Movie recommendation system using collaborative filtering system Mauryasuraj98
The document describes a mini project on building a movie recommendation system. It includes an abstract that discusses different recommendation approaches like demographic, content-based, and collaborative filtering. It also outlines the problem statement, proposed solution, workflow, dataset description, algorithm details, GUI design, result analysis, and applications. The system uses a user-based collaborative filtering model to recommend movies to users based on their preferences and ratings of similar users. Evaluation shows it has good prediction performance.
Trust and Recommender System
This document discusses recommender systems and how trust models can be incorporated. It begins by outlining recommender systems and their applications. It then describes three trust models - MoleTrust, TidalTrust, and PageRank - and how they propagate trust through networks. The document concludes that introducing trust is effective in addressing issues with recommender systems like data sparsity and attacks by malicious users. Trust propagation must balance accuracy and coverage of recommendations.
This document discusses recommendation systems and provides examples of different types of recommendation approaches. It introduces collaborative filtering and content-based filtering as the main recommendation techniques. For collaborative filtering, it provides an example of item-based collaborative filtering using the R programming language on a Last.fm music dataset. Content-based filtering recommends items based on their properties and features. Hybrid systems combine collaborative and content-based filtering to generate recommendations.
Collaborative filtering is a technique used by recommender systems to predict items users may like based on opinions of similar users. K-nearest neighbors (KNN) is a collaborative filtering algorithm that finds the k most similar users and bases predictions on the ratings of those neighbors. The document describes KNN collaborative filtering, including finding neighbor similarity, making predictions, and evaluating error rates on a movie recommendation system using the MovieLens dataset.
This document discusses the detection of profile injection attacks in recommender systems. It evaluates six machine learning models - decision tree, random forest, Ada boost, SVM, linear regression, and neural network - to distinguish authentic from attack profiles based on attributes of ratings data. The top three performing models of neural network, SVM, and random forest are ensembled using voting to create a proposed ensemble model. Experimental results on the MovieLens 100K dataset show the ensemble model achieves over 90% accuracy in most cases for detecting attack profiles, outperforming individual models.
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.
Movie recommendation system using collaborative filtering system Mauryasuraj98
The document describes a mini project on building a movie recommendation system. It includes an abstract that discusses different recommendation approaches like demographic, content-based, and collaborative filtering. It also outlines the problem statement, proposed solution, workflow, dataset description, algorithm details, GUI design, result analysis, and applications. The system uses a user-based collaborative filtering model to recommend movies to users based on their preferences and ratings of similar users. Evaluation shows it has good prediction performance.
Trust and Recommender System
This document discusses recommender systems and how trust models can be incorporated. It begins by outlining recommender systems and their applications. It then describes three trust models - MoleTrust, TidalTrust, and PageRank - and how they propagate trust through networks. The document concludes that introducing trust is effective in addressing issues with recommender systems like data sparsity and attacks by malicious users. Trust propagation must balance accuracy and coverage of recommendations.
This document discusses recommendation systems and provides examples of different types of recommendation approaches. It introduces collaborative filtering and content-based filtering as the main recommendation techniques. For collaborative filtering, it provides an example of item-based collaborative filtering using the R programming language on a Last.fm music dataset. Content-based filtering recommends items based on their properties and features. Hybrid systems combine collaborative and content-based filtering to generate recommendations.
Collaborative filtering is a technique used by recommender systems to predict items users may like based on opinions of similar users. K-nearest neighbors (KNN) is a collaborative filtering algorithm that finds the k most similar users and bases predictions on the ratings of those neighbors. The document describes KNN collaborative filtering, including finding neighbor similarity, making predictions, and evaluating error rates on a movie recommendation system using the MovieLens dataset.
This document discusses the detection of profile injection attacks in recommender systems. It evaluates six machine learning models - decision tree, random forest, Ada boost, SVM, linear regression, and neural network - to distinguish authentic from attack profiles based on attributes of ratings data. The top three performing models of neural network, SVM, and random forest are ensembled using voting to create a proposed ensemble model. Experimental results on the MovieLens 100K dataset show the ensemble model achieves over 90% accuracy in most cases for detecting attack profiles, outperforming individual models.
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.
A REVIEW PAPER ON BFO AND PSO BASED MOVIE RECOMMENDATION SYSTEM | J4RV4I1015Journal For Research
Recommendation system plays important role in Internet world and used in many applications. It has created the collection of many application, created global village and growth for numerous information. This paper represents the overview of Approaches and techniques generated in recommendation system. Recommendation system is categorized in three classes: Collaborative Filtering, Content based and hybrid based Approach. This paper classifies collaborative filtering in two types: Memory based and Model based Recommendation .The paper elaborates these approaches and their techniques with their limitations. The result of our system provides much better recommendations to users because it enables the users to understand the relation between their emotional states and the recommended movies.
