JPD1414 LARS*: An Efficient and Scalable Location-Aware Recommender Systemchennaijp
We have best 2014 free dot not projects topics are available along with all document, you can easy to find out number of documents for various projects titles.
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IEEE 2014 DOTNET DATA MINING PROJECTS Lars an-efficient-and-scalable-location...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
ReMashed presentation at Community Building Cluster at CELSTEC, Heerlen, NLHendrik Drachsler
ReMashed is a mash-up personal learning environment that provides recommendations for Web 2.0 services using collaborative filtering. It matches users with similar interests across sites like Delicious, Flickr, blogs, SlideShare, Twitter and YouTube. Users register, enter their favorite services and rate recommendations. The system is being developed to evaluate recommendation algorithms and create data sets for recommender systems in informal learning networks. Future work includes additional recommendation techniques, dataset creation, and integration into other personal learning environments.
Lars an efficient and scalable location aware recommender systemLeMeniz Infotech
Lars an efficient and scalable location aware recommender system
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
JPD1414 LARS*: An Efficient and Scalable Location-Aware Recommender Systemchennaijp
We have best 2014 free dot not projects topics are available along with all document, you can easy to find out number of documents for various projects titles.
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/dot-net-projects/
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
IEEE 2014 DOTNET DATA MINING PROJECTS Lars an-efficient-and-scalable-location...IEEEMEMTECHSTUDENTPROJECTS
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
ReMashed presentation at Community Building Cluster at CELSTEC, Heerlen, NLHendrik Drachsler
ReMashed is a mash-up personal learning environment that provides recommendations for Web 2.0 services using collaborative filtering. It matches users with similar interests across sites like Delicious, Flickr, blogs, SlideShare, Twitter and YouTube. Users register, enter their favorite services and rate recommendations. The system is being developed to evaluate recommendation algorithms and create data sets for recommender systems in informal learning networks. Future work includes additional recommendation techniques, dataset creation, and integration into other personal learning environments.
Lars an efficient and scalable location aware recommender systemLeMeniz Infotech
Lars an efficient and scalable location aware recommender system
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* is a location-aware recommender system that uses location-based ratings to produce personalized recommendations. It supports three novel classes of location-based ratings and exploits user and item locations to maximize recommendation quality and scalability. Experimental results show LARS* produces recommendations twice as accurate as existing approaches using real-world data from Foursquare and MovieLens.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
FIND MY VENUE: Content & Review Based Location Recommendation SystemIJTET Journal
Abstract—Recommender system is a software application agent that presents the culls, interest and predilections of individual persons/ users and makes recommendation accordingly. During the online search they provide more facile method for users to make decisions predicated on their recommendations. Collaborative filtering (CF) technique is utilized, which is predicated on past group community opinions for utilizer and item and correlates them to provide results to the utilizer queries. Here the LARS is a location cognizant recommender system to engender location recommendation by utilizing location predicated ratings within a single framework. The system suggests k items personalized for a querying utilizer u. For traditional system which could not fortify spatial properties of users, community opinion can be expressed through triple explicit ratings that are (utilizer, rating, item) which represents a utilizer providing numeric ratings for an item. LARS engenders recommendation through taxonomy of three types of location predicated ratings. Namely spatial ratings for non-spatial items, non-spatial ratings for spatial items, spatial ratings for spatial items. Through this LARS can apply with the Content & Review Predicated Location Recommendation System. Which gives a culled utilizer a group of venues or ads by giving thought to each personal interest and native predilection. This system deals with offline modeling and on-line recommendation. To get the instant results, a ascendable question process technique is developed by elongating each the edge rule with Threshold Algorithm.
A Novel Approach for Travel Package Recommendation Using Probabilistic Matrix...IJSRD
This document proposes a novel approach for travel package recommendation using probabilistic matrix factorization (PMF). It discusses how existing recommendation systems are usually classification-based and supervised, whereas the proposed approach uses an unsupervised E-TRAST (Efficient-Tourist Relation Area Season Topic) model. The E-TRAST model represents travel packages and tourists using different topics modeled through PMF. It analyzes travel data characteristics and introduces a cocktail approach considering features like seasonal tourist performance to recommend customized travel packages.
IRJET- Personalize Travel Recommandation based on Facebook DataIRJET Journal
The document summarizes a proposed system for personalized travel recommendation based on Facebook data. It begins by discussing existing challenges with cold start recommendations and existing recommendation techniques. It then proposes a new framework called Implicit-feedback based Content-aware Collaborative Filtering (ICCF) that incorporates semantic content from social networks to address cold start recommendations without negative sampling. Finally, it evaluates ICCF on a large location-based social network dataset and finds it outperforms existing baselines particularly for cold start scenarios by leveraging user profile information.
