Lars an efficient and scalable location aware recommender system
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The document presents Lars, a novel location-aware recommender system that utilizes location-based ratings to enhance recommendations by considering spatial properties. It combines three classes of location ratings, integrating user partitioning and travel penalties to improve scalability and quality compared to traditional recommendation methods. Lars efficiently generates personalized suggestions based on user and item locations, distinguishing itself from existing systems which often lack personalization.
Lars an efficient and scalable location aware recommender system
- 1. LeMeniz Infotech 36, 100 feet Road, Natesan Nagar(Near Indira Gandhi Statue, Next to Fish-O-Fish), Pondicherry-605 005 Call: 0413-4205444, +91 99625 88976, 95663 55386. For More Projects Titles Visits : www.lemenizinfotech.com | Call Us : 9962588976 /9566355386 Do Your Projects With Domain Experts LARS: AN EFFICIENT AND SCALABLE LOCATION-AWARE RECOMMENDER SYSTEM ABSTRACT LARS, a location-aware recommender system that uses location-based ratings to produce recommendations is proposed. 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. LARS is efficient, scalable, and capable of producing recommendations twice as accurate compared to existing recommendation approaches.
- 2. LeMeniz Infotech 36, 100 feet Road, Natesan Nagar(Near Indira Gandhi Statue, Next to Fish-O-Fish), Pondicherry-605 005 Call: 0413-4205444, +91 99625 88976, 95663 55386. For More Projects Titles Visits : www.lemenizinfotech.com | Call Us : 9962588976 /9566355386 Do Your Projects With Domain Experts AIM To propose LARS, a location-aware recommender system that uses location-based ratings to produce recommendations for user query. INTRODUCTION 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. Currently, myriad applications can produce location-based ratings that embed user and/or item locations. For example, location-based social networks 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.
- 3. LeMeniz Infotech 36, 100 feet Road, Natesan Nagar(Near Indira Gandhi Statue, Next to Fish-O-Fish), Pondicherry-605 005 Call: 0413-4205444, +91 99625 88976, 95663 55386. For More Projects Titles Visits : www.lemenizinfotech.com | Call Us : 9962588976 /9566355386 Do Your Projects With Domain Experts EXISTING SYSTEM Existing recommendation techniques assume ratings are represented by the (user, rating, item) triple, thus are ill-equipped to produce location-aware recommendations. Location-based services Current location-based services employ two main methods to provide interesting destinations to users. (1) KNN techniques and variants (e.g., aggregate KNN) simply retrieve the k objects nearest to a user and are completely removed from any notion of user personalization. (2) Preference methods such as skylines and location-based top-k methods require users to express explicit preference constraints. Traditional recommenders A wide array of techniques are capable of producing recommendations using non-spatial ratings for non-spatial items represented as the triple (user, rating, item). The closest these approaches come to considering location is by incorporating contextual attributes into statistical recommendation models. However, these are not personalized to each user; rather, this list is built using aggregate rental data for a particular city.
- 4. LeMeniz Infotech 36, 100 feet Road, Natesan Nagar(Near Indira Gandhi Statue, Next to Fish-O-Fish), Pondicherry-605 005 Call: 0413-4205444, +91 99625 88976, 95663 55386. For More Projects Titles Visits : www.lemenizinfotech.com | Call Us : 9962588976 /9566355386 Do Your Projects With Domain Experts Location-aware recommenders The CityVoyager system mines a user’s personal GPS trajectory data to determine her preferred shopping sites, and provides recommendation based on where the system predicts the user is likely to go in the future. The spatial activity recommendation system mines GPS trajectory data with embedded user-provided tags in order to detect interesting activities located in a city. It uses this data to answer two query types: (a) given an activity type, return where in the city this activity is happening, and (b) given an explicit spatial region, provide the activities available in this region. Geo-measured friend-based collaborative filtering produces recommendations by using only ratings that are from a querying user’s social-network friends that live in the same city. This technique only addresses user location embedded in ratings. Disadvantages Does not personalize answers to the querying user No traditional approach has studied explicit location-based ratings.
