Recommendation Item Selection Algorithm Considering the Recommendation Region in Embedding Space and New Evaluation Metric
Tomoki Amano, Ryotaro Shimizu, Masayuki Goto · Industrial Engineering & Management Systems · 2023
In recent years, recommender systems based on machine learning have become common tools on various web services. Among recommendation models, embedding methods such as Item2Vec that utilize embedded representations of items are widely used in actual applications due to their effectiveness and ease of use. By utilizing embedded representations acquired through learning the interaction between users and items, it is easy to discover similar items from the viewpoint of the user’s purchasing tendencies. In contrast, with this method, only biased items are recommended, making it difficult to ensure a wide variety of recommended items. However, there is a trade-off between the diversity of recommended items and accuracy and providing diversity in recommended items while maintaining accuracy is a challenging problem. Therefore, in this study, we propose a method to expand the new evaluation metric "recommendation region" (sum of distances of recommended items from the user vector in the embedding space) without significantly reducing accuracy. Specifically, we recommend not only items that are close to the user vector in the embedding space but also items with a certain distance based on detailed observation of the positional relationships. With the proposed method, we aim to increase user satisfaction by expanding the diversity of items that the user comes into contact with in the service. Finally, we demonstrate the usefulness of our proposed method through evaluation experiments using open-source datasets.