scholarly journals Evolving Matrix-Factorization-Based Collaborative Filtering Using Genetic Programming

2020 ◽  
Vol 10 (2) ◽  
pp. 675 ◽  
Author(s):  
Raúl Lara-Cabrera ◽  
Ángel González-Prieto ◽  
Fernando Ortega ◽  
Jesús Bobadilla
Keyword(s):  
Genetic Programming ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Matrix Factorization ◽  
Latent Structure ◽  
Matrix Factorizations ◽  
Evolutionary Approach ◽  
Similar Performance ◽  
Latent Space ◽  
Low Dimensional

Recommender systems aim to estimate the judgment or opinion that a user might offer to an item. Matrix-factorization-based collaborative filtering typifies both users and items as vectors of factors inferred from item rating patterns. This method finds latent structure in the data, assuming that observations lie close to a low-dimensional latent space. However, matrix factorizations have been traditionally designed by hand. Here, we present Evolutionary Matrix Factorization (EMF), an evolutionary approach that automatically generates matrix factorizations aimed at improving the performance of recommender systems. Initial experiments using this approach show that EMF generally outperforms baseline methods when applied to MovieLens and FilmTrust datasets, having a similar performance to those baselines on the worst cases. These results serve as an incentive to continue improving and studying the application of an evolutionary approach to collaborative filtering based on Matrix Factorization.

scholarly journals Predictive learning as a network mechanism for extracting low-dimensional latent space representations

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Stefano Recanatesi ◽  
Matthew Farrell ◽  
Guillaume Lajoie ◽  
Sophie Deneve ◽  
Mattia Rigotti ◽  
...  
Keyword(s):  
Latent Variables ◽  
Latent Structure ◽  
Network Activity ◽  
Semantic Organization ◽  
Sequential Processing ◽  
Predictive Learning ◽  
Neural Representations ◽  
Latent Space ◽  
Low Dimensional ◽  
Sensory Prediction

AbstractArtificial neural networks have recently achieved many successes in solving sequential processing and planning tasks. Their success is often ascribed to the emergence of the task’s low-dimensional latent structure in the network activity – i.e., in the learned neural representations. Here, we investigate the hypothesis that a means for generating representations with easily accessed low-dimensional latent structure, possibly reflecting an underlying semantic organization, is through learning to predict observations about the world. Specifically, we ask whether and when network mechanisms for sensory prediction coincide with those for extracting the underlying latent variables. Using a recurrent neural network model trained to predict a sequence of observations we show that network dynamics exhibit low-dimensional but nonlinearly transformed representations of sensory inputs that map the latent structure of the sensory environment. We quantify these results using nonlinear measures of intrinsic dimensionality and linear decodability of latent variables, and provide mathematical arguments for why such useful predictive representations emerge. We focus throughout on how our results can aid the analysis and interpretation of experimental data.

A Meta-Algorithm for Improving Top-N Prediction Efficiency of Matrix Factorization Models in Collaborative Filtering

2019 ◽  
Vol 34 (03) ◽  
pp. 2059007
Author(s):  
A. Murat Yagci ◽  
Tevfik Aytekin ◽  
Fikret S. Gurgen
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Space Efficiency ◽  
Neighbor Search ◽  
Prediction Time ◽  
Low Dimensional ◽  
Prediction Efficiency

Matrix factorization models often reveal the low-dimensional latent structure in high-dimensional spaces while bringing space efficiency to large-scale collaborative filtering problems. Improving training and prediction time efficiencies of these models are also important since an accurate model may raise practical concerns if it is slow to capture the changing dynamics of the system. For the training task, powerful improvements have been proposed especially using SGD, ALS, and their parallel versions. In this paper, we focus on the prediction task and combine matrix factorization with approximate nearest neighbor search methods to improve the efficiency of top-N prediction queries. Our efforts result in a meta-algorithm, MMFNN, which can employ various common matrix factorization models, drastically improve their prediction efficiency, and still perform comparably to standard prediction approaches or sometimes even better in terms of predictive power. Using various batch, online, and incremental matrix factorization models, we present detailed empirical analysis results on many large implicit feedback datasets from different application domains.

