Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
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In this paper we describe the collaborative filtering feature of a location-aware, Web content recommendation service, called Gloe. The main purpose of our collaborative filtering solution is to increase the diversity of recommendations and to thereby mitigate popularity bias. The key challenge is to filter candidate suggestions in real-time, with minimal data mining and model building overhead. There is an apparent trade-off between building general purpose reusable models with contributions from a large user base on one hand and efficient on-line evaluation and recommendation in realtime on the other hand. Our solution is to apply item-based, top-N collaborative filtering within a hierarchical folksonomy structure in a Geohash pre-partitioned geographic locale. We demonstrate that these recommendations can be, on average, as fast to compute as aggregate rating-based recommendations, while offering a more diverse as well as personalized set of recommendations.