# Learning from My Friends: Few-Shot Personalized Conversation Systems via Social Networks

arXiv: https://arxiv.org/abs/2105.10323 · HTML: https://mlsanity.com/paper/2105.10323/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 56 references that are in the map.
- First posted on arXiv 21 May 2021.

## Builds on (most cited first, 30 of 35)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [A Diversity-Promoting Objective Function for Neural Conversation Models](https://mlsanity.com/paper/1510.03055/) (2015)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [CTRL: A Conditional Transformer Language Model for Controllable Generation](https://mlsanity.com/paper/1909.05858/) (2019)
- [A Persona-Based Neural Conversation Model](https://mlsanity.com/paper/1603.06155/) (2016)
- [Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders](https://mlsanity.com/paper/1703.10960/) (2017)
- [Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models](https://mlsanity.com/paper/1507.04808/) (2015)
- [Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory](https://mlsanity.com/paper/1704.01074/) (2017)
- [Training Millions of Personalized Dialogue Agents](https://mlsanity.com/paper/1809.01984/) (2018)
- [Neural Responding Machine for Short-Text Conversation](https://mlsanity.com/paper/1503.02364/) (2015)
- [Personalizing Dialogue Agents via Meta-Learning](https://mlsanity.com/paper/1905.10033/) (2019)
- [Personalized Dialogue Generation with Diversified Traits](https://mlsanity.com/paper/1901.09672/) (2019)
- [A Pre-training Based Personalized Dialogue Generation Model with Persona-sparse Data](https://mlsanity.com/paper/1911.04700/) (2019)
- [Towards Exploiting Background Knowledge for Building Conversation Systems](https://mlsanity.com/paper/1809.08205/) (2018)
- [Conversing by Reading: Contentful Neural Conversation with On-demand Machine Reading](https://mlsanity.com/paper/1906.02738/) (2019)
- [Personalization in Goal-Oriented Dialog](https://mlsanity.com/paper/1706.07503/) (2017)
- [GSN: A Graph-Structured Network for Multi-Party Dialogues](https://mlsanity.com/paper/1905.13637/) (2019)
- [DeepCopy: Grounded Response Generation with Hierarchical Pointer Networks](https://mlsanity.com/paper/1908.10731/) (2019)
- [Addressee and Response Selection in Multi-Party Conversations with Speaker Interaction RNNs](https://mlsanity.com/paper/1709.04005/) (2017)
- [Learning Personalized End-to-End Goal-Oriented Dialog](https://mlsanity.com/paper/1811.04604/) (2018)
- [Augmenting End-to-End Dialog Systems with Commonsense Knowledge](https://mlsanity.com/paper/1709.05453/) (2017)
- [Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks](https://mlsanity.com/paper/1910.14326/) (2019)
- [Learning Personas from Dialogue with Attentive Memory Networks](https://mlsanity.com/paper/1810.08717/) (2018)

## Built on by (most cited first, 5 of 5)

- [A Model-Agnostic Data Manipulation Method for Persona-based Dialogue Generation](https://mlsanity.com/paper/2204.09867/) (2022)
- [COSPLAY: Concept Set Guided Personalized Dialogue Generation Across Both Party Personas](https://mlsanity.com/paper/2205.00872/) (2022)
- [Improving Meta-learning for Low-resource Text Classification and Generation via Memory Imitation](https://mlsanity.com/paper/2203.11670/) (2022)
- [Unify Graph Learning with Text: Unleashing LLM Potentials for Session Search](https://mlsanity.com/paper/2505.14156/) (2025)
- [Post Persona Alignment for Multi-Session Dialogue Generation](https://mlsanity.com/paper/2506.11857/) (2025)

## Abstract (arXiv)

Personalized conversation models (PCMs) generate responses according to speaker preferences. Existing personalized conversation tasks typically require models to extract speaker preferences from user descriptions or their conversation histories, which are scarce for newcomers and inactive users. In this paper, we propose a few-shot personalized conversation task with an auxiliary social network. The task requires models to generate personalized responses for a speaker given a few conversations from the speaker and a social network. Existing methods are mainly designed to incorporate descriptions or conversation histories. Those methods can hardly model speakers with so few conversations or connections between speakers. To better cater for newcomers with few resources, we propose a personalized conversation model (PCM) that learns to adapt to new speakers as well as enabling new speakers to learn from resource-rich speakers. Particularly, based on a meta-learning based PCM, we propose a task aggregator (TA) to collect other speakers' information from the social network. The TA provides prior knowledge of the new speaker in its meta-learning. Experimental results show our methods outperform all baselines in appropriateness, diversity, and consistency with speakers.