  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.