Evaluating and Enhancing Efficiency of Recommendation System using Big Data A...IRJET Journal
This document discusses evaluating and enhancing the efficiency of recommendation systems using big data analytics. It begins with an abstract that outlines recommendation systems, collaborative filtering, and the need for big data analytics due to large datasets. It then discusses specific collaborative filtering techniques like user-based, item-based, and matrix factorization. It describes challenges like scalability that big data analytics can help address. The document evaluates recommendation algorithms using metrics like MAE, RMSE, precision and time taken on movie recommendation datasets. It aims to design an efficient recommendation system using the best techniques.
Efficient Filtering Algorithms for Location- Aware Publish/subscribeIJSRD
Location-based services have been mostly used in many systems. preceding systems uses a pull model or user-initiated model, where a user arrival a query to a server which gives response with location-aware answers. To offer outcomes to users with fast responses, a push model or server-initiated model is flattering an important computing model in the next-generation location-based services. In the push model, subscribers arrive spatio-textual subscriptions to fastening their curiosities, and publishers send spatio-textual messages. It is used for a high-performance location-aware publish/subscribe system to send publishers’ messages to valid subscribers. In this paper, we find the exploration happenstances that start in manipulative a location-aware publish/subscribe system. We recommend an R-tree based index by merging textual descriptions into R-tree nodes. We design efficient filtering algorithms and effective pruning techniques to accomplish high performance. This method can support likewise conjunctive queries and ranking queries.
A LOCATION-BASED RECOMMENDER SYSTEM FRAMEWORK TO IMPROVE ACCURACY IN USERBASE...ijcsa
This document proposes a framework to improve the accuracy of recommendations in collaborative filtering recommender systems by considering users' locations. The framework enhances traditional collaborative filtering in several ways: 1) It increases the similarity score of users located in the same place as the active user; 2) It filters peers to remove non-related users; 3) It selects the top peers and recommends items based on those peers' ratings. The framework aims to provide more local recommendations by incorporating geographic location data throughout the recommendation process.
JPD1435 Preserving Location Privacy in Geosocial Applicationschennaijp
We have best 2014 free dot not projects topics are available along with all document, you can easy to find out number of documents for various projects titles.
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/dot-net-projects/
[Decisions2013@RecSys]The Role of Emotions in Context-aware RecommendationYONG ZHENG
The document discusses the role of emotions in context-aware recommender systems (CARS). It explores two classes of CARS algorithms: context-aware splitting approaches and differential context modeling. For context-aware splitting approaches, it examines which emotional contexts are most frequently used to split items or users. For differential context modeling, it analyzes which emotional dimensions are selected or weighted most highly for different algorithm components. The experimental results found that the emotions of end emotion and dominant emotion were the most influential across approaches. User splitting also generally outperformed item splitting.
This document summarizes a research paper that proposes a bi-objective recommendation framework (BORF) for venue recommendation on mobile social networks. The BORF uses multiple objective optimization techniques to generate personalized recommendations. It addresses issues like cold start and data sparsity by preprocessing data using a Hub-Average inference model. Both scalar optimization using a Weighted Sum Approach and vector optimization using a NSGA-II evolutionary algorithm are implemented to provide optimal venue recommendations to users. Experimental results on a large real-world dataset confirm the accuracy of the proposed recommendation system.
Service rating prediction by exploring social mobile users’ geographical loca...CloudTechnologies
Service rating prediction by exploring social mobile users’ geographical locations M-Tech IEEE 2017 Projects B-Tech Major Projects B-tech Main Projects Data mining Project
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
A Brief Survey on Recommendation System for a Gradient Classifier based Inade...Christo Ananth
Recommender systems are a common and successful feature of modern internet services. (RS). A service that connects users to tasks is known as a recommendation system. Making it simpler for customers and project providers to identify and receive projects and other solutions achieves this. A recommendation system is a strong device that may be advantageous to a business or organisation. This study explores whether recommender systems may be utilised to solve cold-start and data-sparsely issues with recommender systems, as well as delays and business productivity. Recommender systems make it easier and more convenient for people to get information. Over the years, several different methods have been created. We employ a potent predictive regression method known as the slope classifier algorithm, which minimises a loss function by repeatedly choosing a function that points in the direction of the weak hypothesis or the negative gradient. A group that is experiencing trouble handling cold beginnings and data sparsity will send enormous datasets to the suggested systems team. The users have to finish their job by the deadline in order to overcome these challenges.
Toward privacy preserving and collusion resistance in a location proof updati...JPINFOTECH JAYAPRAKASH
This document proposes APPLAUS, a privacy-preserving location proof updating system. Existing location-based systems lack secure mechanisms for users to prove their locations. APPLAUS uses Bluetooth-enabled devices to mutually generate location proofs that are sent anonymously to an untrusted server. It aims to effectively provide location proofs while preserving user privacy and detecting colluding attacks. The system architecture involves mobile devices generating proofs, an untrusted server verifying proofs, and an authorized verifier querying the server. Betweenness ranking and correlation clustering algorithms are used to detect outliers.
