Thursday, 17 July 2014
LARS*: An Efficient and Scalable Location-Aware Recommender System
LARS*: AN EFFICIENT AND SCALABLE LOCATION-AWARE RECOMMENDER SYSTEM
This paper proposes LARS*, a location-aware recommender system that uses location-based ratings to produce recommendations. Traditional recommender systems do not consider spatial properties of users nor items; LARS*, on the other hand, supports a taxonomy of three novel classes of location-based ratings, namely, spatial ratings for non-spatial items, non-spatial ratings for spatial items, and spatial ratings for spatial items. LARS* exploits user rating locations through user partitioning, a technique that influences recommendations with ratings spatially close to querying users in a manner that maximizes system scalability while not sacrificing recommendation quality. LARS* exploits item locations using travel penalty, a technique that favors recommendation candidates closer In travel distance to querying users in a way that avoids exhaustive access to all spatial items. LARS* can apply these techniques separately, or together, depending on the type of location-based rating available. Experimental evidence using large-scale real-world data from both the Foursquare location-based social network and the MovieLens movie recommendation system reveals that LARS* is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches.
The technique used by many of these systems is collaborative filtering (CF), which analyzes past community opinions to find correlations of similar users and items to suggest k personalized items (e.g., movies) to a querying user u. Community opinions are expressed through explicit ratings represented by the triple (user, rating, item) that represents a user providing a numeric rating for an item. Currently, myriad applications can produce location-based ratings that embed user and/or item locations. For example, location-based social networks (e.g., Foursquare and Facebook Places ) allow users to “check-in” at spatial destinations (e.g., restaurants) and rate their visit, thus are capable of associating both user and item locations with ratings. Such ratings motivate an interesting new paradigm of location-aware recommendations, whereby the recommender system exploits the spatial aspect of ratings when producing recommendations. Existing recommendation techniques assume ratings are represented by the (user, rating, item) triple, thus are ill-equipped to produce location-aware recommendations.
DISADVANTAGES OF EXISTING SYSTEM:
v Used only Location based ratings.
v They do not consider travel locality.
v Novel classification of three types of location-based ratings not supported.
In this paper, we propose LARS*, a novel locationaware recommender system built specifically to produce highquality location-based recommendations in an efficient manner.
LARS* produces recommendations using a taxonomy three types of location-based ratings within a single framework:
(1) Spatial ratings for non-spatial items, represented as a four-tuple (user, ulocation, rating, item), where ulocation represents a user location, for example, a user located at home rating a book; (2) non-spatial ratings for spatial items, represented as a four-tuple (user, rating, item, ilocation), where ilocation represents an item location, for example, a user with unknown location rating a restaurant; (3) spatial ratings for spatial items, represented as a five-tuple (user, ulocation, rating, item, ilocation), for example, a user at his/her office rating a restaurant visited for lunch. Traditional rating triples can be classified as non-spatial ratings for nonspatial items and do not fit this taxonomy.
ADVANTAGES OF PROPOSED SYSTEM:
v Preference locality suggests users from a spatial region (e.g., neighborhood) prefer items (e.g., movies, destinations) that are manifestly different than items preferred by users from other, even adjacent, regions.
v A novel location-aware recommender system capable of using three classes of location based ratings.
Speed - 1.1 Ghz
RAM - 512 MB(min)
Hard Disk - 40 GB
Key Board - Standard Windows Keyboard
Mouse - Two or Three Button Mouse
Monitor - LCD/LED
Operating system : Windows XP.
Coding Language : .Net
Data Base : SQL Server 2005
Tool : VISUAL STUDIO 2008.
Mohamed Sarwt∗, Justin J. Levandoski†, Ahmed Eldawy∗ and Mohamed F. Mokbel∗, “LARS*: An Efficient and Scalable Location-Aware Recommender System” TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 26, NO. 6, June 2014