Poster - Recommender system
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The document describes a recommendation system that clusters Foursquare users based on their check-in histories to efficiently recommend new venues. The system first receives a user's data and category preferences, then groups the user into a cluster of similar users. It then compares the top venue choices of the most similar user to recommend the top 3 unvisited venues to the input user. The system was tested on check-in data from over 1,000 Foursquare users in New York City over 10 months.
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- Recommends people with similar interests, explaining which topics brings them together.
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Then, we present a case of how we've combined those techniques to build Smart Canvas (www.smartcanvas.com), a service that allows people to bring, create and curate content relevant to their organization, and also helps to tear down knowledge silos.
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- Associate tags to users’ profiles based on topics discovered from content they have contributed. These tags become searchable, allowing users to find experts or people with specific interests.
- Recommends people with similar interests, explaining which topics brings them together.
We give a deep dive into the design of our large-scale recommendation algorithms, giving special attention to our content-based approach that uses topic modeling techniques (like LDA and NMF) to discover people’s topics of interest from unstructured text, and social-based algorithms using a graph database connecting content, people and teams around topics.
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Similar to Poster - Recommender system
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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.
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PREDICTING VENUES IN LOCATION BASED SOCIAL NETWORK
1) The document analyzes check-in data from Foursquare to examine user behaviors and venue popularity across three cities.
2) Analysis of the dataset revealed different mobility habits, preferred locations, and location patterns between users.
3) This information about user behaviors and venue popularity can be used to build recommendation systems and predict future locations users may visit based on their check-in history and preferred categories.
Predicting Venues in Location Based Social Network
The circulation of the social networks and the evolution of the mobile phone devices has led to a
big usage of location based social networks application such as Foursquare, Twitter, Swarm
and Zomato on mobile phone devices mean that huge dataset which is containing a blend of
information about users behaviour’s, social society network of each users and also information
about each of venues, all these information available in mobile location recommendation
system .These datasets are much more different from those which is used in online recommender
systems, these datasets have more information and details about the users and the venues which
is allowing to have more clear result with much more higher accuracy of the analysing in the
result.
In this paper we examine the users behaviour’s and the popularity of the venue through a large
check-ins dataset from a location based social services, Foursquare: by using large scale
dataset containing both user check-in and location information .Our analysis expose across 3
different cities.On analysis of these dataset reveal a different mobility habits, preferring places
and also location patterns in the user personality. This information about the users behaviour’s
and each of the location popularity can be used to know the recommendation systems and to
predict the next move of the users depending on the categories that the users attend to visit and
according to the history of each users check-ins.
Lars an efficient and scalable location aware recommender system
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
K1803057782
This document summarizes an approach to improve personalized ranking using associated edge weights. The key points are:
1) Personalized ranking aims to improve traditional search and retrieval by tailoring results to a user's interests. Authority flow approaches like PageRank and ObjectRank can be used for personalized ranking on entity-relationship graphs.
2) Edge-based personalization assigns different weights to edge types based on the user, affecting authority flow. The paper focuses on improving edge-based personalization by hybridizing the ScaleRank algorithm with k-means clustering.
3) ScaleRank approximates the DataApprox algorithm for ranking. The hybrid approach uses k-means clustering on ScaleRank output to further group related nodes,
Collaborative Filtering Recommendation System
Collaborative filtering is a technique used in recommender systems to predict a user's preferences based on other similar users' preferences. It involves collecting ratings data from users, calculating similarities between users or items, and making recommendations. Common approaches include user-user collaborative filtering, item-item collaborative filtering, and probabilistic matrix factorization. Recommender systems are evaluated both offline using metrics like MAE and RMSE, and through online user testing.
IRJET - Venue Recommender for Events based on User Preferences
This document presents a venue recommendation system that provides recommendations for event hosts on Meetup to host their events. It proposes using deep learning techniques to compute descriptors for entities like venues, events, and groups, in order to calculate similarities between them. For single user recommendations, the system considers the user's interests and nearby event preferences. For group recommendations, it collects members' locations to find a central point and recommends venues close to all users. The system aims to address data sparsity and provide context-driven recommendations by utilizing additional information from other location-based social networks.
Recommender system and big data (design a smartphone recommender system based...