The document discusses recommender systems and describes several techniques used in collaborative filtering recommender systems including k-nearest neighbors (kNN), singular value decomposition (SVD), and similarity weights optimization (SWO). It provides examples of how these techniques work and compares kNN to SWO. The document aims to explain state-of-the-art recommender system methods.
This document discusses recommendation techniques. It begins by outlining researchers' current troubles with finding and connecting relevant information in a timely manner. It then introduces recommendation techniques as having the potential to greatly influence all aspects of life by addressing these problems. The document defines recommendation techniques as systems that predict items a user may be interested in based on their preferences and activities. It categorizes techniques based on the data sources used, such as user demographics, item attributes, user ratings, and knowledge about users and items. Different recommendation approaches are described, including non-personalized, content-based, collaborative filtering, and knowledge-based techniques. The document concludes by thanking the audience and inviting them to learn more in future classes.
This document summarizes key considerations for evaluating collaborative filtering recommender systems. It discusses the user tasks being evaluated, types of analysis and datasets used, ways to measure prediction quality and other attributes, and how to evaluate the overall system from the user perspective. It presents empirical results showing that different accuracy metrics on one dataset collapsed into three groups that were either strongly or uncorrelated. The document aims to help researchers and practitioners properly evaluate and compare recommender system algorithms.
These slides present Movie Recommender, a system which provides movie recommendations based on the information known about the users. These recommendations are done using the analysis of the users' psychological profile, their watching history and the movies scores from other websites. They are based on aggregate similarity calculation. The system uses both collaborative filtering and content filtering (using an approach based on different features of the movies from the database). Although there are similar applications available, they tend to ignore the data specific to the user, which in our opinion is essential for his/her behavior
Personalized recommendation for cold start usersIRJET Journal
The document discusses personalized recommendation methods for cold start users. It describes several recommendation techniques including content-based filtering, collaborative filtering, and hybrid recommendation. It also discusses challenges like cold start problems and data sparsity. Trust-based recommendation systems are described that incorporate social relationships between users. Matrix factorization techniques are discussed for modeling user-item interactions and incorporating additional contextual factors. The use of probabilistic matrix factorization models to address cold start and sparsity problems is also covered.
This document discusses recommender systems, including:
1. It provides an overview of recommender systems, their history, and common problems like top-N recommendation and rating prediction.
2. It then discusses what makes a good recommender system, including experiment methods like offline, user surveys, and online experiments, as well as evaluation metrics like prediction accuracy, diversity, novelty, and user satisfaction.
3. Key metrics that are important to evaluate recommender systems are discussed, such as user satisfaction, prediction accuracy, coverage, diversity, novelty, serendipity, trust, robustness, and response time. The document emphasizes selecting metrics based on business goals.
[ADMA 2017] Identification of Grey Sheep Users By Histogram Intersection In R...YONG ZHENG
The document proposes a new approach to identify "grey sheep users" in recommender systems. Grey sheep users have unusual tastes and low correlations with other users. The approach represents each user as a histogram of their similarities to other users. It then uses outlier detection on the histograms to identify grey sheep users as the outliers with low similarities. The approach is tested on movie rating data and is shown to better identify grey sheep users compared to other methods. Future work involves applying this approach to other datasets and improving recommendations for identified grey sheep users.
Improving Social Recommendations by applying a Personalized Item Clustering P...Γιώργος Αλεξανδρίδης
This document presents a personalized item clustering approach for social recommendations. It aims to improve recommendation accuracy, novelty, and diversity. The approach clusters items based on a user's social network consumption patterns. It then constructs item networks and performs random walks to select recommendation items from each cluster. An evaluation on an Epinions dataset found the approach outperformed baseline and traditional recommender systems in novelty and diversity while achieving comparable accuracy.
This document presents a tag recommendation model for collaborative bookmarking systems. The team proposes using Lucene indexing and clustering approaches to suggest the most relevant tags for a given URL and its description. They describe extracting tags from the URL, user's previous tags, description text, and related words to then rank and recommend tags using a weighted clustering approach. The proposed architecture crawls URLs to extract content, indexes it with Lucene, and identifies candidate tags from multiple sources before applying clustering and weighting to select the most relevant tags.
This document provides an overview of a book recommendation system project. It introduces the problem of recommending books to users and discusses existing recommendation approaches like collaborative and content-based filtering. It then outlines the design of the system, which will use both user-based and item-based collaborative filtering techniques. It describes how these techniques work by calculating item and user similarities, identifying nearest neighbors, and making predictions. Finally, it discusses how the system will be evaluated using metrics like mean absolute error and root mean squared error.
