LARS*: An Efficient and Scalable Location-Aware Recommender System

LARS*: An Efficient and Scalable Location-Aware Recommender System

ABSTRACT:

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.

EXISTING SYSTEM:

Recommender systems make use of community opinions to help users identify useful items from a considerably large search space. 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. Myriad applications can produce location-based ratings that embed user and/or item locations. Existing recommendation techniques assume ratings are represented by the (user, rating, item) triple.

DISADVANTAGES OF EXISTING SYSTEM:

·        The existing systems are ill-equipped to produce location aware recommendations.

·        The existing system provides more expensive operations to maintain the user partitioning structure.

·        The existing system does not provide spatial ratings.

PROPOSED SYSTEM:

We have proposed LARS*, a location-aware recommender system that uses location-based ratings to produce recommendations. LARS*, 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. Within LARS*, we propose:

(a) A user partitioning technique that exploits user locations in a way that maximizes system scalability while not sacrificing recommendation locality

(b) A travel penalty technique that exploits item locations and avoids exhaustively processing all spatial recommendation candidates.

ADVANTAGES OF PROPOSED SYSTEM:

·        LARS*, 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* achieves higher locality gain using a better user partitioning data structure and algorithm.

·        LARS* exhibits a more flexible tradeoff between locality and scalability.

·        LARS* provides a more efficient way to maintain the user partitioning structure

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

Ø System                          :         Pentium IV 2.4 GHz.

Ø Hard Disk                      :         40 GB.

Ø Floppy Drive                 :         1.44 Mb.

Ø Monitor                         :         15 VGA Colour.

Ø Mouse                            :         Logitech.

Ø Ram                               :         512 Mb.

SOFTWARE REQUIREMENTS:

Ø Operating system           :         Windows XP/7.

Ø Coding Language :         ASP.net, C#.net

Ø Tool                     :         Visual Studio 2010

Ø Database              :         SQL SERVER 2008

REFERENCE:

Mohamed Sarwat, Justin J. Levandoski, Ahmed Eldawy, and Mohamed F. Mokbel. “LARS*: An Efficient and Scalable Location-Aware Recommender System”. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 26, NO. 6, JUNE 2014.