Personalised context-aware re-ranking in recommender system

Abstract

Recommender systems can help correlate information and recommend personalised services to users as a general information filtering tool. However, contextual factors significantly affect user behaviour, especially in the Internet of Things (IoT), which brings difficulties to modelling user preferences. In this paper, we propose a personalised context-aware re-ranking algorithm (p-CAR) in IoT. Our primary purpose is to improve the recommender performance from multiple metrics, such as precision, recall, diversity, and popularity. The core idea is to re-rank the ranking list using the user's preference behaviour under different contexts. The re-ranking process is an iterative selection process; each time an optimal item that meets the target criteria is selected from the candidate items and added to the re-ranked list. The selection of items depends on the given context and the user's interest in that context. User's preference and interest in contexts are both expressed by probability in our algorithm. In addition, we use a weight parameter to control the influence of contexts and model the contextual personalisation of different users through local personalisation parameters. We verify our algorithm through experiments on the real Movielens 100K dataset and show the performance advantage with the existing algorithm.