Ontological and clustering approach for content based recommendation systemsvikramadityajakkula
This document proposes a novel content-based recommendation system that uses ontological graphs and dynamic weighted ranking. It builds an adaptive ranking mechanism based on user selections and preferences to improve recommendation accuracy over time. The system segments data into ontological groups and identifies relationships between entities. It then calculates similarity between entities using feature vectors and ranks entities based on weights assigned to their connections in the ontological graph. These weights are updated dynamically based on user feedback to personalize recommendations for each user. The paper describes testing this approach in a recipe recommendation tool called RecipeMiner, which produced coherent recommendations that adapted to user preferences.
LARS* is a location-aware recommender system that uses location-based ratings to produce personalized recommendations. It supports three novel classes of location-based ratings and exploits user and item locations to maximize recommendation quality and scalability. Experimental results show LARS* produces recommendations twice as accurate as existing approaches using real-world data from Foursquare and MovieLens.
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09666155510, 09849539085 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
FIND MY VENUE: Content & Review Based Location Recommendation SystemIJTET Journal
Abstract—Recommender system is a software application agent that presents the culls, interest and predilections of individual persons/ users and makes recommendation accordingly. During the online search they provide more facile method for users to make decisions predicated on their recommendations. Collaborative filtering (CF) technique is utilized, which is predicated on past group community opinions for utilizer and item and correlates them to provide results to the utilizer queries. Here the LARS is a location cognizant recommender system to engender location recommendation by utilizing location predicated ratings within a single framework. The system suggests k items personalized for a querying utilizer u. For traditional system which could not fortify spatial properties of users, community opinion can be expressed through triple explicit ratings that are (utilizer, rating, item) which represents a utilizer providing numeric ratings for an item. LARS engenders recommendation through taxonomy of three types of location predicated ratings. Namely spatial ratings for non-spatial items, non-spatial ratings for spatial items, spatial ratings for spatial items. Through this LARS can apply with the Content & Review Predicated Location Recommendation System. Which gives a culled utilizer a group of venues or ads by giving thought to each personal interest and native predilection. This system deals with offline modeling and on-line recommendation. To get the instant results, a ascendable question process technique is developed by elongating each the edge rule with Threshold Algorithm.
A Novel Approach for Travel Package Recommendation Using Probabilistic Matrix...IJSRD
This document proposes a novel approach for travel package recommendation using probabilistic matrix factorization (PMF). It discusses how existing recommendation systems are usually classification-based and supervised, whereas the proposed approach uses an unsupervised E-TRAST (Efficient-Tourist Relation Area Season Topic) model. The E-TRAST model represents travel packages and tourists using different topics modeled through PMF. It analyzes travel data characteristics and introduces a cocktail approach considering features like seasonal tourist performance to recommend customized travel packages.
IRJET- Personalize Travel Recommandation based on Facebook DataIRJET Journal
The document summarizes a proposed system for personalized travel recommendation based on Facebook data. It begins by discussing existing challenges with cold start recommendations and existing recommendation techniques. It then proposes a new framework called Implicit-feedback based Content-aware Collaborative Filtering (ICCF) that incorporates semantic content from social networks to address cold start recommendations without negative sampling. Finally, it evaluates ICCF on a large location-based social network dataset and finds it outperforms existing baselines particularly for cold start scenarios by leveraging user profile information.
Evaluating and Enhancing Efficiency of Recommendation System using Big Data A...IRJET Journal
This document discusses evaluating and enhancing the efficiency of recommendation systems using big data analytics. It begins with an abstract that outlines recommendation systems, collaborative filtering, and the need for big data analytics due to large datasets. It then discusses specific collaborative filtering techniques like user-based, item-based, and matrix factorization. It describes challenges like scalability that big data analytics can help address. The document evaluates recommendation algorithms using metrics like MAE, RMSE, precision and time taken on movie recommendation datasets. It aims to design an efficient recommendation system using the best techniques.
Efficient Filtering Algorithms for Location- Aware Publish/subscribeIJSRD
Location-based services have been mostly used in many systems. preceding systems uses a pull model or user-initiated model, where a user arrival a query to a server which gives response with location-aware answers. To offer outcomes to users with fast responses, a push model or server-initiated model is flattering an important computing model in the next-generation location-based services. In the push model, subscribers arrive spatio-textual subscriptions to fastening their curiosities, and publishers send spatio-textual messages. It is used for a high-performance location-aware publish/subscribe system to send publishers’ messages to valid subscribers. In this paper, we find the exploration happenstances that start in manipulative a location-aware publish/subscribe system. We recommend an R-tree based index by merging textual descriptions into R-tree nodes. We design efficient filtering algorithms and effective pruning techniques to accomplish high performance. This method can support likewise conjunctive queries and ranking queries.
