A Point of Interest Recommendation Engine for Suggesting Sightseeing Spots by User Reviews and Ratings

Computer engineering

Tourism has become an important industry for most of the economies, especially for non-industrialized countries where it represents the main source of income. Recommendation systems are defined as the techniques used to predict the rating one individual will give to an item or social entity. These items can be places, books, movies, restaurants and things on which individuals have different preferences. These preferences are being predicted using two approaches first content-based approach which involves characteristics of an item and second collaborative filtering approaches which takes into account user's past behavior to make choices. Point of interest (POI) recommendation, which provides personalized recommendation of places to users. However, quite different from traditional interest oriented merchandise recommendation, POI recommendation is more complex due to the timing effects: we need to examine whether the POI fits a user’s availability. With increasing adoption and presence of Online services, designing novel approaches for efficient and effective recommendation has become of paramount importance. In existing services discovery and recommendation approaches focus on keyword dominant Web service search engines, which possess many limitations such as poor recommendation performance and heavy dependence on correct and complex queries from users. Recent research efforts on Online service recommendation center on two prominent approaches: collaborative filtering and content based recommendation. Unfortunately, both approaches have some drawbacks, which restrict their applicability in Web service recommendation. In proposed system for recommendation we will be using Agglomerative Hierarchal Clustering and collaborative filtering for effective recommendation in this system.

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