Latency of Neighborhood Based Recommender Systems

Latency of user-based and item-based recommenders is evaluated. The two algorithms can deliver high quality predictions in dynamically changing environments. However, their response time depends not only on the size, but also on the structure of underlying datasets. This constitutes a major drawback when compared to two other competitive approaches i.e. content-based and model-based systems. Therefore, we believe that there exists a need for comprehensive evaluation of the latency of the two algorithms. During a typical worst case scenario analysis of collaborative filtering algorithms two assumption are made. The first assumption says that data are stored in dense collections. The second assumption states that large amount of computations can be performed in advance during the training phase. As a result it is advised to deploy user-based system when the number of users is relatively small. Item-based algorithms are believed to have better technical properties when the number of items is small. We consider a situation in which the two assumptions are not necessarily met. We show that even though the latency of the two methods depends heavily on the proportion of users to items, this factor does not differentiate the two methods. We evaluate the algorithms with several real-life datasets. We augment the analysis with both graph-theoretical and experimental techniques.