  Accumulation of large amount of location-specific reviews on web due to
escalating popularity of Location-based Social Networking platforms like Yelp,
Foursquare, Brightkite etc. in recent years, has created the opportunity to
discover location-specific activities and develop myriads of location-aware
activity recommendation applications. The performance and popularity of such
recommendation applications greatly depend on the richness and accuracy of the
back-end knowledgebase, which intern is regulated by information relevancy and
redundancy issues. Existing work on activity discovery have not made any
attempt to ensure relevancy and non-redundancy of discovered knowledge (i.e.,
location-specific activities). Moreover, majority of these work have utilized
body-worn sensors, images or human GPS traces and discovered generalized
activities that do not convey any location-specific knowledge.
  In this thesis, we address the mentioned issues with serious concern and
propose an effective solution to discover Location-specific Activity Network,
in short LANet from location-aware reviews. The information network LANet
serves as an accurate, enriched and unified knowledgebase of a Location-aware
Activity Recommendation System. While building LANet, we also introduce novel
ideas like, activity-based location similarity detection and measuring
uniqueness, generality/speciality of an activity at a particular location to
enrich the said knowledge base to a great extent. Experimental results show the
information richness and accuracy of the proposed knowledge base which is
comparable to human perception and accounts for our success in achieving the
desired solution.
