Latent factor models and aggregation operators for collaborative filtering in reciprocal recommender systems

2019 ◽  
Author(s):  
James Neve ◽  
Ivan Palomares
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Factor Models ◽  
Aggregation Operators ◽  
Latent Factor ◽  
Latent Factor Models

Content-Enhanced Matrix Factorization for Recommender Systems

2013 ◽  
Vol 475-476 ◽  
pp. 1084-1089
Author(s):  
Hui Yuan Chang ◽  
Ding Xia Li ◽  
Qi Dong Liu ◽  
Rong Jing Hu ◽  
Rui Sheng Zhang
Keyword(s):  
Recommender Systems ◽  
Matrix Factorization ◽  
Factor Models ◽  
User Preferences ◽  
Latent Factor ◽  
Successful Approach ◽  
Latent Factor Models ◽  
User Ratings ◽  
Important Measurement ◽  
Potential Customers

Recommender systems are widely employed in many fields to recommend products, services and information to potential customers. As the most successful approach to recommender systems, collaborative filtering (CF) predicts user preferences in item selection based on the known user ratings of items. It can be divided into two main braches - the neighbourhood approach (NB) and latent factor models. Some of the most successful realizations of latent factor models are based on matrix factorization (MF). Accuracy is one of the most important measurement criteria for recommender systems. In this paper, to improve accuracy, we propose an improved MF model. In this model, we not only consider the latent factors describing the user and item, but also incorporate content information directly into MF.Experiments are performed on the Movielens dataset to compare the present approach with the other method. The experiment results indicate that the proposed approach can remarkably improve the recommendation quality.

scholarly journals Aspect-Level Deep Collaborative Filtering via Heterogeneous Information Networks

2018 ◽  
Author(s):  
Xiaotian Han ◽  
Chuan Shi ◽  
Senzhang Wang ◽  
Philip S. Yu ◽  
Li Song
Keyword(s):  
Neural Network ◽  
Collaborative Filtering ◽  
Factor Models ◽  
Neural Network Models ◽  
Latent Factors ◽  
Latent Factor ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Latent Features ◽  
Latent Factor Models

Latent factor models have been widely used for recommendation. Most existing latent factor models mainly utilize the rating information between users and items, although some recently extended models add some auxiliary information to learn a unified latent factor between users and items.  The unified latent factor only represents the latent features of users and items from the aspect of purchase history. However, the latent features of users and items may stem from different aspects, e.g., the brand-aspect and category-aspect of items. In this paper, we propose a Neural network based Aspect-level Collaborative Filtering model (NeuACF) to exploit different aspect latent factors. Through modelling rich objects and relations in recommender system as a heterogeneous information network, NeuACF first extracts different aspect-level similarity matrices of users and items through different meta-paths and then feeds an elaborately designed deep neural network with these matrices to learn aspect-level latent factors. Finally, the aspect-level latent factors are effectively fused with an attention mechanism for the top-N recommendation. Extensive experiments on three real datasets show that NeuACF significantly outperforms both existing latent factor models and recent neural network models.

AN OPTIMAL SCALING FRAMEWORK FOR COLLABORATIVE FILTERING RECOMMENDATION SYSTEMS

2012 ◽  
Vol 21 (06) ◽  
pp. 1250033
Author(s):  
MANOLIS G. VOZALIS ◽  
ANGELOS I. MARKOS ◽  
KONSTANTINOS G. MARGARITIS
Keyword(s):  
Missing Data ◽  
Collaborative Filtering ◽  
Principal Component ◽  
Factor Models ◽  
Optimal Scaling ◽  
Low Rank ◽  
Data Sets ◽  
Low Rank Approximation ◽  
Latent Factor ◽  
Latent Factor Models

Collaborative Filtering (CF) is a popular technique employed by Recommender Systems, a term used to describe intelligent methods that generate personalized recommendations. Some of the most efficient approaches to CF are based on latent factor models and nearest neighbor methods, and have received considerable attention in recent literature. Latent factor models can tackle some fundamental challenges of CF, such as data sparsity and scalability. In this work, we present an optimal scaling framework to address these problems using Categorical Principal Component Analysis (CatPCA) for the low-rank approximation of the user-item ratings matrix, followed by a neighborhood formation step. CatPCA is a versatile technique that utilizes an optimal scaling process where original data are transformed so that their overall variance is maximized. We considered both smooth and non-smooth transformations for the observed variables (items), such as numeric, (spline) ordinal, (spline) nominal and multiple nominal. The method was extended to handle missing data and incorporate differential weighting for items. Experiments were executed on three data sets of different sparsity and size, MovieLens 100k, 1M and Jester, aiming to evaluate the aforementioned options in terms of accuracy. A combined approach with a multiple nominal transformation and a "passive" missing data strategy clearly outperformed the other tested options for all three data sets. The results are comparable with those reported for single methods in the CF literature.

Dual-Embedding based Deep Latent Factor Models for Recommendation

2021 ◽  
Vol 15 (5) ◽  
pp. 1-24
Author(s):  
Weiyu Cheng ◽  
Yanyan Shen ◽  
Linpeng Huang ◽  
Yanmin Zhu
Keyword(s):  
Collaborative Filtering ◽  
State Of The Art ◽  
Factor Models ◽  
Superior Performance ◽  
Implicit Feedback ◽  
The Novel ◽  
Latent Factor ◽  
Latent Factor Models ◽  
Real World Datasets

Among various recommendation methods, latent factor models are usually considered to be state-of-the-art techniques, which aim to learn user and item embeddings for predicting user-item preferences. When applying latent factor models to the recommendation with implicit feedback, the quality of embeddings always suffers from inadequate positive feedback and noisy negative feedback. Inspired by the idea of NSVD that represents users based on their interacted items, this article proposes a dual-embedding based deep latent factor method for recommendation with implicit feedback. In addition to learning a primitive embedding for a user (resp. item), we represent each user (resp. item) with an additional embedding from the perspective of the interacted items (resp. users) and propose attentive neural methods to discriminate the importance of interacted users/items for dual-embedding learning. We design two dual-embedding based deep latent factor models, DELF and DESEQ, for pure collaborative filtering and temporal collaborative filtering (i.e., sequential recommendation), respectively. The novel attempt of the proposed models is to capture each user-item interaction with four deep representations that are subtly fused for preference prediction. We conducted extensive experiments on four real-world datasets. The results verify the effectiveness of user/item dual embeddings and the superior performance of our methods on item recommendation.

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