced reply relevance and user engagement demonstrated

Framework adaptable to various social media platforms

Abstract

While liking or upvoting a post on a mobile app is easy to do, replying with a written note is much more difficult, due to both the cognitive load of coming up with a meaningful response as well as the mechanics of entering the text. Here we present a novel textual reply generation model that goes beyond the current auto-reply and predictive text entry models by taking into account the content preferences of the user, the idiosyncrasies of their conversational style, and even the structure of their social graph. Specifically, we have developed two types of models for personalized user interactions: a content-based conversation model, which makes use of location together with user information, and a social-graph-based conversation model, which combines content-based conversation models with social graphs.