Query-Driven Graph-Based User Recommender System

Abstract

Current Social Networking Systems (SNS) such as YouTube are creator-driven systems in which creators create content and users search among available content to find what they want. However, queries from users can be time-sensitive, such as some real-time hot topics, which are difficult to obtain at the very moment due to their timeliness and dynamically changing nature. To address this situation, we quest if the system can directly let a user input a query, match the most relevant users (receivers) based on the query and let the receivers decide whether to respond with the very content. In this way, the user can obtain the most relevant data through highly relevant receivers while reducing the reliance on the system's existing data in the recommendation process as an alternative, a new query-driven SNS paradigm.

The main objective is to target the most relevant receivers based on a query. In this case, we propose that by allowing users to provide their very moment ideas as queries, the system searches and ranks well-targeted users based on the semantic content of the query and existing user features. However, the user's feature might be incomplete or missing. To alleviate this issue, we propose a novel two-stage query-driven graph-based user recommender system (QDG) that supports query-to-user matching with dynamic update capabilities. In the first stage, we encode the query and item descriptions into attribute features and perform a similarity search to target the Top-N candidate items. In the second stage, we propose a temporal-based graph neural network (t-GNN), which combines the inductive learning-based GNN with the self-attention-based temporal analysis module to predict the most relevant user-item interaction by simultaneously extracting the existing Spatio-temporal features, where spatial feature represents user's relationship with items and temporal feature represents user's behaviour information. We conducted recommendation simulations on six million users and 150,000 merchants on North America YELP data. Experiments show that the QDG system can accurately target strongly relevant users in the North American population based on the query. To the best of our knowledge, we are the first to propose query-driven SNS and demonstrate its effectiveness in a million-scale Yelp dataset.