This document summarizes key aspects of recommender systems and ranking techniques. It discusses how recommender systems typically focus on accuracy but overlook diversity. The paper explores various recommendation techniques, including content-based, collaborative filtering, knowledge-based, and hybrid approaches. It also examines different ranking methods that can increase aggregate diversity, such as popularity-based, reverse predicted rating, and parameterized ranking. The goal is to improve recommendation diversity while maintaining adequate accuracy.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
SIMILARITY MEASURES FOR RECOMMENDER SYSTEMS: A COMPARATIVE STUDYJournal For Research
Recommender Systems have the ability to guide the users in a personalized way to interesting items in a large space of possible options. They have fundamental applications in e-commerce and information retrieval, providing suggestion that prune large information spaces so that users are directed towards those items that best meets the needs and preferences. A variety of approaches have been proposed but collaborative filtering has been the most popular and widely used which makes use of various similarity measures to calculate the similarity. Collaborative Filtering takes the user feedback in the form of ratings in an application area and uses it to find similarities and differences between user profiles to generate recommendations. Collaborative Filtering makes use of various similarity measures to calculate the similarity or difference between the users. This paper provides an overview on few important similarity measures that are currently being used. Different similarity measures provide different results against same input parameters. So, to understand how various similarity measures behave when they are put in different contexts but with same input, few observations are made. This paper also provides a comparison graph to help understand the results of different similarity measures.
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.
IRJET- An Intuitive Sky-High View of Recommendation SystemsIRJET Journal
This document discusses recommendation systems and their importance in today's information-rich world. It describes two main types of recommendation systems: content-based and collaborative filtering. Content-based systems recommend items similar to those a user liked in the past, while collaborative filtering recommends items liked by other users with similar preferences. The document outlines memory-based and model-based collaborative filtering approaches, and user-based and item-based collaborative filtering methods. It concludes that recommendation systems are crucial for industries relying on user engagement to guide consumers' decision-making.
IRJET- Analysis on Existing Methodologies of User Service Rating Prediction S...IRJET Journal
This document summarizes and analyzes existing methodologies for user service rating prediction systems. It discusses recommendation systems including collaborative filtering, content-based filtering, and hybrid approaches. Collaborative filtering predicts user ratings based on opinions of other similar users but faces challenges of cold start, scalability, and sparsity. Content-based filtering relies on item profiles and user preferences to recommend similar items but requires detailed item information. Hybrid systems combine collaborative and content-based filtering to address their individual limitations. The document also examines social recommender systems and how they can account for relationship strength, expertise, and user similarity within social networks.
Hybrid Personalized Recommender System Using Modified Fuzzy C-Means Clusterin...Waqas Tariq
Recommender Systems apply machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource. This paper proposes a novel Modified Fuzzy C-means (MFCM) clustering algorithm which is used for Hybrid Personalized Recommender System (MFCMHPRS). The proposed system works in two phases. In the first phase, opinions from the users are collected in the form of user-item rating matrix. They are clustered offline using MFCM into predetermined number clusters and stored in a database for future recommendation. In the second phase, the recommendations are generated online for active users using similarity measures by choosing the clusters with good quality rating. We propose coefficient parameter for similarity computation when weighting of the users’ similarity. This helps to get further effectiveness and quality of recommendations for the active users. The experimental results using Iris dataset show that the proposed MFCM performs better than Fuzzy C-means (FCM) algorithm. The performance of MFCMHPRS is evaluated using Jester database available on website of California University, Berkeley and compared with fuzzy recommender system (FRS). The results obtained empirically demonstrate that the proposed MFCMHPRS performs superiorly.
A Study of Neural Network Learning-Based Recommender Systemtheijes
This document summarizes a study that proposes a neural network learning model for recommender systems. The study aims to improve collaborative filtering methods by estimating user preferences based on learned correlations between users through a neural network. The proposed method was tested on MovieLens data and showed improved precision of 6.7% compared to other techniques. Additionally, the study found that precision and recall improved further, by 3.5% and 2.4% respectively, when including film genre information in the neural network learning. The document concludes the proposed technique can utilize diverse data sources and perform well regardless of data complexity compared to other recommender system methods.
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.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
SIMILARITY MEASURES FOR RECOMMENDER SYSTEMS: A COMPARATIVE STUDYJournal For Research
Recommender Systems have the ability to guide the users in a personalized way to interesting items in a large space of possible options. They have fundamental applications in e-commerce and information retrieval, providing suggestion that prune large information spaces so that users are directed towards those items that best meets the needs and preferences. A variety of approaches have been proposed but collaborative filtering has been the most popular and widely used which makes use of various similarity measures to calculate the similarity. Collaborative Filtering takes the user feedback in the form of ratings in an application area and uses it to find similarities and differences between user profiles to generate recommendations. Collaborative Filtering makes use of various similarity measures to calculate the similarity or difference between the users. This paper provides an overview on few important similarity measures that are currently being used. Different similarity measures provide different results against same input parameters. So, to understand how various similarity measures behave when they are put in different contexts but with same input, few observations are made. This paper also provides a comparison graph to help understand the results of different similarity measures.
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.
