An Improved Hybrid Recommender System: Integrating Document Context-Based and Behavior-Based Methods

TL;DR

This paper introduces a hybrid recommender system that combines document context-based and behavior-based methods, enhancing accuracy and enabling online personalization, tested successfully on real-world datasets.

Contribution

It presents a novel hybrid model integrating document context and collaborative filtering with online update capabilities, improving recommendation quality.

Findings

Outperforms baseline methods on three real-world datasets.

Supports efficient online personalization with user interaction updates.

Enhances recommendation accuracy by combining textual data and user behavior.

Abstract

One of the main challenges in recommender systems is data sparsity which leads to high variance. Several attempts have been made to improve the bias-variance trade-off using auxiliary information. In particular, document modeling-based methods have improved the model's accuracy by using textual data such as reviews, abstracts, and storylines when the user-to-item rating matrix is sparse. However, such models are insufficient to learn optimal representation for users and items. User-based and item-based collaborative filtering, owing to their efficiency and interpretability, have been long used for building recommender systems. They create a profile for each user and item respectively as their historically interacted items and the users who interacted with the target item. This work combines these two approaches with document context-aware recommender systems by considering users' opinions on these items. Another advantage of our model is that it supports online personalization. If a user has new interactions, it needs to refresh the user and item history representation vectors instead of updating model parameters. The proposed algorithm is implemented and tested on three real-world datasets that demonstrate our model's effectiveness over the baseline methods.