IMPROVING COLLABORATIVE RECOMMENDATION VIA USER-ITEM SUBGROUPSNexgen Technology
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
GTC 2021: Counterfactual Learning to Rank in E-commerceGrubhubTech
Many ecommerce companies have extensive logs of user behavior such as clicks and conversions. However, if supervised learning is naively applied, then systems can suffer from poor performance due to bias and feedback loops. Using techniques from counterfactual learning we can leverage log data in a principled manner in order to model user behaviour and build personalized recommender systems. At Grubhub, a user journey begins with recommendations and the vast majority of conversions are powered by recommendations. Our recommender policies can drive user behavior to increase orders and/or profit. Accordingly, the ability to rapidly iterate and experiment is very important. Because of our powerful GPU workflows, we can iterate 200% more rapidly than with counterpart CPU workflows. Developers iterate ideas with notebooks powered by GPUs. Hyperparameter spaces are explored up to 8x faster with multi-GPUs Ray clusters. Solutions are shipped from notebooks to production in half the time with nbdev. With our accelerated DS workflows and Deep Learning on GPUs, we were able to deliver a +12.6% conversion boost in just a few months. In this talk we hope to present modern techniques for industrial recommender systems powered by GPU workflows. First a small background on counterfactual learning techniques, then followed by practical information and data from our industrial application.
By Alex Egg, accepted to Nvidia GTC 2021 Conference
This document provides an overview of recommender systems for e-commerce. It discusses various recommender approaches including collaborative filtering algorithms like nearest neighbor methods, item-based collaborative filtering, and matrix factorization. It also covers content-based recommendation, classification techniques, addressing challenges like data sparsity and scalability, and hybrid recommendation approaches.
[RecSys '13]Pairwise Learning: Experiments with Community Recommendation on L...Amit Sharma
1) The document proposes pairwise learning models for community recommendations on LinkedIn that learn preferences between communities rather than individual recommendations.
2) Three pairwise models are introduced - a feature difference model, logistic loss model, and pairwise PLSI latent preference model.
3) Evaluation on LinkedIn data shows the pairwise PLSI model improves performance on learning pairwise preferences and leads to more successful recommendations compared to baseline models. Online testing also showed click-through-rate increases of 3-5% for the pairwise models over baseline methods.
CSTalks - Real movie recommendation - 9 Marcstalks
This document proposes a new approach to movie recommendation that considers temporal dynamics and local user ratings. The current best approach is collaborative filtering with temporal dynamics, but this new approach clusters users based on their individual monitoring and behavior over time. It also clusters movies based on their global and dynamic class ratings. The model would monitor users, user-user patterns, user-movie patterns, and movie-movie patterns over time to update recommendations and predictions. This is aimed to provide more accurate recommendations by considering how user preferences can change over time.
ACM ICTIR 2019 Slides - Santa Clara, USAIadh Ounis
This document proposes a novel weak supervision approach to unify explicit and implicit feedback for rating prediction and ranking recommendation tasks. It trains an explicit feedback model to annotate implicit feedback with predicted ratings. This allows training a new model on the annotated data, improving ranking accuracy while increasing coverage of long-tail items compared to baselines. Evaluation on multiple datasets shows the approach enhances recommendation for both rating prediction and ranking, with less popularity bias than models using only explicit or implicit feedback.
The document proposes developing a recommender system using a movie lens dataset. It discusses using a collaborative filtering approach that divides users into virtual users based on product categories rated. This category-based collaborative filtering is intended to improve the performance and efficiency of calculating nearest neighbors compared to traditional collaborative filtering. Key phases include categorizing products, dividing user ratings, generating virtual users, analyzing virtual users, finding nearest neighbors, and generating recommendations by combining results for virtual users. The proposed system aims to more efficiently provide personalized recommendations to users.
Online BookStore Recommender Systems Using Collaborative Filtering AlgorithmBinay Sharma
Recommender systems are the software tools that make valuable recommendations to users by considering their profiles, preferences during interaction usually with online applications or websites.
Please kindly contact me at Sharma_binay9@hotmail.com for any questions. Thank you.
A REVIEW PAPER ON BFO AND PSO BASED MOVIE RECOMMENDATION SYSTEM | J4RV4I1015Journal For Research
Recommendation system plays important role in Internet world and used in many applications. It has created the collection of many application, created global village and growth for numerous information. This paper represents the overview of Approaches and techniques generated in recommendation system. Recommendation system is categorized in three classes: Collaborative Filtering, Content based and hybrid based Approach. This paper classifies collaborative filtering in two types: Memory based and Model based Recommendation .The paper elaborates these approaches and their techniques with their limitations. The result of our system provides much better recommendations to users because it enables the users to understand the relation between their emotional states and the recommended movies.
