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LARS An Efficient and Scalable Location-Aware Recommender System 
LARS*: An Efficient and Scalable Location-Aware Recommender System 
Contact: 9703109334, 9533694296 
Email id: academicliveprojects@gmail.com, www.logicsystems.org.in 
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.
LARS An Efficient and Scalable Location-Aware Recommender System 
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. 
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. 
Contact: 9703109334, 9533694296 
PROPOSED SYSTEM: 
Email id: academicliveprojects@gmail.com, www.logicsystems.org.in
LARS An Efficient and Scalable Location-Aware Recommender System 
 LARS* achieves higher locality gain using a better user partitioning data structure and 
 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: 
 System : Pentium IV 2.4 GHz. 
 Hard Disk : 40 GB. 
 Floppy Drive : 1.44 Mb. 
 Monitor : 15 VGA Colour. 
 Mouse : Logitech. 
 Ram : 512 Mb. 
SOFTWARE REQUIREMENTS: 
 Operating system : Windows XP/7. 
 Coding Language : ASP.net, C#.net 
 Tool : Visual Studio 2010 
 Database : SQL SERVER 2008 
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. 
Contact: 9703109334, 9533694296 
algorithm. 
REFERENCE: 
Email id: academicliveprojects@gmail.com, www.logicsystems.org.in

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lars an efficient and scalable location-aware recommender system

  • 1. LARS An Efficient and Scalable Location-Aware Recommender System LARS*: An Efficient and Scalable Location-Aware Recommender System Contact: 9703109334, 9533694296 Email id: academicliveprojects@gmail.com, www.logicsystems.org.in 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.
  • 2. LARS An Efficient and Scalable Location-Aware Recommender System 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. 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. Contact: 9703109334, 9533694296 PROPOSED SYSTEM: Email id: academicliveprojects@gmail.com, www.logicsystems.org.in
  • 3. LARS An Efficient and Scalable Location-Aware Recommender System  LARS* achieves higher locality gain using a better user partitioning data structure and  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:  System : Pentium IV 2.4 GHz.  Hard Disk : 40 GB.  Floppy Drive : 1.44 Mb.  Monitor : 15 VGA Colour.  Mouse : Logitech.  Ram : 512 Mb. SOFTWARE REQUIREMENTS:  Operating system : Windows XP/7.  Coding Language : ASP.net, C#.net  Tool : Visual Studio 2010  Database : SQL SERVER 2008 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. Contact: 9703109334, 9533694296 algorithm. REFERENCE: Email id: academicliveprojects@gmail.com, www.logicsystems.org.in