Semantic web, since its inception is approved for providing contexts to the search strings applicable to a given domain. Various frameworks or models based on semantic technologies utilizing semantic enhanced annotations and reasoning are recognized to deliver more relevant outputs. Thus, Semantic Web based recommenders are required for enriched recommendations in this age of information overload on the web. Contextual data may be used not only to represent domain objects and the user preferences in a more precise and refined way but also to apply better matching procedures with the aid of semantic similarity measures. Also, the presently used content-based recommendation techniques and collaborative filtering ones may certainly benefit from the introduction of explicit domain knowledge to produce recommendations using logical inferences applicable in that domain. Both recommender systems and semantic web complement each other and may aid in their progress mutually. In the last decade, there has been some research work done utilizing the semantic web technologies for aiding recommender systems, which play a significant role towards the goal of semantic web. In this paper, first, recommender systems (RS) have been discussed along with key research concerns, benefits and issues being explored and revisited. Second, scope and literature survey has been presented in the track of how semantic web technologies have contributed to enhancements of RS. Third, the role of various semantic web technologies has been explored and discussed for enhancement of present recommender systems. Fourth, useful inferences of the work done are tabulated along with the key discussions.

Semantic aided recommender systems, ontology, semantic web technologies, recommendation issues. Linked open dataset

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