The document discusses recommender systems and describes several techniques used in collaborative filtering recommender systems including k-nearest neighbors (kNN), singular value decomposition (SVD), and similarity weights optimization (SWO). It provides examples of how these techniques work and compares kNN to SWO. The document aims to explain state-of-the-art recommender system methods.
This document discusses recommendation techniques. It begins by outlining researchers' current troubles with finding and connecting relevant information in a timely manner. It then introduces recommendation techniques as having the potential to greatly influence all aspects of life by addressing these problems. The document defines recommendation techniques as systems that predict items a user may be interested in based on their preferences and activities. It categorizes techniques based on the data sources used, such as user demographics, item attributes, user ratings, and knowledge about users and items. Different recommendation approaches are described, including non-personalized, content-based, collaborative filtering, and knowledge-based techniques. The document concludes by thanking the audience and inviting them to learn more in future classes.
This document summarizes key considerations for evaluating collaborative filtering recommender systems. It discusses the user tasks being evaluated, types of analysis and datasets used, ways to measure prediction quality and other attributes, and how to evaluate the overall system from the user perspective. It presents empirical results showing that different accuracy metrics on one dataset collapsed into three groups that were either strongly or uncorrelated. The document aims to help researchers and practitioners properly evaluate and compare recommender system algorithms.
These slides present Movie Recommender, a system which provides movie recommendations based on the information known about the users. These recommendations are done using the analysis of the users' psychological profile, their watching history and the movies scores from other websites. They are based on aggregate similarity calculation. The system uses both collaborative filtering and content filtering (using an approach based on different features of the movies from the database). Although there are similar applications available, they tend to ignore the data specific to the user, which in our opinion is essential for his/her behavior
Personalized recommendation for cold start usersIRJET Journal
The document discusses personalized recommendation methods for cold start users. It describes several recommendation techniques including content-based filtering, collaborative filtering, and hybrid recommendation. It also discusses challenges like cold start problems and data sparsity. Trust-based recommendation systems are described that incorporate social relationships between users. Matrix factorization techniques are discussed for modeling user-item interactions and incorporating additional contextual factors. The use of probabilistic matrix factorization models to address cold start and sparsity problems is also covered.
This document discusses recommender systems, including:
1. It provides an overview of recommender systems, their history, and common problems like top-N recommendation and rating prediction.
2. It then discusses what makes a good recommender system, including experiment methods like offline, user surveys, and online experiments, as well as evaluation metrics like prediction accuracy, diversity, novelty, and user satisfaction.
3. Key metrics that are important to evaluate recommender systems are discussed, such as user satisfaction, prediction accuracy, coverage, diversity, novelty, serendipity, trust, robustness, and response time. The document emphasizes selecting metrics based on business goals.
[ADMA 2017] Identification of Grey Sheep Users By Histogram Intersection In R...YONG ZHENG
The document proposes a new approach to identify "grey sheep users" in recommender systems. Grey sheep users have unusual tastes and low correlations with other users. The approach represents each user as a histogram of their similarities to other users. It then uses outlier detection on the histograms to identify grey sheep users as the outliers with low similarities. The approach is tested on movie rating data and is shown to better identify grey sheep users compared to other methods. Future work involves applying this approach to other datasets and improving recommendations for identified grey sheep users.
Improving Social Recommendations by applying a Personalized Item Clustering P...Γιώργος Αλεξανδρίδης
This document presents a personalized item clustering approach for social recommendations. It aims to improve recommendation accuracy, novelty, and diversity. The approach clusters items based on a user's social network consumption patterns. It then constructs item networks and performs random walks to select recommendation items from each cluster. An evaluation on an Epinions dataset found the approach outperformed baseline and traditional recommender systems in novelty and diversity while achieving comparable accuracy.
This document presents a tag recommendation model for collaborative bookmarking systems. The team proposes using Lucene indexing and clustering approaches to suggest the most relevant tags for a given URL and its description. They describe extracting tags from the URL, user's previous tags, description text, and related words to then rank and recommend tags using a weighted clustering approach. The proposed architecture crawls URLs to extract content, indexes it with Lucene, and identifies candidate tags from multiple sources before applying clustering and weighting to select the most relevant tags.
This document provides an overview of a book recommendation system project. It introduces the problem of recommending books to users and discusses existing recommendation approaches like collaborative and content-based filtering. It then outlines the design of the system, which will use both user-based and item-based collaborative filtering techniques. It describes how these techniques work by calculating item and user similarities, identifying nearest neighbors, and making predictions. Finally, it discusses how the system will be evaluated using metrics like mean absolute error and root mean squared error.
