Session-based k-NNs with Semantic Suggestions for Next-item Prediction

TL;DR

This paper introduces a semantic-enhanced session-based k-NN recommendation model that detects interest shifts during user sessions by leveraging NLP techniques on product titles, improving next-item prediction accuracy in e-commerce.

Contribution

The paper proposes a novel cSkNN model extension that incorporates semantic-level properties for better interest change detection and recommendation adaptation in session-based recommendations.

Findings

Improved accuracy over existing SkNN methods on e-commerce data

Semantic-based change detection enhances recommendation relevance

Two versions of the extension outperform baseline models

Abstract

One of the most critical problems in e-commerce domain is the information overload problem. Usually, an enormous number of products is offered to a user. The characteristics of this domain force researchers to opt for session-based recommendation methods, from which nearest-neighbors-based (SkNN) approaches have been shown to be competitive with and even outperform neural network-based models. Existing SkNN approaches, however, lack the ability to detect sudden interest changes at a micro-level, i.e., during an individual session; and to adapt their recommendations to these changes. In this paper, we propose a conceptual (cSkNN) model extension for the next-item prediction allowing better adaptation to the interest changes via the semantic-level properties. We use an NLP technique to parse salient concepts from the product titles to create linguistically based product generalizations that are used for change detection and a recommendation list post-filtering. We conducted experiments with two versions of our extension that differ in semantics derivation procedure while both showing an improvement over the existing SkNN method on a sparse fashion e-commerce dataset.