IRJET- An Intuitive Sky-High View of Recommendation SystemsIRJET Journal
This document discusses recommendation systems and their importance in today's information-rich world. It describes two main types of recommendation systems: content-based and collaborative filtering. Content-based systems recommend items similar to those a user liked in the past, while collaborative filtering recommends items liked by other users with similar preferences. The document outlines memory-based and model-based collaborative filtering approaches, and user-based and item-based collaborative filtering methods. It concludes that recommendation systems are crucial for industries relying on user engagement to guide consumers' decision-making.
IRJET- Analysis on Existing Methodologies of User Service Rating Prediction S...IRJET Journal
This document summarizes and analyzes existing methodologies for user service rating prediction systems. It discusses recommendation systems including collaborative filtering, content-based filtering, and hybrid approaches. Collaborative filtering predicts user ratings based on opinions of other similar users but faces challenges of cold start, scalability, and sparsity. Content-based filtering relies on item profiles and user preferences to recommend similar items but requires detailed item information. Hybrid systems combine collaborative and content-based filtering to address their individual limitations. The document also examines social recommender systems and how they can account for relationship strength, expertise, and user similarity within social networks.
Hybrid Personalized Recommender System Using Modified Fuzzy C-Means Clusterin...Waqas Tariq
Recommender Systems apply machine learning and data mining techniques for filtering unseen information and can predict whether a user would like a given resource. This paper proposes a novel Modified Fuzzy C-means (MFCM) clustering algorithm which is used for Hybrid Personalized Recommender System (MFCMHPRS). The proposed system works in two phases. In the first phase, opinions from the users are collected in the form of user-item rating matrix. They are clustered offline using MFCM into predetermined number clusters and stored in a database for future recommendation. In the second phase, the recommendations are generated online for active users using similarity measures by choosing the clusters with good quality rating. We propose coefficient parameter for similarity computation when weighting of the users’ similarity. This helps to get further effectiveness and quality of recommendations for the active users. The experimental results using Iris dataset show that the proposed MFCM performs better than Fuzzy C-means (FCM) algorithm. The performance of MFCMHPRS is evaluated using Jester database available on website of California University, Berkeley and compared with fuzzy recommender system (FRS). The results obtained empirically demonstrate that the proposed MFCMHPRS performs superiorly.
A Study of Neural Network Learning-Based Recommender Systemtheijes
This document summarizes a study that proposes a neural network learning model for recommender systems. The study aims to improve collaborative filtering methods by estimating user preferences based on learned correlations between users through a neural network. The proposed method was tested on MovieLens data and showed improved precision of 6.7% compared to other techniques. Additionally, the study found that precision and recall improved further, by 3.5% and 2.4% respectively, when including film genre information in the neural network learning. The document concludes the proposed technique can utilize diverse data sources and perform well regardless of data complexity compared to other recommender system methods.
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.
A Hybrid Approach for Personalized Recommender System Using Weighted TFIDF on...Editor IJCATR
Recommender systems are gaining a great popularity with the emergence of e-commerce and social media on the internet. These recommender systems enable users’ access products or services that they would otherwise not be aware of due to the wealth of information on the internet. Two traditional methods used to develop recommender systems are content-based and collaborative filtering. While both methods have their strengths, they also have weaknesses; such as sparsity, new item and new user problem that leads to poor recommendation quality. Some of these weaknesses can be overcome by combining two or more methods to form a hybrid recommender system. This paper deals with issues related to the design and evaluation of a personalized hybrid recommender system that combines content-based and collaborative filtering methods to improve the precision of recommendation. Experiments done using MovieLens dataset shows the personalized hybrid recommender system outperforms the two traditional methods implemented separately.
The document describes research using support vector machines (SVMs) to classify movie reviews based on sentiment. It discusses related work applying SVMs, Naive Bayes classifiers, and other techniques to sentiment analysis. The researchers applied SVMs to a dataset of 1000 positive and 1000 negative movie reviews, obtaining the best accuracy of 86.72% using a linear SVM with normalized presence vectors representing word features in the reviews.
An Adaptive Framework for Enhancing Recommendation Using Hybrid Techniqueijcsit
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.
This document summarizes a research paper that proposes a novel approach for dynamic personalized recommendation. It utilizes information from user ratings and profiles to develop dynamic features that describe user preferences over multiple phases of interest. An adaptive weighting algorithm then makes recommendations by weighting these dynamic features based on the amount of rating data available. The proposed approach was tested on public datasets and performed well for dynamic recommendation compared to existing algorithms.
This document provides an overview of a module on recommender systems for a digital library curriculum. The module aims to teach students about different recommender system approaches, including content-based, collaborative filtering, and hybrid systems. It also covers challenges in recommender system design and the use of user profiles. The key topics covered include recommender system types and techniques, challenges in collaborative filtering, and modeling user profiles both explicitly and implicitly.
a, u ) =
∑ w (r − r )(r − r )
∑ w (r − r ) ∑ w (r − r )
i
i
a ,i
a
i
u ,i
u
i
a ,i
a
i
2
u ,i
2
u
(4)
The proposed method enhances user-based collaborative filtering by using content features of items to assign weights. When finding similar users to an active user, it emphasizes similarities in ratings for items with similar content features. This is done by weighting each rating based on
AN EXTENDED HYBRID RECOMMENDER SYSTEM BASED ON ASSOCIATION RULES MINING IN DI...csandit
Social groups in the form of different discussion forums are proliferating rapidly. Most of these
forums have been created to exchange and share members’ knowledge in various domains.