IMPROVING COLLABORATIVE RECOMMENDATION VIA USER-ITEM SUBGROUPSNexgen Technology
TO GET THIS PROJECT COMPLETE SOURCE ON SUPPORT WITH EXECUTION PLEASE CALL BELOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM,WWW.FINALYEAR-IEEEPROJECTS.COM, EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
GTC 2021: Counterfactual Learning to Rank in E-commerceGrubhubTech
Many ecommerce companies have extensive logs of user behavior such as clicks and conversions. However, if supervised learning is naively applied, then systems can suffer from poor performance due to bias and feedback loops. Using techniques from counterfactual learning we can leverage log data in a principled manner in order to model user behaviour and build personalized recommender systems. At Grubhub, a user journey begins with recommendations and the vast majority of conversions are powered by recommendations. Our recommender policies can drive user behavior to increase orders and/or profit. Accordingly, the ability to rapidly iterate and experiment is very important. Because of our powerful GPU workflows, we can iterate 200% more rapidly than with counterpart CPU workflows. Developers iterate ideas with notebooks powered by GPUs. Hyperparameter spaces are explored up to 8x faster with multi-GPUs Ray clusters. Solutions are shipped from notebooks to production in half the time with nbdev. With our accelerated DS workflows and Deep Learning on GPUs, we were able to deliver a +12.6% conversion boost in just a few months. In this talk we hope to present modern techniques for industrial recommender systems powered by GPU workflows. First a small background on counterfactual learning techniques, then followed by practical information and data from our industrial application.
By Alex Egg, accepted to Nvidia GTC 2021 Conference
This document provides an overview of recommender systems for e-commerce. It discusses various recommender approaches including collaborative filtering algorithms like nearest neighbor methods, item-based collaborative filtering, and matrix factorization. It also covers content-based recommendation, classification techniques, addressing challenges like data sparsity and scalability, and hybrid recommendation approaches.
[RecSys '13]Pairwise Learning: Experiments with Community Recommendation on L...Amit Sharma
1) The document proposes pairwise learning models for community recommendations on LinkedIn that learn preferences between communities rather than individual recommendations.
2) Three pairwise models are introduced - a feature difference model, logistic loss model, and pairwise PLSI latent preference model.
3) Evaluation on LinkedIn data shows the pairwise PLSI model improves performance on learning pairwise preferences and leads to more successful recommendations compared to baseline models. Online testing also showed click-through-rate increases of 3-5% for the pairwise models over baseline methods.
CSTalks - Real movie recommendation - 9 Marcstalks
This document proposes a new approach to movie recommendation that considers temporal dynamics and local user ratings. The current best approach is collaborative filtering with temporal dynamics, but this new approach clusters users based on their individual monitoring and behavior over time. It also clusters movies based on their global and dynamic class ratings. The model would monitor users, user-user patterns, user-movie patterns, and movie-movie patterns over time to update recommendations and predictions. This is aimed to provide more accurate recommendations by considering how user preferences can change over time.
ACM ICTIR 2019 Slides - Santa Clara, USAIadh Ounis
This document proposes a novel weak supervision approach to unify explicit and implicit feedback for rating prediction and ranking recommendation tasks. It trains an explicit feedback model to annotate implicit feedback with predicted ratings. This allows training a new model on the annotated data, improving ranking accuracy while increasing coverage of long-tail items compared to baselines. Evaluation on multiple datasets shows the approach enhances recommendation for both rating prediction and ranking, with less popularity bias than models using only explicit or implicit feedback.
The document proposes developing a recommender system using a movie lens dataset. It discusses using a collaborative filtering approach that divides users into virtual users based on product categories rated. This category-based collaborative filtering is intended to improve the performance and efficiency of calculating nearest neighbors compared to traditional collaborative filtering. Key phases include categorizing products, dividing user ratings, generating virtual users, analyzing virtual users, finding nearest neighbors, and generating recommendations by combining results for virtual users. The proposed system aims to more efficiently provide personalized recommendations to users.
Online BookStore Recommender Systems Using Collaborative Filtering AlgorithmBinay Sharma
Recommender systems are the software tools that make valuable recommendations to users by considering their profiles, preferences during interaction usually with online applications or websites.
Please kindly contact me at Sharma_binay9@hotmail.com for any questions. Thank you.
Recommendation systems, also known as recommendation engines, are a type of information system whose purpose is to suggest, or recommend items or actions to users.
The recommendations may consist of:
-> retail items (movies, books, etc.) or
-> actions, such as following other users in a social network.