This document discusses the design of a hybrid smartphone recommender system based on collaborative and content-based filtering approaches using big data technologies. It begins with definitions of recommender systems and their common approaches. Then it explains how the system will apply a map-reduce algorithm using Hadoop: the map function will apply collaborative filtering to generate user-item pairs, and the reduce function will apply content-based filtering to calculate item scores and select top recommendations. Finally, the document proposes developing a web interface to demonstrate the hybrid recommender system and discusses how big data can help address challenges in recommender systems.
Service rating prediction by exploring social mobile users’ geographical loca...
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
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Enhanced User-driven Ranking System with Splay Tree
E-learning is one of the information and communication technology products used for teaching
and learning process [35]. An efficient and effective way to construct trust relationship among peer users in
e-learning environment is ranking. User-driven ranking systems are based only on the feedback or rating
provided by the users. In [46-48] the authors provide a variety of trust and reputation methods. Certified
Belief in Strength (CBS) [45] is a novel trust measurement method based on reputation and strength . In
[38] author presents a recommendation system based on the relevant feedback review to predict the
user's interests, that are ranked based on the recommendations history they provide previously. Users with
higher rating obtain high reputation compared to less scored users. In question answering websites like
StackOverflow, new or low scored users are ignored by the community. This discourage them and their
involvement with the community reduces further down, as power law states, alleged low users are pu shed
to the bottom of the ranking list. Avoid this condition by encouraging less reputed users and prevent them
from moving further down in ranking level. Thus, low reputed users are provided with few more chances to
participate actively in the e-learning environments. A splay tree is a Binary Search Tree with self-balancing
skill. The splay tree brings the recently accessed item to the top of the tree, thus active users are always
on the top of the tree. A splay tree is used to represent user's ranks, and to semi-splay low ranked users
again in the tree thus preventing them from further drowning in the ranking list. The focus of this research
work is to find and enhance low reputed users in reputation system by providing few more chances to take
part actively in the e-learning environment using the splay tree. Normalized discounted cumulative gain
(NDCG) acts as a decision part for identifying drowning users.
A Novel Approach for Travel Package Recommendation Using Probabilistic Matrix...
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.
Personalizing Forum Search using Multidimensional Random Walks
This document describes a method for improving forum search through personalization. It proposes using multidimensional random walks to compute user similarities based on multiple dimensions like co-participation, interactions, topics and profiles. The approach builds a multidimensional heterogeneous graph and executes random walks with weights based on egocentric relations. Key results show this method predicts similar users to answer future questions better than 6 baselines, and can enhance keyword search by re-ranking results based on contributor authority scores from multiple relations.
An Adaptive Framework for Enhancing Recommendation Using Hybrid Technique
Recommender systems provide useful recommendations to a collection of users for items or products that
might be of concern or interest to them. Several techniques have been proposed for recommendation such
as collaborative filtering, content-based, knowledge-based, and demographic filtering. Each of these
techniques suffers from scalability, data sparsity, and cold-start problems when applied individually
resulting in poor recommendations. This paper proposes an adaptive hybrid recommender system that
combines multiple techniques together to achieve some synergy between them. Collaborative filtering and
demographic techniques are combined in a weighted linear formula. Different experiments applied using
movieLen dataset confirm that the proposed adaptable hybrid framework outperforms the weaknesses
resulted when using traditional recommendation techniques.
History and Overview of the Recommender Systems.pdf
This chapter provides an overview of recommender systems, including their history and classification methods. Recommender systems aim to filter and recommend useful items to users based on their preferences and the preferences of similar users. The chapter discusses the five main categories of recommender systems: content-based, collaborative-based, knowledge-based, demographic-based, and hybrid systems. It also outlines the general requirements and processes for building recommender systems and provides examples of content-based recommendation techniques.
An Efficient Trust Evaluation using Fact-Finder Technique
Provide individualized suggestions
of data or products related to users’ needs
by Recommender systems (RSs). Even
if RSs have created substantial progresses
in theory and formula development and
have achieved many business successes, a
way to operate the wide accessible info in
online social Networks (OSNs) has been
mainly overlooked. Noticing such a gap in
the existing research in RSs and taking
into account a user’s choice being greatly
influenced by his/her trustworthy friends
and their opinions; this paper proposes a,
Fact Finder technique that improves the
prevailing recommendation approaches by
exploring a new source of data from
friends’ short posts in microbloggings as
micro-reviews.Degree of friends’
sentiment and level being sure to a user’s
choice are known by victimisation
machine learning strategies as well as
Naive Bayes, Logistic Regression and
Decision Trees. As the verification of the
proposed Fact finder, experiments
victimisation real social data from Twitter
microblogger area unit given and results
show the effectiveness and promising of
the planned approach.
