Session-based Recommender Systems with Real-time Learning using Distributed Representations

Abstract

The study proposes recommender systems which learn the distributed representation of items, sessions, and users from session data by using Item2Vec. For a recommendation query, the systems construct a session-specific distributed representation for the user (real-time user representation) in real-time via a simple computation method. In addition, we propose NN(Nearest Neighbors)-type and CF(Collaborative Filtering)-type search approaches which consider real-time user representations only and similar user representations, respectively. The experimental results suggest that the proposed systems are well balanced in accuracy, diversity, and novelty compared with the baseline systems. Moreover, CF-type search is superior to NN-type search in terms of accuracy, diversity, and novelty.