Members in these groups may need to use and retrieve other members’ knowledge. Therefore,
recommender systems are one of the techniques which can be employed in order to extract
knowledge based on the members’ needs and favorites. It is noteworthy that not only the users’
comments and posts can have valuable information, but also there are some other valuable
information which can be obtained from social data; moreover, it could be extracted from
relations and interactions among users. Hence, association rules mining techniques are one of
the techniques which can be applied in order to extract more implicit data as input to the
recommender system. Our objective in this study is to improve the performance of a hybrid
recommender system by defining new hybrid rules. In this regard, for the first time, we have
defined new hybrid rules by considering both users and posts’ content data. Each of the defined
rules has been examined on an asynchronous discussion group in this study. In addition, the
impact of the defined rules on the precision and recall values of the recommender system has
been examined. We found that according to this impact, a classification of the defined rules can
be considered and a number of weights can be assigned to each rule based on their impact and
usability in the specific domain or application. It is noteworthy that the results of the
experiments have been promising.
IRJET- Book Recommendation System using Item Based Collaborative FilteringIRJET Journal
This document describes an item-based collaborative filtering approach for a book recommendation system. It discusses different recommendation system techniques including collaborative filtering, content-based filtering, and hybrid filtering. It then focuses on item-based collaborative filtering, explaining how it calculates item similarities using adjusted cosine similarity and makes predictions using weighted sums. The document tests the approach on the Goodbooks10k dataset and evaluates it using mean absolute error, finding lower error rates with more neighbor items. In conclusion, item-based collaborative filtering is an effective approach for book recommendations.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
IRJET- Analysis of Rating Difference and User InterestIRJET Journal
This document summarizes a research paper that proposes a collaborative filtering recommendation algorithm that incorporates rating differences and user interests. It first adds a rating difference factor to the traditional collaborative filtering algorithm. It then calculates user interests based on item attributes and the similarity between user interests. Recommendations are made by weighting user rating differences and interest similarities. The proposed algorithm is shown to reduce error rates and improve accuracy compared to traditional collaborative filtering.
Recommendation systems only provide more specific recommendations to users. They do not consider
giving a justification for the recommendation. However, the justification for the recommendation allows the
user to make the decision whether or not to accept the recommendation. It also improves user satisfaction
and the relevance of the recommended item. However, the IAAS recommendation system that uses
advisories to make recommendations does not provide a justification for the recommendations. That is why
in this article, our task consists for helping IAAS users to justify their recommendations.
A Survey on Sentiment Analysis and Opinion MiningIJSRD
In Today’s world, the social media has given web users a place for expressing and sharing their thoughts and opinions on different topics or events. For this purpose, the opinion mining has gained the importance. Sentiment classification and Opinion Mining is the study of people’s opinion, emotions, attitude towards the product, services, etc. Sentiment Analysis and Opinion Mining are the two interchangeable terms. There are various approaches and techniques exist for Sentiment Analysis like Naïve Bayes, Decision Trees, Support Vector Machines, Random Forests, Maximum Entropy, etc. Opinion mining is a useful and beneficial way to scientific surveys, political polls, market research and business intelligence, etc. This paper presents a literature review of various techniques used for opinion mining and sentiment analysis.
IRJET- A Survey on Recommender Systems used for User Service Rating in Social...IRJET Journal
This document summarizes research on recommender systems used for user service ratings in social networks. It first discusses how recommender systems predict user ratings using collaborative, content-based, and hybrid filtering techniques. It then reviews related work on collaborative, content-based, and hybrid recommendation approaches. Challenges like cold starts are also discussed. The document concludes that combining personal interests, social similarities and influences into a unified framework can improve rating predictions.
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.
This document discusses techniques for personalizing search engine results using concept-based user profiles. It proposes six methods for creating user profiles that capture both positive and negative user preferences and interests based on concepts extracted from search queries and results. The methods use machine learning algorithms to learn weighted concept vectors representing user profiles. An evaluation found that profiles capturing both positive and negative preferences performed best. The goal is to resolve query ambiguity and increase result relevance by understanding each user's unique interests and preferences.
Recommender systems have grown to be a critical research subject after the emergence of the first paper on collaborative filtering in the Nineties. Despite the fact that educational studies on recommender systems, has extended extensively over the last 10 years, there are deficiencies in the complete literature evaluation and classification of that research. Because of this, we reviewed articles on recommender structures, and then classified those based on sentiment analysis. The articles are categorized into three techniques of recommender system, i.e.; collaborative filtering (CF), content based and context based. We have tried to find out the research papers related to sentimental analysis based recommender system. To classify research done by authors in this field, we have shown different approaches of recommender system based on sentimental analysis with the help of tables. Our studies give statistics, approximately trends in recommender structures research, and gives practitioners and researchers with perception and destiny route on the recommender system using sentimental analysis. We hope that this paper enables all and sundry who is interested in recommender systems research with insight for destiny.