Mining intrinsic information by matrix factorization-based approaches for collaborative filtering in recommender systems

2017 ◽  
Vol 249 ◽  
pp. 48-63 ◽  
Author(s):  
Yangyang Li ◽  
Dong Wang ◽  
Haiyang He ◽  
Licheng Jiao ◽  
Yu Xue
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Matrix Factorization

scholarly journals Discrete Trust-aware Matrix Factorization for Fast Recommendation

2019 ◽  
Author(s):  
Guibing Guo ◽  
Enneng Yang ◽  
Li Shen ◽  
Xiaochun Yang ◽  
Xiaodong He
Keyword(s):  
Social Influence ◽  
Collaborative Filtering ◽  
Recommender Systems ◽  
Social Relations ◽  
Real World ◽  
Matrix Factorization ◽  
State Of The Art ◽  
Proposed Model ◽  
Hamming Space ◽  
Real World Datasets

Trust-aware recommender systems have received much attention recently for their abilities to capture the influence among connected users. However, they suffer from the efficiency issue due to large amount of data and time-consuming real-valued operations. Although existing discrete collaborative filtering may alleviate this issue to some extent, it is unable to accommodate social influence. In this paper we propose a discrete trust-aware matrix factorization (DTMF) model to take dual advantages of both social relations and discrete technique for fast recommendation. Specifically, we map the latent representation of users and items into a joint hamming space by recovering the rating and trust interactions between users and items. We adopt a sophisticated discrete coordinate descent (DCD) approach to optimize our proposed model. In addition, experiments on two real-world datasets demonstrate the superiority of our approach against other state-of-the-art approaches in terms of ranking accuracy and efficiency.

scholarly journals Book Recommendation System using Matrix Factorization

2021 ◽  
Vol 9 (VI) ◽  
pp. 4578-4582
Author(s):  
K. Venkata Ruchitha
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Recommender System ◽  
Matrix Factorization ◽  
Recommendation System ◽  
Previous History ◽  
Speed Up ◽  
Area Unit ◽  
Content Based Filtering

In recent years, recommender systems became more and more common and area unit applied to a various vary of applications, thanks to development of things and its numerous varieties accessible, that leaves the users to settle on from bumper provided choices. Recommendations generally speed up searches and create it easier for users to access content that they're curious about, and conjointly surprise them with offers they'd haven't sought for. By victimisation filtering strategies for pre-processing the information, recommendations area unit provided either through collaborative filtering or through content-based Filtering. This recommender system recommends books supported the description and features. It identifies the similarity between the books supported its description. It conjointly considers the user previous history so as to advocate the identical book.

scholarly journals A Survey of Collaborative Filtering Recommender Algorithms and Their Evaluation Metrics

2017 ◽  
Vol 2 (2) ◽  
pp. 14 ◽  
Author(s):  
Mahdi Jalili
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Matrix Factorization ◽  
Contextual Information ◽  
Previous History ◽  
Rank Aggregation ◽  
Performance Criteria ◽  
Target User ◽  
Collaborative Filtering Algorithm ◽  
Evaluation Metric

Abstract—Recommender systems are often used to provide useful recommendations for users. They use previous history of the users-items interactions, e.g. purchase history and/or users rating on items, to provide a suitable recommendation list for any target user. They may also use contextual information available about items and users. Collaborative filtering algorithm and its variants are the most successful recommendation algorithms that have been applied to many applications. Collaborative filtering method works by first finding the most similar users (or items) for a target user (or items), and then building the recommendation lists. There is no unique evaluation metric to assess the performance of recommendations systems, and one often choose the one most appropriate for the application in hand. This paper compares the performance of a number of well-known collaborative filtering algorithms on movie recommendation. To this end, a number of performance criteria are used to test the algorithms. The algorithms are ranked for each evaluation metric and a rank aggregation method is used to determine the wining algorithm. Our experiments show that the probabilistic matrix factorization has the top performance in this dataset, followed by item-based and user-based collaborative filtering. Non-negative matrix factorization and Slope 1 has the worst performance among the considered algorithms. Keywords—Social networks analysis and mining, big data, recommender systems, collaborative filtering.

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