It can be said that, Recommendation engines are nothing but an automated form of a “shop counter guy”. You ask him for a product. Not only he shows that product, but also the related ones which you could buy. They are well trained in cross selling and up selling. So, does our recommendation engines.
IRJET- Hybrid Book Recommendation SystemIRJET Journal
This document describes a hybrid book recommendation system that aims to overcome some common issues with recommendation systems like the cold start problem. The system collects demographic information from users during signup to provide more personalized recommendations. It uses both collaborative and content-based filtering approaches. For new users, it recommends books based on their interests. For users without ratings, it considers their purchase history. For users who provide ratings, it uses algorithms like KNN, SVD, RBM and hybrid approaches. The system aims to improve accuracy and provide a more personalized experience for users.
Recommender systems using collaborative filteringD Yogendra Rao
This document summarizes a student project on implementing recommender systems. The project objectives were to design a website using user-based, item-based, and model-based collaborative filtering as well as MapReduce to generate movie recommendations. The system was tested on the MovieLens dataset using MAE and RMSE metrics, with user-based filtering found to have the best performance. The document outlines the technical aspects of the recommendation system including the technologies used, website architecture, and references.
Social Recommender Systems Tutorial - WWW 2011idoguy
The document discusses social recommender systems and various approaches used in them. It covers fundamental recommendation techniques like collaborative filtering, content-based recommendation, and knowledge-based recommendation. It also discusses using tags, social relationships, and temporal data in recommendations. Evaluation of recommender systems and challenges are also summarized.
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.
The document provides an overview of recommender systems. It discusses the typical architecture of recommender systems and describes three main types: collaborative filtering systems, content-based systems, and knowledge-based systems. It also covers paradigms like collaborative filtering, content-based, knowledge-based, and hybrid recommender systems. The document then focuses on collaborative filtering techniques like user-based nearest neighbor collaborative filtering and item-based collaborative filtering. It also discusses latent factor models, matrix factorization approaches, and context-based recommender systems.
Movie Recommender System Using Artificial Intelligence Shrutika Oswal
In recent years, a huge amount of information is available on the internet and it is very difficult for the user to collect the relevant information. While purchasing any product also a lot of choices available and the user is confused about what to choose. This will be a time-consuming process as well. The search engine will solve this problem to some extent by but it will fail in giving a personalized recommendation. In this presentation, I will describe the different types and working of the recommender system how they gather the data, build recommender, generate recommendations from it, evaluate the performance and effectiveness of the recommender system. The further part of the presentation will describe how to build a movie recommender system using python.
Lecture Notes on Recommender System IntroductionPerumalPitchandi
This document provides an overview of recommender systems and the techniques used to build them. It discusses collaborative filtering, content-based filtering, knowledge-based recommendations, and hybrid approaches. For collaborative filtering, it describes user-based and item-based approaches, including measuring similarity, making predictions, and generating recommendations. It also discusses evaluation techniques and advanced topics like explanations.
Mixed Recommendation Algorithm Based on Content, Demographic and Collaborativ...IRJET Journal
The document describes a proposed hybrid recommendation algorithm that incorporates content filtering, collaborative filtering, and demographic filtering. It begins with an overview of recommendation systems and different filtering techniques. Then, it discusses related work incorporating various filtering approaches. The methodology section outlines the original algorithm, which develops user profiles based on browsing history and ratings. It provides recommendations by calculating similarities between user and item profiles. The proposed methodology enhances this by incorporating demographic attributes into user profiles and using fuzzy logic to validate recommendations. It claims this integrated approach can provide more accurate and personalized recommendations.
Alleviating cold-user start problem with users' social network data in recomm...Eduardo Castillejo Gil
This work explores the possibility of using relevant data from users’
social network to alleviate the cold-user problems in a recommender
system domain. The proposed solution extracts the most valuable
node in the graph generated by check in a venue with an Android
application using the Foursquare API. By obtaining the recommendations to this node we estimate the probability of some categories
to be similar to users tastes...
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.
This document presents a project that aims to detect fake online reviews using semi-supervised and supervised learning techniques. It discusses detecting fake reviews as the problem definition. The proposed system generates feature vectors from reviews for classification using algorithms like Naive Bayes. The system has modules for service providers and remote users. UML diagrams like use case diagrams, sequence diagrams and activity diagrams are presented to model the system. Testing strategies like unit testing, integration testing are discussed.
Recommender system and big data (design a smartphone recommender system based...Siwar Abidi
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.
