A web search-centric approach to recommender systems with URLs as minimal user contexts

In service-oriented computing, a recommender system can be wrapped as a web service with machine-readable interface. However, owing to the cross-organizational privacy issue, the internal dataset of an organization is seldom exposed to external services. In this paper, we propose a higher level recommender strategy INSERT that guides the underlying external universal recommender to suggest a set of indexes. INSERT then matches the title of each top-ranked index entry with the domain-specific keywords in the organization's internal dataset, and further directs the universal recommender to verify the popularity of such matching. INSERT finally makes recommendation based on the verification results. INSERT also employs URLs taken from a client as user contexts, which is challenging because URLs contain little content. Our experiment shows that this strategy is feasible and effective.