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.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
This study examines the use of ethanol-kerosene fuel blends in kerosene wick stoves as a more sustainable alternative fuel. Various blends of 5%, 10%, 15%, and 20% ethanol in kerosene by volume were tested in an unmodified stove and compared to pure kerosene. The thermal efficiency and fuel consumption rate were evaluated using a standard water boiling test. The maximum thermal efficiency was obtained with a 5% ethanol blend, while the minimum was with pure kerosene. The 10% ethanol blend had the highest fuel consumption rate. Overall, the performance of the blended fuels was found to be comparable to kerosene, indicating their potential as a renewable and less polluting alternative for cooking
El documento contiene una serie de frases ingeniosas y reflexivas sobre la vida. Algunas de las ideas expresadas son: comenzar el día con una sonrisa a pesar de las dificultades, que el dinero no trae la felicidad, no tomarse la vida demasiado en serio dado que nadie sale vivo de ella, y recordar que a pesar de ser únicos todos somos iguales. El documento termina deseando que el lector disfrute de la vida ya que es corta pero linda.
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.
A Hybrid Approach for Personalized Recommender System Using Weighted TFIDF on...Editor IJCATR
Recommender systems are gaining a great popularity with the emergence of e-commerce and social media on the internet. These recommender systems enable users’ access products or services that they would otherwise not be aware of due to the wealth of information on the internet. Two traditional methods used to develop recommender systems are content-based and collaborative filtering. While both methods have their strengths, they also have weaknesses; such as sparsity, new item and new user problem that leads to poor recommendation quality. Some of these weaknesses can be overcome by combining two or more methods to form a hybrid recommender system. This paper deals with issues related to the design and evaluation of a personalized hybrid recommender system that combines content-based and collaborative filtering methods to improve the precision of recommendation. Experiments done using MovieLens dataset shows the personalized hybrid recommender system outperforms the two traditional methods implemented separately.
The document describes research using support vector machines (SVMs) to classify movie reviews based on sentiment. It discusses related work applying SVMs, Naive Bayes classifiers, and other techniques to sentiment analysis. The researchers applied SVMs to a dataset of 1000 positive and 1000 negative movie reviews, obtaining the best accuracy of 86.72% using a linear SVM with normalized presence vectors representing word features in the reviews.
An Adaptive Framework for Enhancing Recommendation Using Hybrid Techniqueijcsit
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.
This document summarizes a research paper that proposes a novel approach for dynamic personalized recommendation. It utilizes information from user ratings and profiles to develop dynamic features that describe user preferences over multiple phases of interest. An adaptive weighting algorithm then makes recommendations by weighting these dynamic features based on the amount of rating data available. The proposed approach was tested on public datasets and performed well for dynamic recommendation compared to existing algorithms.
This document provides an overview of a module on recommender systems for a digital library curriculum. The module aims to teach students about different recommender system approaches, including content-based, collaborative filtering, and hybrid systems. It also covers challenges in recommender system design and the use of user profiles. The key topics covered include recommender system types and techniques, challenges in collaborative filtering, and modeling user profiles both explicitly and implicitly.
a, u ) =
∑ w (r − r )(r − r )
∑ w (r − r ) ∑ w (r − r )
i
i
a ,i
a
i
u ,i
u
i
a ,i
a
i
2
u ,i
2
u
(4)
The proposed method enhances user-based collaborative filtering by using content features of items to assign weights. When finding similar users to an active user, it emphasizes similarities in ratings for items with similar content features. This is done by weighting each rating based on
AN EXTENDED HYBRID RECOMMENDER SYSTEM BASED ON ASSOCIATION RULES MINING IN DI...csandit
Social groups in the form of different discussion forums are proliferating rapidly. Most of these
forums have been created to exchange and share members’ knowledge in various domains.
Members in these groups may need to use and retrieve other members’ knowledge. Therefore,
recommender systems are one of the techniques which can be employed in order to extract
knowledge based on the members’ needs and favorites. It is noteworthy that not only the users’
comments and posts can have valuable information, but also there are some other valuable
information which can be obtained from social data; moreover, it could be extracted from
relations and interactions among users. Hence, association rules mining techniques are one of
the techniques which can be applied in order to extract more implicit data as input to the
recommender system. Our objective in this study is to improve the performance of a hybrid
recommender system by defining new hybrid rules. In this regard, for the first time, we have
defined new hybrid rules by considering both users and posts’ content data. Each of the defined
rules has been examined on an asynchronous discussion group in this study. In addition, the
impact of the defined rules on the precision and recall values of the recommender system has
been examined. We found that according to this impact, a classification of the defined rules can
be considered and a number of weights can be assigned to each rule based on their impact and
usability in the specific domain or application. It is noteworthy that the results of the
experiments have been promising.
