Object typicality for effective Web of Things recommendations

With the rapid growth of “Web of Things” (WoT), there is a pressing need to develop effective mechanisms for the intelligent discovery and selection of these things (items). Recommender systems are viable solutions to address the issue of WoT discovery and selection. However, classical recommender systems are weak in handling sparse recommendation spaces which characterize most WoT recommendations. Moreover, classical recommender systems may not be able to scale up to efficiently process a large number of things on the Web, and yet these systems may produce big-error recommendations that diminish users' trusts on utilizing WoT. The main contribution of our research is the design and development of a novel recommendation method which is underpinned by the principle of object typicality verified in the field of cognitive psychology to address the aforementioned issues related to WoT recommendations. Based on the MovieLens benchmark data set, our experimental results show that the proposed recommendation method is effective and produces the least big-errors. Since the proposed method exploits data generalization by operating at item group and user group level during recommendation time, it is more effective and efficient than other baseline methods given sparse training data. Based on the Netflix benchmark data set that simulates a large WoT recommendation space, the proposed method also significantly outperforms state-of-the-art recommendation methods in terms of Mean Absolute Error (MAE). The business implication of our research is that the proposed recommendation method can enhance the situation awareness of WoT applications which facilitate the reuse of enterprise resources and the interoperability among enterprises.