  In recent years, with the enormous explosion of web based learning resources,
personalization has become a critical factor for the success of services that
wish to leverage the power of Web 2.0. However, the relevance, significance and
impact of tailored content delivery in the learning domain is still
questionable. Apart from considering only interaction based features like
ratings and inferring learner preferences from them, if these services were to
incorporate innate user profile attributes which affect learning activities,
the quality of recommendations produced could be vastly improved. Recognizing
the crucial role of effective guidance in informal educational settings, we
provide a principled way of utilizing multiple sources of information from the
user profile itself for the recommendation task. We explore factors that affect
the choice of learning resources and explain in what way are they helpful to
improve the pedagogical accuracy of learning objects recommended. Through a
systematical application of machine learning techniques, we further provide a
technological solution to convert these indirectly mapped learner specific
attributes into a direct mapping with the learning resources. This mapping has
a distinct advantage of tagging learning resources to make their metadata more
informative. The results of our empirical study depict the similarity of
nominal learning attributes with respect to each other. We further succeed in
capturing the learner subset, whose preferences are most likely to be an
indication of learning resource usage. Our novel system filters learner profile
attributes to discover a tag that links them with learning resources.