IRJET- Book Recommendation System using Item Based Collaborative FilteringIRJET Journal
This document describes an item-based collaborative filtering approach for a book recommendation system. It discusses different recommendation system techniques including collaborative filtering, content-based filtering, and hybrid filtering. It then focuses on item-based collaborative filtering, explaining how it calculates item similarities using adjusted cosine similarity and makes predictions using weighted sums. The document tests the approach on the Goodbooks10k dataset and evaluates it using mean absolute error, finding lower error rates with more neighbor items. In conclusion, item-based collaborative filtering is an effective approach for book recommendations.
The International Journal of Engineering and Science (The IJES)theijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
IRJET- Analysis of Rating Difference and User InterestIRJET Journal
This document summarizes a research paper that proposes a collaborative filtering recommendation algorithm that incorporates rating differences and user interests. It first adds a rating difference factor to the traditional collaborative filtering algorithm. It then calculates user interests based on item attributes and the similarity between user interests. Recommendations are made by weighting user rating differences and interest similarities. The proposed algorithm is shown to reduce error rates and improve accuracy compared to traditional collaborative filtering.
Recommendation systems only provide more specific recommendations to users. They do not consider
giving a justification for the recommendation. However, the justification for the recommendation allows the
user to make the decision whether or not to accept the recommendation. It also improves user satisfaction
and the relevance of the recommended item. However, the IAAS recommendation system that uses
advisories to make recommendations does not provide a justification for the recommendations. That is why
in this article, our task consists for helping IAAS users to justify their recommendations.
A Survey on Sentiment Analysis and Opinion MiningIJSRD
In Today’s world, the social media has given web users a place for expressing and sharing their thoughts and opinions on different topics or events. For this purpose, the opinion mining has gained the importance. Sentiment classification and Opinion Mining is the study of people’s opinion, emotions, attitude towards the product, services, etc. Sentiment Analysis and Opinion Mining are the two interchangeable terms. There are various approaches and techniques exist for Sentiment Analysis like Naïve Bayes, Decision Trees, Support Vector Machines, Random Forests, Maximum Entropy, etc. Opinion mining is a useful and beneficial way to scientific surveys, political polls, market research and business intelligence, etc. This paper presents a literature review of various techniques used for opinion mining and sentiment analysis.
IRJET- A Survey on Recommender Systems used for User Service Rating in Social...IRJET Journal
This document summarizes research on recommender systems used for user service ratings in social networks. It first discusses how recommender systems predict user ratings using collaborative, content-based, and hybrid filtering techniques. It then reviews related work on collaborative, content-based, and hybrid recommendation approaches. Challenges like cold starts are also discussed. The document concludes that combining personal interests, social similarities and influences into a unified framework can improve rating predictions.
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.
This document discusses techniques for personalizing search engine results using concept-based user profiles. It proposes six methods for creating user profiles that capture both positive and negative user preferences and interests based on concepts extracted from search queries and results. The methods use machine learning algorithms to learn weighted concept vectors representing user profiles. An evaluation found that profiles capturing both positive and negative preferences performed best. The goal is to resolve query ambiguity and increase result relevance by understanding each user's unique interests and preferences.
Recommender systems have grown to be a critical research subject after the emergence of the first paper on collaborative filtering in the Nineties. Despite the fact that educational studies on recommender systems, has extended extensively over the last 10 years, there are deficiencies in the complete literature evaluation and classification of that research. Because of this, we reviewed articles on recommender structures, and then classified those based on sentiment analysis. The articles are categorized into three techniques of recommender system, i.e.; collaborative filtering (CF), content based and context based. We have tried to find out the research papers related to sentimental analysis based recommender system. To classify research done by authors in this field, we have shown different approaches of recommender system based on sentimental analysis with the help of tables. Our studies give statistics, approximately trends in recommender structures research, and gives practitioners and researchers with perception and destiny route on the recommender system using sentimental analysis. We hope that this paper enables all and sundry who is interested in recommender systems research with insight for destiny.
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.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
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The document discusses movie recommendation systems. It describes how recommendation systems work by predicting a user's rating or preference for an item based on their past ratings and preferences. It outlines several methods used in recommendation systems, including collaborative filtering, content-based filtering, and hybrid systems. It also discusses some specific types of recommendation systems like multi-criteria, risk-aware, and mobile recommender systems. The document provides examples of companies that use recommendation systems and classifications and techniques used to develop these systems.
This document presents a project proposal for a Recommendation System for Technical Learning. It includes:
1. The names of the team members and project guide.
2. The objectives are to create a recommendation system to recommend relevant courses and books to users based on popularity and interests using collaborative and content-based filtering.
3. The literature review discusses previous recommendation system problems and solutions using collaborative filtering on Hadoop and considering location as an attribute.
4. The solution approach uses two types of filtering - collaborative and content-based - to build the recommendation system and analyze user ratings to train an ML model to make recommendations.
Analysing the performance of Recommendation System using different similarity...IRJET Journal
The document analyzes the performance of different similarity metrics used in recommendation systems, including Pearson correlation, cosine similarity, Jaccard coefficient, mean squared difference, and singular value decomposition. It finds that the Jaccard similarity metric produces better accuracy and less time complexity compared to Pearson correlation and cosine similarity when applied to the Movielens 100k dataset. The document also provides an overview of recommendation system types such as content-based, collaborative filtering, and hybrid systems, as well as collaborative filtering approaches like user-to-user and item-to-item.
