IMPROVING LATENT FACTOR MODEL BASED COLLABORATIVE FILTERING VIA INTEGRATED FOLKSONOMY FACTORS

Latent Factor Model (LFM) based approaches are becoming popular when implementing Collaborative Filtering (CF) recommenders, due to their high recommendation accuracy. However, current LFM approaches address the accuracy issue only based on the rating data, whereas early research indicates that integrating information from additional data sources is helpful to the recommendation accuracy. In this work we focus on improving the recommendation accuracy of a LFM based CF recommender by integrating folksonomy information. To implement this approach, we first propose a novel model named Item Folsonomy Relevance (IFR) to analyze the item relevance inside the folksonomy; we subsequently integrate the latent factors of the IFR model and rating data through probabilistic matrix factorization (PMF), a state-of-the-art matrix factorization technique, to produce recommendations based on information from both the ratings and folksonomy simultaneously. The experiments on MovieLens dataset showed that compared to two state-of-the-art LFM approaches and another folksonomy-augmented recommder, our approach could obtain advantage in recommendation accuracy.

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Word Sequential Using Deep LSTM and Matrix Factorization to Handle Rating Sparse Data for E-Commerce Recommender System

Computational Intelligence and Neuroscience ◽

10.1155/2021/8751173 ◽

2021 ◽

Vol 2021 ◽

pp. 1-20

Author(s):

Hanafi ◽

Burhanuddin Mohd Aboobaider

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Recommender System ◽

Matrix Factorization ◽

Factor Model ◽

Sparse Data ◽

Product Review ◽

Latent Factor ◽

Latent Factor Model ◽

Future Work

Recommender systems are essential engines to deliver product recommendations for e-commerce businesses. Successful adoption of recommender systems could significantly influence the growth of marketing targets. Collaborative filtering is a type of recommender system model that uses customers’ activities in the past, such as ratings. Unfortunately, the number of ratings collected from customers is sparse, amounting to less than 4%. The latent factor model is a kind of collaborative filtering that involves matrix factorization to generate rating predictions. However, using only matrix factorization would result in an inaccurate recommendation. Several models include product review documents to increase the effectiveness of their rating prediction. Most of them use methods such as TF-IDF and LDA to interpret product review documents. However, traditional models such as LDA and TF-IDF face some shortcomings, in that they show a less contextual understanding of the document. This research integrated matrix factorization and novel models to interpret and understand product review documents using LSTM and word embedding. According to the experiment report, this model significantly outperformed the traditional latent factor model by more than 16% on an average and achieved 1% on an average based on RMSE evaluation metrics, compared to the previous best performance. Contextual insight of the product review document is an important aspect to improve performance in a sparse rating matrix. In the future work, generating contextual insight using bidirectional word sequential is required to increase the performance of e-commerce recommender systems with sparse data issues.

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Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian Inference

2020 25th International Conference on Pattern Recognition (ICPR) ◽

10.1109/icpr48806.2021.9412376 ◽

2021 ◽

Author(s):

Jiansheng Fang ◽

Xiaoqing Zhang ◽

Yan Hu ◽

Yanwu Xu ◽

Ming Yang ◽

...

Keyword(s):

Bayesian Inference ◽

Collaborative Filtering ