Preference-Adaptive Meta-Learning for Cold-Start

Anhui Province Key Laboratory of Big Data Analysis and Application （USTC）

Preference-Adaptive Meta-Learning for Cold-Start Recommendation

Li Wang1 , Binbin Jin1 , Zhenya Huang1 , Hongke Zhao2 , Defu Lian1 ,Qi Liu1 and Enhong Chen1∗

1Anhui Province Key Laboratory of Big Data Analysis and Application, School of Data Science, University of Science and Technology of China (USTC); 2The College of Management and Economics, Tianjin University

Reporter: Li Wang

IJCAI-PRICAI 2021

30th International Joint Conference on Artificial Intelligence，19th -26th August,

Montreal-themed Virtual Reality


Introduction

Cold-start problem

When encountering new users, collaborative filtering based approaches fail due to

scarce interactions, leading to a decline in the new users’ experience.

Meta-learning for cold-start recommendation

Most existing works formulate each user as a task and aim to learn globally shared

prior knowledge across all users. The learned prior knowledge can be quickly

adapted to the personalized based on the sparse interactions of cold-start users.

A user

…

A learning task


Introduction

Limitations

Globally shared prior knowledge may be inadequate to discern users’ complicated

behaviors and causes poor generalization.

Solutions: Preference-Specific Meta-Learning

Users with similar preferences should locally share similar prior knowledge so that

it can be easily generalized to these users.

Social relations can provide a guidance to recognize a bundle of users who have

similar preferences and share similar knowledge.


Problem Definition

Given

▶User set: U; Item set: I; Rating set: R.

▶A user u of user set U can be defined as a learning task: 𝓣𝒖 = (𝓕𝒖, 𝓢𝒖, 𝑸𝒖)

𝓕𝒖: the friend set of u.

𝓢𝒖: the support set containing the interacted items.

𝑸𝒖: the query set containing the items to be predicted.

▶Meta-training tasks: 𝓣𝒕𝒓; Meta-testing tasks: 𝓣𝒕𝒆.

Goal

▶Predicting the unknown rating 𝑟𝑢,𝑖^ between the user u and the item i.


Method

Preference-Adaptive Meta-Learning(PAML)

Identifying Implicit Friends over the HIN

In cold-start scenarios, social relations are also sparse, we identify reliable implicit friends

by defining palindrome paths over the user-item-attribute graph.


Users appeared in the same path share similar tastes since they

express the same opinion on the item (blue path).

Users appeared in the same path share similar tastes since they

express the same opinion on the item attribute (red path).

Identifying Implicit Friends over the HIN

Similarity measurement


Methodology

Preference-Adaptive Meta-Learning(PAML)

Integrating a user’s interactions and her friends to capture her overall preference.

Coarse-fine preference modeling

Fine level: Distinguish the strength of social relations and combine them at each rating score.

Coarse level: Learn an overall preference by integrating preferences obtained by the fine level.


Fine level preference modeling

1. We split items of support set

into several groups by rating

scores.

2. Learn an item based user

preference.


Fine level preference modeling

1. Getting the item based preference for

implicit friends and explicit friends.

2. Adopting the attention mechanism to

get two kinds of social-based

preference.


Coarse level preference modeling

Using attention mechanism to aggregate the

preferences under different scores and get the

overall preference.

Prediction

Objective function


Methodology

Meta-learning Framework

Part 1：Preference-specific adaptation Part 2：Local update

Part 3：Global update


Preference-specific adaptation

Preference-specific gates: Preference-specific knowledge:


Meta optimizationLocal update:

Global update:

1. Use preference-specific initial parameters to make predictions for support set.

2. Local update to get the personalized initial parameters.

1. Use personalized initial parameters to make predictions for query set.

2. Global update to get the prior knowledge.


Experiments

Experimental Setups

Datasets: Douban, Yelp

Metrics: RMSE，nDCG@k

RMSE =


Overall performance

Comparison Methods:1. FM/ NeuMF/ Wide & Deep 2. SoReg/ DiffNet 3. MeLU/ MetaEmb/ MAMO

Scenarios:1. UC: User-Cold 2. IC: Item-Cold 3. UIC: User & Item Cold 4.NC: Traditional


Ablation study

Variants of PAML

PAML-I: without implicit friends

PAML-E: without explicit friends

PAML-A: without preference-specific

The results clearly demonstrate that the social relations could contribute to modeling the

user’s preference so that facilitating the performance.

The results not only prove our claim that users with similar preferences should locally share

prior knowledge is reasonable but also demonstrate preference-specific adapter is effective.


Parameter sensitivity

At the beginning, as the number increases,

nDCG@5 also increases, and then it will

reach a stable level.

The results reach the optimal performance at

one local update, and then as the number of

local updates increases, nDCG@5 gradually

decreases.


Thanks!

For more details, please refer to our paper!

Reporter：Li [email protected]

Documents

Preference-Adaptive Meta-Learning for Cold-Start