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.
A Study of Neural Network Learning-Based Recommender Systemtheijes
A recommender system sorts and recommends the information which meets personal preferences among a huge amount of data provided by e-commerce. In particular, collaborative filtering (CF) is the most widely used technique in these recommendation systems. This method finds neighboring users who have similar preferences with particular users and recommends the items preferred by the former. This study proposes a neural network learning model as a new technique to find neighboring users using the collaborative filtering method. This kind of neural network learning model takes care of a sparseness problem during the analysis stage among those related with target users. The proposed method was tested with MovieLens data sets, and the results showed that precision improved by 6.7%.
Recommender System (RS) has emerged as a significant research interest that aims to assist users to seek out items online by providing suggestions that closely match their interests. Recommender system, an information filtering technology employed in many items is presented in internet sites as per the interest of users, and is implemented in applications like movies, music, venue, books, research articles, tourism and social media normally. Recommender systems research is usually supported comparisons of predictive accuracy: the higher the evaluation scores, the higher the recommender. One amongst the leading approaches was the utilization of advice systems to proactively recommend scholarly papers to individual researchers. In today's world, time has more value and therefore the researchers haven't any much time to spend on trying to find the proper articles in line with their research domain. Recommender Systems are designed to suggest users the things that best fit the user needs and preferences. Recommender systems typically produce an inventory of recommendations in one among two ways -through collaborative or content-based filtering. Additionally, both the general public and also the non-public used descriptive metadata are used. The scope of the advice is therefore limited to variety of documents which are either publicly available or which are granted copyright permits. Recommendation systems (RS) support users and developers of varied computer and software systems to beat information overload, perform information discovery tasks and approximate computation, among others.
AN EXTENDED HYBRID RECOMMENDER SYSTEM BASED ON ASSOCIATION RULES MINING IN DI...cscpconf
Social groups in the form of different discussion forums are proliferating rapidly. Most of these forums have been created to exchange and share members’ knowledge in various domains.
Members in these groups may need to use and retrieve other members’ knowledge. Therefore, recommender systems are one of the techniques which can be employed in order to extract
knowledge based on the members needs and favorites. It is noteworthy that not only the users comments and posts can have valuable information, but also there are some other valuable information which can be obtained from social data; moreover, it could be extracted from relations and interactions among users. Hence, association rules mining techniques are one of the techniques which can be applied in order to extract more implicit data as input to the recommender system. Our objective in this study is to improve the performance of a hybrid
recommender system by defining new hybrid rules. In this regard, for the first time, we have defined new hybrid rules by considering both users and posts’ content data. Each of the defined rules has been examined on an asynchronous discussion group in this study. In addition, the impact of the defined rules on the precision and recall values of the recommender system has been examined. We found that according to this impact, a classification of the defined rules can
be considered and a number of weights can be assigned to each rule based on their impact and usability in the specific domain or application. It is noteworthy that the results of the
experiments have been promising
Analysis on Recommended System for Web Information Retrieval Using HMMIJERA Editor
Web is a rich domain of data and knowledge, which is spread over the world in unstructured manner. The
number of users is continuously access the information over the internet. Web mining is an application of data
mining where web related data is extracted and manipulated for extracting knowledge. The data mining is used
in the domain of web information mining is refers as web mining, that is further divided into three major
domains web uses mining, web content mining and web structure mining. The proposed work is intended to
work with web uses mining. The concept of web mining is to improve the user feedbacks and user navigation
pattern discovery for a CRM system. Finally a new algorithm HMM is used for finding the pattern in data,
which method promises to provide much accurate recommendation.
This document provides an overview of recommender systems. It discusses how recommender systems aim to help users find items online that match their interests. It describes two main approaches for recommender systems - collaborative filtering and content-based filtering. Collaborative filtering looks at users' past behaviors and items to find similarities between users and make recommendations. Content-based filtering uses item attributes and properties to recommend similar items to users. The document also discusses challenges with existing recommender systems and how different techniques can be combined in hybrid systems.
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.
A recommender system helps users deal with large numbers of alternatives by providing personalized recommendations. Collaborative filtering is a common approach that recommends items liked by similar users. It works by finding users with similar rating patterns to the active user and using the ratings from those similar users to calculate predictions. Content-based filtering recommends items similar to those a user liked in the past based on item descriptions and the user's profile and interaction history. Most modern recommender systems use a hybrid approach combining collaborative, content-based, and other techniques.
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.
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
This document summarizes an article on e-learning recommendation systems. It begins by defining recommendation systems and their use in helping learners identify suitable learning resources. It then provides a brief history of recommendation systems and discusses popular approaches like collaborative filtering, content-based filtering, and hybrid filtering. The document focuses on e-learning recommendation systems, reviewing key works that examine factors like context and setting. It also summarizes specific e-learning recommendation system frameworks and a study measuring learner performance with such systems.
