Due to the increase in the importance of giving real-time recommendation to e-commerce users, session-based recommender systems become more popular. Session-based recommendation systems have the ability to adapt quickly to respond to changes in user interests and newly added items. The ranking is the core part of recommender systems regardless of recommender system type. Re-ranking is applied to recommender systems to have more personalised recommendations by considering context-awareness. In this paper, we proposed an approach to re-rank recommended items by using a linear regression model. In our approach, we use users' current session features and temporal features of recommended items to measure a user's interest level on a recommended item. We focus on having better recall and precision scores with fewer recommendations to able to prove the success of our re-ranking strategy. We conduct computational experiments on six real-world datasets and show that after applying re-ranking, we can get higher recall and precision scores. These results confirm that taking user interest level on an item in a session into account can improve the chance of getting correct items in top 5 recommendations.
|Name||2020 International Joint Conference on Neural Networks (IJCNN)|
|Conference||IEEE World Congress on Computational Intelligence (WCCI) 2020|
|Period||19/07/20 → 24/07/20|
- session-based recommendation
- linear regression