Session-based k-NNs with Semantic Suggestions for Next-item Prediction
This research provides an incremental improvement for e-commerce platforms by enhancing session-based recommendation systems to better adapt to user interest changes.
This paper addresses the information overload problem in e-commerce by proposing a conceptual extension to session-based k-nearest neighbors (SkNN) models for next-item prediction. The extension, cSkNN, uses NLP techniques to parse salient concepts from product titles, enabling better adaptation to sudden interest changes within a session and improving recommendations on a sparse fashion e-commerce dataset.
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.