Content-based Dimensionality Reduction for Recommender Systems

  title={Content-based Dimensionality Reduction for Recommender Systems},
  author={Panagiotis Symeonidis},
Recommender Systems are gaining widespread acceptance in e-commerce applications to confront the information overload problem. [] Key Method We apply Latent Semantic Indexing (LSI) to reveal the dominant features of a user. We provide recommendations according to this dimensionally-reduced feature profile. We perform experimental comparison of the proposed method against well-known CF, CB and hybrid algorithms. Our results show significant improvements in terms of providing accurate recommendations.

Content-based Recommender Systems: State of the Art and Trends

The role of User Generated Content is described as a way for taking into account evolving vocabularies, and the challenge of feeding users with serendipitous recommendations, that is to say surprisingly interesting items that they might not have otherwise discovered.

Enhanced vector space models for content-based recommender systems

Two approaches are introduced: the first, based on a technique called Random Indexing, reduces the impact of two classical VSM problems, this is to say its high dimensionality and the inability to manage the semantics of documents and the second extends the previous one by integrating a negation operator implemented in the Semantic Vectors1 open-source package.

An efficient collaborative filtering algorithm using SVD-free latent Semantic indexing and particle swarm optimization

A proficient dimensionality reduction-based Collaborative Filtering (CF) Recommender System that enormously increases the recommendations prediction quality and speed and decreases the memory requirements.

Improving Ant Collaborative Filtering on Sparsity via Dimension Reduction

This work takes one step further to improve the Ant Collaborative Filtering’s performance by adding a clustering step in the initialization phase to reduce the dimension of the rate matrix, which leads to the results that K<<#users, where K is the number of clusters, which stands for the maximum number of types of pheromone carried by all users.

Improved Collaborative Filtering using Evolutionary Algorithm based Feature Extraction

This work explores Evolutionary algorithms based feature extraction techniques where the extracted features may describe user or item profiles and finds that the item-based evolutionary feature extraction schemes outperform their user-based counterparts under varying parameter values.

A Web Based Recommendation System for Personal Learning Environments Using Hybrid Collaborative Filtering Approach

Improved Neighborhood- based Collaborative filtering and Hybrid Genetic algorithm with Particle Swarm Optimization (PSO) method is implemented for improving the diversity, and the convergence towards the preferred solution taking into account the preferences of users.

A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis

The approach develops a unified framework to model the three types of entities that exist in a social tagging system: users, items, and tags using a 3-order tensor, on which multiway latent semantic analysis and dimensionality reduction is performed.

On enhancing recommender systems by utilizing general social networks combined with users goals and contextual awareness

The proposed solutions cannot be extended directly to General Purpose Social Networks like Facebook and Twitter which are open social networks where users can do a variety of useful actions that can be useful for recommendation, but as they can’t rate items, these information are not possible to be used in recommender systems.

A Study on Publication Recommender System with Content Modelling

This work addresses the problem of recommending appropriate conferences to the authors to increase their chances of receipt and presents three approaches for the same involving the use of social network of the authors and the content of the paper in the settings of dimensionality reduction and topic modelling.



Content-boosted collaborative filtering for improved recommendations

This paper presents an elegant and effective framework for combining content and collaboration, which uses a content-based predictor to enhance existing user data, and then provides personalized suggestions through collaborative filtering.

Application of Dimensionality Reduction in Recommender System - A Case Study

This paper presents two different experiments where one technology called Singular Value Decomposition (SVD) is explored to reduce the dimensionality of recommender system databases and suggests that SVD has the potential to meet many of the challenges ofRecommender systems, under certain conditions.

CinemaScreen recommender agent: combining collaborative and content-based filtering

A film recommender agent expands and fine-tunes collaborative-filtering results according to filtered content elements - namely, actors, directors, and genres. This approach supports recommendations

Attribute-aware Collaborative Filtering

Recommender systems can be viewed as a way of reducing large information spaces and to personalize information access by providing recommendations for information items based on prior usage.

Discovering Emerging Topics in Unlabelled Text Collections

This paper addresses the challenge of finding emerging and persistent “themes”, i.e. subjects that live long enough to be incorporated into a taxonomy or ontology describing the document collection, on the identification of cluster labels that “survive” changes in the constitution of the underlying population of documents.

Applica - tion of dimensionality reduction in recommender systemA case study ”

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