A LOCATION-BASED RECOMMENDER SYSTEM FRAMEWORK TO IMPROVE ACCURACY IN USERBASE...ijcsa
This document proposes a framework to improve the accuracy of recommendations in collaborative filtering recommender systems by considering users' locations. The framework enhances traditional collaborative filtering in several ways: 1) It increases the similarity score of users located in the same place as the active user; 2) It filters peers to remove non-related users; 3) It selects the top peers and recommends items based on those peers' ratings. The framework aims to provide more local recommendations by incorporating geographic location data throughout the recommendation process.
JPD1435 Preserving Location Privacy in Geosocial Applicationschennaijp
We have best 2014 free dot not projects topics are available along with all document, you can easy to find out number of documents for various projects titles.
For More Details:
http://jpinfotech.org/final-year-ieee-projects/2014-ieee-projects/dot-net-projects/
[Decisions2013@RecSys]The Role of Emotions in Context-aware RecommendationYONG ZHENG
The document discusses the role of emotions in context-aware recommender systems (CARS). It explores two classes of CARS algorithms: context-aware splitting approaches and differential context modeling. For context-aware splitting approaches, it examines which emotional contexts are most frequently used to split items or users. For differential context modeling, it analyzes which emotional dimensions are selected or weighted most highly for different algorithm components. The experimental results found that the emotions of end emotion and dominant emotion were the most influential across approaches. User splitting also generally outperformed item splitting.
This document summarizes a research paper that proposes a bi-objective recommendation framework (BORF) for venue recommendation on mobile social networks. The BORF uses multiple objective optimization techniques to generate personalized recommendations. It addresses issues like cold start and data sparsity by preprocessing data using a Hub-Average inference model. Both scalar optimization using a Weighted Sum Approach and vector optimization using a NSGA-II evolutionary algorithm are implemented to provide optimal venue recommendations to users. Experimental results on a large real-world dataset confirm the accuracy of the proposed recommendation system.
Service rating prediction by exploring social mobile users’ geographical loca...CloudTechnologies
Service rating prediction by exploring social mobile users’ geographical locations M-Tech IEEE 2017 Projects B-Tech Major Projects B-tech Main Projects Data mining Project
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.co¬m-Visit Our Website: www.finalyearprojects.org
To Get any Project for CSE, IT ECE, EEE Contact Me @ 09849539085, 09966235788 or mail us - ieeefinalsemprojects@gmail.com-Visit Our Website: www.finalyearprojects.org
A Brief Survey on Recommendation System for a Gradient Classifier based Inade...Christo Ananth
Recommender systems are a common and successful feature of modern internet services. (RS). A service that connects users to tasks is known as a recommendation system. Making it simpler for customers and project providers to identify and receive projects and other solutions achieves this. A recommendation system is a strong device that may be advantageous to a business or organisation. This study explores whether recommender systems may be utilised to solve cold-start and data-sparsely issues with recommender systems, as well as delays and business productivity. Recommender systems make it easier and more convenient for people to get information. Over the years, several different methods have been created. We employ a potent predictive regression method known as the slope classifier algorithm, which minimises a loss function by repeatedly choosing a function that points in the direction of the weak hypothesis or the negative gradient. A group that is experiencing trouble handling cold beginnings and data sparsity will send enormous datasets to the suggested systems team. The users have to finish their job by the deadline in order to overcome these challenges.
Toward privacy preserving and collusion resistance in a location proof updati...JPINFOTECH JAYAPRAKASH
This document proposes APPLAUS, a privacy-preserving location proof updating system. Existing location-based systems lack secure mechanisms for users to prove their locations. APPLAUS uses Bluetooth-enabled devices to mutually generate location proofs that are sent anonymously to an untrusted server. It aims to effectively provide location proofs while preserving user privacy and detecting colluding attacks. The system architecture involves mobile devices generating proofs, an untrusted server verifying proofs, and an authorized verifier querying the server. Betweenness ranking and correlation clustering algorithms are used to detect outliers.
Ontological and clustering approach for content based recommendation systemsvikramadityajakkula
This document proposes a novel content-based recommendation system that uses ontological graphs and dynamic weighted ranking. It builds an adaptive ranking mechanism based on user selections and preferences to improve recommendation accuracy over time. The system segments data into ontological groups and identifies relationships between entities. It then calculates similarity between entities using feature vectors and ranks entities based on weights assigned to their connections in the ontological graph. These weights are updated dynamically based on user feedback to personalize recommendations for each user. The paper describes testing this approach in a recipe recommendation tool called RecipeMiner, which produced coherent recommendations that adapted to user preferences.
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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