- 5. LeMeniz Infotech 36, 100 feet Road, Natesan Nagar(Near Indira Gandhi Statue, Next to Fish-O-Fish), Pondicherry-605 005 Call: 0413-4205444, +91 99625 88976, 95663 55386. For More Projects Titles Visits : www.lemenizinfotech.com | Call Us : 9962588976 /9566355386 Do Your Projects With Domain Experts PROPOSED SYSTEM LARS, a novel location-aware recommender system built specifically to produce high-quality location-based recommendations in an efficient manner. LARS produces recommendations using a taxonomy of three types of location-based ratings within a single framework 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 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 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. Advantages Helps users discover new and interesting items LARS, produces personalized recommendations influenced by location-based ratings and a querying user location.
- 6. LeMeniz Infotech 36, 100 feet Road, Natesan Nagar(Near Indira Gandhi Statue, Next to Fish-O-Fish), Pondicherry-605 005 Call: 0413-4205444, +91 99625 88976, 95663 55386. For More Projects Titles Visits : www.lemenizinfotech.com | Call Us : 9962588976 /9566355386 Do Your Projects With Domain Experts LITERATURE REVIEW Location-based services Current location-based services employ two main methods to provide interesting destinations to users. (1) KNN techniques and variants (e.g., aggregate KNN) simply retrieve the k objects nearest to a user and are completely removed from any notion of user personalization. (2) Preference methods such as skylines and location-based top-k methods require users to express explicit preference constraints. Recent research has proposed the problem of hyper-local place ranking. Given a user location and query string, hyper-local ranking provides a list of top-k points of interest influenced by previously logged directional queries. Hyper-local ranking does not personalize answers to the querying user, i.e., two users issuing the same search term from the same location will receive exactly the same ranked answer set. Traditional recommenders A wide array of techniques are capable of producing recommendations using non-spatial ratings for non-spatial items represented as the triple (user, rating, item). The closest these approaches come to considering location is by incorporating contextual attributes into statistical recommendation models. Some existing commercial applications make cursory use of location when proposing interesting
- 7. LeMeniz Infotech 36, 100 feet Road, Natesan Nagar(Near Indira Gandhi Statue, Next to Fish-O-Fish), Pondicherry-605 005 Call: 0413-4205444, +91 99625 88976, 95663 55386. For More Projects Titles Visits : www.lemenizinfotech.com | Call Us : 9962588976 /9566355386 Do Your Projects With Domain Experts items to users. For instance, Netflix displays a “local favorites” list containing popular movies for a user’s given city. However, these movies are not personalized to each user (e.g., using recommendation techniques); rather, this list is built using aggregate rental data for a particular city. Location-aware recommenders The CityVoyager system mines a user’s personal GPS trajectory data to determine her preferred shopping sites, and provides recommendation based on where the system predicts the user is likely to go in the future. The spatial activity recommendation system mines GPS trajectory data with embedded user-provided tags in order to detect interesting activities located in a city. It uses this data to answer two query types: (a) given an activity type, return where in the city this activity is happening, and (b) given an explicit spatial region, provide the activities available in this region. Geo-measured friend-based collaborative filtering produces recommendations by using only ratings that are from a querying user’s social-network friends that live in the same city. This technique only addresses user location embedded in ratings.
- 8. LeMeniz Infotech 36, 100 feet Road, Natesan Nagar(Near Indira Gandhi Statue, Next to Fish-O-Fish), Pondicherry-605 005 Call: 0413-4205444, +91 99625 88976, 95663 55386. For More Projects Titles Visits : www.lemenizinfotech.com | Call Us : 9962588976 /9566355386 Do Your Projects With Domain Experts Hardware requirements: Processor : Any Processor above 500 MHz. Ram : 128Mb. Hard Disk : 10 Gb. Compact Disk : 650 Mb. Input device : Standard Keyboard and Mouse. Output device : VGA and High Resolution Monitor. Software requirements: Operating System : Windows Family. Language : JDK 1.5 Database : MySQL 5.0 Tool : HeidiSQL 3.0