Beyond One-Size-Fits-All: A Study of Neural and Behavioural Variability Across Different Recommendation Categories

TL;DR

This study explores neural and behavioral responses to different recommendation types in e-commerce, revealing variability across categories and individuals, and emphasizing the importance of user-centric evaluation beyond accuracy.

Contribution

It introduces a novel analysis of neural and behavioral variability across recommendation categories, integrating EEG and behavioral data for comprehensive insights.

Findings

Distinct neural and behavioral patterns for each recommendation category

Significant inter-subject variability in responses

Insights into user decision-making processes

Abstract

Traditionally, Recommender Systems (RS) have primarily measured performance based on the accuracy and relevance of their recommendations. However, this algorithmic-centric approach overlooks how different types of recommendations impact user engagement and shape the overall quality of experience. In this paper, we shift the focus to the user and address for the first time the challenge of decoding the neural and behavioural variability across distinct recommendation categories, considering more than just relevance. Specifically, we conducted a controlled study using a comprehensive e-commerce dataset containing various recommendation types, and collected Electroencephalography and behavioural data. We analysed both neural and behavioural responses to recommendations that were categorised as Exact, Substitute, Complement, or Irrelevant products within search query results. Our findings offer novel insights into user preferences and decision-making processes, revealing meaningful relationships between behavioural and neural patterns for each category, but also indicate inter-subject variability.