JPD1414 LARS*: An Efficient and Scalable Location-Aware Recommender System
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/
A Recommendation Engine For Predicting Movie Ratings Using A Big Data Approach
The document describes a study that developed a movie recommendation system using collaborative filtering and the alternating least squares (ALS) model on Apache Spark. Key points:
- The system predicts the top movie ratings for users by analyzing a user's past movie searches and ratings to recommend similar movies.
- It uses a model-based approach with matrix factorization to learn latent features and solve problems like cold starts and sparsity.
- The study tests different ALS parameters and achieves a root mean squared error of 0.9761, predicting the top 1000 movie ratings with 97% accuracy in 517 seconds.
Similar to Poster - Recommender system (20)
FIND MY VENUE: Content & Review Based Location Recommendation System
FIND MY VENUE: Content & Review Based Location Recommendation System
FHCC: A SOFT HIERARCHICAL CLUSTERING APPROACH FOR COLLABORATIVE FILTERING REC...
FHCC: A SOFT HIERARCHICAL CLUSTERING APPROACH FOR COLLABORATIVE FILTERING REC...
The application of data mining to recommender systems
The application of data mining to recommender systems
PREDICTING VENUES IN LOCATION BASED SOCIAL NETWORK
PREDICTING VENUES IN LOCATION BASED SOCIAL NETWORK
Predicting Venues in Location Based Social Network
Predicting Venues in Location Based Social Network
Lars an efficient and scalable location aware recommender system
Lars an efficient and scalable location aware recommender system
IRJET - Venue Recommender for Events based on User Preferences
IRJET - Venue Recommender for Events based on User Preferences
Recommender system and big data (design a smartphone recommender system based...
Recommender system and big data (design a smartphone recommender system based...
Service rating prediction by exploring social mobile users’ geographical loca...
Service rating prediction by exploring social mobile users’ geographical loca...
International Journal of Engineering Research and Development (IJERD)
International Journal of Engineering Research and Development (IJERD)
Enhanced User-driven Ranking System with Splay Tree
Enhanced User-driven Ranking System with Splay Tree
A Novel Approach for Travel Package Recommendation Using Probabilistic Matrix...
A Novel Approach for Travel Package Recommendation Using Probabilistic Matrix...
Personalizing Forum Search using Multidimensional Random Walks
Personalizing Forum Search using Multidimensional Random Walks
An Adaptive Framework for Enhancing Recommendation Using Hybrid Technique
An Adaptive Framework for Enhancing Recommendation Using Hybrid Technique
History and Overview of the Recommender Systems.pdf
History and Overview of the Recommender Systems.pdf
An Efficient Trust Evaluation using Fact-Finder Technique
An Efficient Trust Evaluation using Fact-Finder Technique
JPD1414 LARS*: An Efficient and Scalable Location-Aware Recommender System
JPD1414 LARS*: An Efficient and Scalable Location-Aware Recommender System
A Recommendation Engine For Predicting Movie Ratings Using A Big Data Approach
A Recommendation Engine For Predicting Movie Ratings Using A Big Data Approach
Poster - Recommender system
- 1. FOURSQUARE AWARE Prashant Gupta Ricky Hopper Divya Jain Nikhil Nayak Sandhya Nayak Finding the optimal number of clusters (vs. sum of squares error) Recommendation systems are a prominent application of big data, used everywhere from targeted advertising to online dating. We clustered users to make these systems more efficient and give users easy access to new venues based on their individual interests. We first receive a user’s data and category preference, and aggregate their check-in counts by venue category. We then group the user into a cluster of similar users, reducing the user pool we must compare to. Finally, we compare the most similar user’s top venue choices against the input user, and recommend the top 3 unvisited venues with the highest score. Our dataset was a collection of 227,428 Foursquare check-ins from 1,083 users in New York City over 10 months. Our system successfully returns venue recommendations based on similar users’ preferences. In the future, this system can be extended by factoring in timestamp data to recommend trending locations, or finding new friends at suggested venues. PCA after running k-means w/ 6 centers We fit querying users into clusters using a decision tree generated during our clustering. Preprocessing: Using Foursquare’s API, we grouped check-ins to individual venues by category for easier comparisons, and scaled these values down to indicate relative frequency. We used cosine similarity to find similar users after considering a number of different metrics and optimizing system performance.