A Literature Survey on Recommendation System Based on Sentimental Analysisaciijournal
Recommender systems have grown to be a critical research subject after the emergence of the first paper
on collaborative filtering in the Nineties. Despite the fact that educational studies on recommender systems,
has extended extensively over the last 10 years, there are deficiencies in the complete literature evaluation
and classification of that research. Because of this, we reviewed articles on recommender structures, and
then classified those based on sentiment analysis. The articles are categorized into three techniques of
recommender system, i.e.; collaborative filtering (CF), content based and context based. We have tried to
find out the research papers related to sentimental analysis based recommender system. To classify
research done by authors in this field, we have shown different approaches of recommender system based
on sentimental analysis with the help of tables. Our studies give statistics, approximately trends in
recommender structures research, and gives practitioners and researchers with perception and destiny
route on the recommender system using sentimental analysis. We hope that this paper enables all and
sundry who is interested in recommender systems research with insight for destiny.
A Literature Survey on Recommendation System Based on Sentimental Analysisaciijournal
Recommender systems have grown to be a critical research subject after the emergence of the first paper
on collaborative filtering in the Nineties. Despite the fact that educational studies on recommender systems,
has extended extensively over the last 10 years, there are deficiencies in the complete literature evaluation
and classification of that research. Because of this, we reviewed articles on recommender structures, and
then classified those based on sentiment analysis. The articles are categorized into three techniques of
recommender system, i.e.; collaborative filtering (CF), content based and context based. We have tried to
find out the research papers related to sentimental analysis based recommender system. To classify
research done by authors in this field, we have shown different approaches of recommender system based
on sentimental analysis with the help of tables. Our studies give statistics, approximately trends in
recommender structures research, and gives practitioners and researchers with perception and destiny
route on the recommender system using sentimental analysis. We hope that this paper enables all and
sundry who is interested in recommender systems research with insight for destiny.
Recommending the Appropriate Products for target user in E-commerce using SBT...IRJET Journal
The document proposes a new recommendation system for e-commerce that uses structural balance theory to recommend products when target users have no similar friends or similar product preferences. It discusses limitations of existing recommendation approaches. The proposed system first identifies a target user's "dissimilar enemies" and then determines their "possible friends" according to structural balance theory. Products liked by these "possible friends" are recommended. It aims to leverage structural balance information in user-product networks for more accurate recommendations when collaborative filtering cannot find similar users or items.
Recommending the Appropriate Products for target user in E-commerce using SBT...
Bv31491493
1. S.Deepa Kanmani, Antony Taurshia.A / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 1, January -February 2013, pp.491-493
Recommender System And Ranking Techniques: A Survey
Antony Taurshia.A S.Deepa Kanmani
Post Graduate Student Karunya University Assistant Professor Karunya University
Coimbatore, India Coimbatore, India
ABSTRACT
Recommender system is a subset of recommendation accuracy. This causes
information filtering system that predicts the overspecialization which leads to frustration of the
rating a user would give to an item and user. Some of the researches have focused on
recommends those items to the user. There are improving individual diversity. This paper explores
different ways in which recommendations can be several ranking approaches that increases the
made. The success of recommender system aggregate diversity in recommender system.
depends on the usefulness of the system. The This paper is organized as follows. Section 2
usefulness can be measured in terms of accuracy, includes a discussion on key concepts and section 3
diversity, flexibility, serendipity and reliability. includes a discussion on several recommendation
Most of the recommender system have focused techniques and section 4 gives a discussion of
on improving recommendation accuracy, but several ranking approaches and section 5 gives the
diversity is overlooked. In this paper several conclusion.
recommendation techniques and ranking
techniques that improve the aggregate diversity 2. KEY CONCEPTS
of recommendations have been explored. This consist of several keywords about the
recommender system.
Keywords:- Accuracy, aggregate diversity,
individual diversity, recommendtation. 2.1 Recommendation
Recommendation is the suggestion given
1. INTRODUCTION by system to user like suggestion for books in
There are vast amount of information Amazon.com and movies in Netflix.
available and the recommender system is proven to
be useful in extracting information and making 2.2 Accuracy
useful recommendation to users. Recommender Accuracy is how well a recommender
system plays an important role in electronic system make predictions. Accuracy can be
commerce. It inceases sales by recommending calculated as truly highly ranked items divided by
items and it allows users to make decisions such as highly ranked items.
which item to buy. Item is termed as what the
system recommends to users such as movies, books. 2.3 Individual Diversity
For example Amazon.com uses recommender Individual diversity is the diversity in the
system for recommending books to users. Mostly individual user’s recommendation list.
recommender systems work based on the ratings
given by user. Rating is user’s preference for an 2.4 Aggregate Diversity
item. Rating directly given by the user is called Aggregate diversity is the diversity in
known rating. In order to provide recommendations recommendation list across all users.
to users the following two tasks are performed.
Rating prediction: The ratings of unrated 3. RECOMMENDATION TECHNIQUES
items are predicted (i.e predicted rating) from the This section briefly discuss several
information available. recommendation techniques.
