Augmenting recommendation systems using a model of semantically-related terms extracted from user behavior

TL;DR

This paper introduces a novel recommendation system enhancement that leverages semantically-related keywords mined from user behavior data to address cold-start and data sparsity issues, improving recommendation accuracy.

Contribution

The paper presents a language-agnostic, noise-resistant method to extract semantic relationships from user search logs, enhancing recommender systems with minimal user data.

Findings

Improved recommendation accuracy in cold-start scenarios

Effective semantic relationship extraction from user logs

Language-agnostic and noise-resistant approach

Abstract

Common difficulties like the cold-start problem and a lack of sufficient information about users due to their limited interactions have been major challenges for most recommender systems (RS). To overcome these challenges and many similar ones that result in low accuracy (precision and recall) recommendations, we propose a novel system that extracts semantically-related search keywords based on the aggregate behavioral data of many users. These semantically-related search keywords can be used to substantially increase the amount of knowledge about a specific user's interests based upon even a few searches and thus improve the accuracy of the RS. The proposed system is capable of mining aggregate user search logs to discover semantic relationships between key phrases in a manner that is language agnostic, human understandable, and virtually noise-free. These semantically related keywords are obtained by looking at the links between queries of similar users which, we believe, represent a largely untapped source for discovering latent semantic relationships between search terms.