This document summarizes several research papers on improving recommendation systems using item-based collaborative filtering approaches. It discusses challenges with traditional collaborative filtering like data sparsity and scalability. It then summarizes various item-based recommendation algorithms that analyze item relationships to indirectly compute recommendations. These include item-item similarity techniques like cosine similarity and adjusting for average ratings. The document also reviews literature on combining item categories and interestingness measures to improve accuracy. Overall, it analyzes different item-based collaborative filtering techniques to address challenges and provide high quality, personalized recommendations at scale.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
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5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
Power Grid Model is currently being applied in a wide variety of use cases, including grid planning, expansion, reliability, and congestion studies. It can also help in analyzing the impact of renewable energy integration, assessing the effects of disturbances or faults, and developing strategies for grid control and optimization.
What to expect
For the upcoming meetup we are organizing, we have an exciting lineup of activities planned:
-Insightful presentations covering two practical applications of the Power Grid Model.
-An update on the latest advancements in Power Grid -Model technology during the first and second quarters of 2024.
-An interactive brainstorming session to discuss and propose new feature requests.
-An opportunity to connect with fellow Power Grid Model enthusiasts and users.
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5. LOGORecommender Systems
Neighborhood-based Collaborative Filtering
Basic Steps
Assign a weight to all users with respect to similarity
with the active user.
Select k users that have the highest similarity with the
active user – (neighborhood)
9. LOGORecommender Systems
Item-based Collaborative Filtering
Proposed in 2003
DOES NOT match similar users
DOES match similar items
Leads to faster online systems
Results in improved recommendations
11. LOGORecommender Systems
More Extensions
Highly correlated neighbors based on very few
co-rated items
Significance Weighting
multiply the similarity weight by a significance
weighting factor
Default Voting
assume a default value for the rating for items that
have not been explicitly rated
Inverse User Frequency
Universally loved/hated items are bad
12. LOGORecommender Systems
Model-based Collaborative Filtering
Uses statistical models for predictions
Based on data mining and machine learning
algorithms
Latent factor and Matrix factorization models
have emerged as a state-of-the-art methodology
Netflix Prize competition
13. LOGORecommender Systems
Content-based Recommending
Pure collaborative filtering recommenders treat all
users and items as atomic units
Can make a better personalized recommendation
by knowing more about a user or an item
Demographic information
Movie genres
Literary genres
16. LOGORecommender Systems
Hybrid Approaches
Used to leverage the strengths of content-based
and collaborative recommenders.
Merging the list results to produce a final list.
Content-boosted collaborative filtering
17. LOGORecommender Systems
Evaluation Metrics
Evaluation matrix is used to measure the quality
of a recommender system.
These systems are typical measured using
predictive accuracy metrics
1. Mean Absolute Error (MAE)
2. Root Mean Squared Error (RMSE)
21. LOGORecommender Systems
Sparsity
User ratings matrix is typically very sparse
Effects collaborative filtering systems
The problem
system has a very high item- to user ratio.
The system is in the initial stages of use.
Solution - making assumptions about the data
generation process
22. LOGORecommender Systems
Cold-Start Problem
New items and new users pose a significant
challenge to recommender systems.
New item problem –
content-based approach to produce
recommendations for all items,
New user problem
selecting items to be rated by a user so as
to rapidly improve recommendation
performance with the least user feedback
23. LOGORecommender Systems
Fraud
Push attacks
Increase the rating of their own products
Nuke attacks
Lower the ratings of their competitors
Item-based collaborative filtering is more robust
to these attacks
Content based methods are unaffected by
profile injection attacks.
24. LOGORecommender Systems
Content based or Collaborative
filtering
Advantages of CF over CB
CF can perform in domains where there is not
much content associated with items
CF can also preform when content is difficult for
a computer to analyze.
CF system has the ability to provide
serendipitous recommendations.
Editor's Notes
In neighborhood-based CF, every user should be considered in finding neighborsWhen the number of users is small – neighborhood-based collaborative filtering works
When the number of users is large – computational complexity is highDifficult to find neighborsAlternative - Item-based Collaborative Filtering
Proposed in 2003 by Linden, Smith, and YorkDoes not match similar users as in neighborhood based CFMatch a user’s rated items to similar itemsResearches shows this leads to faster online systems and also results in improved recommendations
Pearson correlation is used to find the similarity between two items i and jU is the set of users who have rated both items i and jr(u,i) is the rating of user u on item ir‘(i) is the average rating of item I across all the usersThen the rating for item ‘i’ for user ‘u’ is predicted using weighted average.