Ranking: The rated items are ranked and
then recommended to users to maximize the user’s 3.1 Content Based Recommendation
utility. Content based recommender system [6]
There are several recommendation recommend items based on user profile information
techniques like collaborative filtering, content and item description. User’s profile contains
based, hybrid. Content based recommender system information like description about the type of items
uses history of user’s preferences for recommending that interest the user and the history of user’s
items. Collaborative filtering recommender system interaction with the recommender system. Items
recommends items based on preferences of similar information are stored in a database with its
users. Hybrid recommender system uses a attributes. The key component of content based
combination of recommender system for recommendation is classification learning algorithm
recommending items. So far used recommender that creates a user model from the user history. This
system have focused only on improving recommender system is capable of introducing new
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2. S.Deepa Kanmani, Antony Taurshia.A / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 1, January -February 2013, pp.491-493
items to user. It can also provide explanation for A graph-theoretic approach [9] based on maximum
recommending items. The user has to fill profile flow or maximum bipartite computations represent
details mandatorily inorder to get recommendations. user and item as vertices and the flow is calculated.
This technique improves aggregate diversity of
3.2 Collaborative Filtering Recommendation recommendations. One of the major drawback with
Collaborative filtering recommender the graph-based approach is when the input data
system [4] recommends items based on the past becomes large it becomes tedious to rebuild graph.
preferences of similar users. First the user gives the
preferences by rating the items. Based on the users 3.6 Hybrid Recommendation
ratings the system finds the similar users. With the Hybrid recommendation uses a
similar users the ratings of unrated items are combination of recommendation technique. Content
predicted and recommended to users. There are based recommendation and collaborative filtering
several approaches of collaborative filtering recommendation is the commonly used
technique. Active filtering uses peer-to-peer combination. Both the system suffers from ramp up
approach, people who have similar interest rate problem. The disadvantages in both the techniques
products. Passive filtering approach uses implicit can be overcome by combining them in parallel or
information like user’s action for recommendation. cascade. Both the rating and the profile data can be
Item-based filtering system uses item-item used for finding recommendations. This technique
relationship for recommendation. improves the recommendation accuracy but
The collaborative filtering technique can be diversity is not considered.
memory based or model based.
Memory based CF: Heuristic based 4. RANKING TECHNIQUES
techniques recommend items based on the past This section includes several ranking
activites of users. techniques [1], [2] in recommender system to
Model based CF: This technique learns a improve aggregate diversity.
predictive model based on the past user activities
using statistical or machine learning model. 4.1 Standard Approach
The system should have enough ratings for This is the commonly used approach for
recommending items as it is fully dependent on ranking the items in recommender system. The
rating. This is called ramp up problem. predicted rating is ranked from highest to lowest.
3.3 Knowledge Based Recommendation
This technique uses the knowledge about where is the predicted rating. The power
products and users needs for making of -1 indicates that the items with highest predicted
recommendations. Recommendation is made by ratings are recommended to user. This approach
matching the similarity between user’s preference increases the accuracy in recommender system but
and product description. It does not suffer from not diversity.
ramp up problem since it does not depend on the
ratings given by user. This system needs a database 4.2 Item Popularity Based Approach
and needs to be updated for making useful Item popularity based approach ranks items
recommendations. based on the popularity of the item from lowest to
highest. The number of users who have rated the
3.4 Outside The Box Recommendation item gives the popoularity of the item.
The problem of overspecialization is
overcome using OTB recommendations [3], [5] and
helps to make fresh discoveries. This technique uses
a concept called item region. Region (i.e the “box”) is the known rating given by user u to item i.
This approach increases the diversity in
is defined as the group of similar items. Regions are
recommender system.
created based on similarity distances between items.
Stickiness is user’s familiarity to a region. Based on
the stickiness the system finds items that are not 4.3 Reverse Predicted Rating Approach
This approach ranks the items based on the
familiar to the user and recommends those items.
predicted rating value from lowest to highest.
This technique increases the novelty.
3.5 Graph Based Recommendation
Most of the recommender system use two 4.4 Item Average Rating
dimensional information like user and item. This approach ranks the items based on the
Homogeneous and heterogeneous graphs [8] can be average of the known ratings.
used which provides the capability to deal with
multidimensional information like user’s intensions.
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3. S.Deepa Kanmani, Antony Taurshia.A / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 3, Issue 1, January -February 2013, pp.491-493
discuss several recommendation techniques and
studies several ranking approaches that improves
diversity with minimal accuracy loss. This also
U(i) is the set of all users who have rated item i. includes parametrized ranking approach that
4.5 Item Absolute Likeability provides a threshold value to adjust the level of
This approach ranks the items according to accuracy and diversity. Thus the ranking
how many users liked the item. approaches can provide consistent and robust
improvements in diversity with different
recommendation techniques.
4.6 Item Relative Likeability
This approach ranks items according to the REFERENCES
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Diversity: A Graph-Theoretic Approach”
Workshop on Novelty and Diversity in
Recommender Systems, held in
TR can be used for ranking and filtering purposes. conjunction with ACM RecSys, 2011.
This approach cannot provide all N [9] Fatih. A., Aysenur. B., “Enhancing
recommendations for each user, but it can be filled Accuracy of Hybrid Recommender
using other recommendation strategies. Systems through Adapting the Domain
Trends."ACM RecSys'10 PRSAT
5 CONCLUSION Workshop, 2010.
Recommendation techniques used so far
focus on improving recommendation accuracy but
diversity is never considered. This paper briefly
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