It is common for the active user to have highly correlated neighbors that are based on very few co-rated (overlapping) items. These neighbors based on a small number of overlapping items tend to be bad predictors. One approach to tackle this problem is to multiply the similarity weight by a significance weighting factor, which devalues the correlations based on few co-rated items.Another approach is applying a default value to unrated itemsThen one can now compute correlation using the union of items rated by users being matched as opposed to the intersection.There may be items which are universally loved or hatedThey are bad for predictionsA value called inverse user frequency is calculated and the original CF rating is multiplied by this valueNeighborhood based methods that generate recommendations based on statistical notions of similarity between users, or between items
Uses statistical models for predictionslatent factor models assume that the similarity between users and items is simultaneously induced by some hidden lower dimensional structure in the dataFor an example, the rating that a user gives to a movie might be assumed to depend on few implicit factors such as the user’s taste across various movie genresThese statistical models are developed based on data mining and machine learning algorithmsCurrently the latent factor and matrix factorization models are widely usedIn 2009 a competition was held by Netflix – popular movie web site to design the best collaborative filtering algorithm to predict user ratings for films. the grand prize of US$1,000,000 was given to the team which bested Netflix's own algorithm for predicting ratings by 10.06%The final winning solution was a complex ensemble of different models, several enhancements to basic matrix factorization models.
So far discussed about collaborative filteringSecond type of recommender systems are content-based recommending.Pure CF techniques treats users and items as atomic units.They make predictions without regard to the specifics of individual users or items.But using underlying information about users or items, better predictions can be made.For examples demographic information about users – age group, gender, ethnicity, languages etc.Movie genres such as action, comedy, horror, drama, romance etc.
Assume that a particular user has liked Start Wars and Star TrekWhen the content of those movies were analyzed, we can find that the genre is sci-fi.Based on that we can recommend another sci-fi movie to the user such as Oblivion
Content base recommending is mainly focused on items with associated textual content such as web pages, books and movies.There are two approaches to tackle this problem.Recommendation problem is treated as an Information Retrieval task.User’s preferences are treated as a Query and the unrated documents are scored with relevance/similarity to this queryRecommendation problem is treated as a Classification task.Each example represents the content of an item, and a user’s past ratings are used as labels for these examples
In order to leverage the strengths of content-based and collaborative recommenders, people have come up with hybrid approaches which combine the two.simple approach is to allow both content-based and collaborative filtering methods to produce separate ranked lists of recommendations, and then merge their resultsto produce a one final list. To improve this combine the two predictions using an adaptive weighted average, where the weight of the collaborative component increases as thenumber of users accessing an item increasescontent-based predictions are applied to convert a sparse user ratings matrix into a full ratings matrix, and then a CF method is used to provide recommendations
quality of a recommender system can be evaluated by comparing recommendations to a test set of known user ratings. these systems are typicaly measured using predictive accuracy metrics where the predicted ratings are directly compared to actual user ratings.The most commonly used metric
The MAE measures the average magnitude of the errors in a set of forecasts, without considering their direction. It measures accuracy for continuous variables.
The RMSE is a quadratic scoring rule which measures the average magnitude of the errorExpressing the formula in words, the difference between forecast and corresponding observed values are each squared and then averaged over the sample. Finally, the square root of the average is taken. Since the errors are squared before they are averaged, the RMSE gives a relatively high weight to large errors. This means the RMSE is most useful when large errors are particularly undesirable.
New items and new userspose a signi+cant challenge to recommender systems.Collectively these problems are referred to as the coldstart problem
Stated simply, most users do not rate most items and, hence, the user ratings matrix is typically very sparse. this is a problem for collaborative filtering systems, since it decreases the probability of finding a set of users with similar ratings.This problem often occurs when a system has a very high itemuser ratio, or the system is in the initial stages of use.Solution to this using additional domain information about item. for example when a new movie is added to the system give additional making assumptions about the data generation process that allows for high-quality imputation
New items and new users pose a significant challenge to recommender systems. Collectively these problems are referred to as the cold start problem The first of these problems arises in collaborative filtering systems, where an item cannot be recommended unless some user hasrated it beforeSolution isSince content-based approaches do not rely on ratings from other users, they can be used to produce recommendations for all items, provided attributes ofthe items are available. Thenew-user problem is dificult to tackle, since without previous preferences of a user it is not possible to find similar users or to build a content-based profile.Solution to this is selecting items to be rated by a user so as to rapidly improve recommendation performance with the least user feedback.
As recommender systems are being increasingly adopted by commercial websites, they have started toplay a significant role in affecting the profitability of sellers. Thishas led to many vendors engaging in different forms of fraud. To increase the profits by cheating the recommendersystems for their benefitsIncrease the rating of their own productsLower the ratings of their competitors
Now let see which method is better. CF can perform in domains where there is not much content associated with itemsCF can also preform when content is difficult for a computer to analyze.CF system has the ability to provideserendipitous recommendations.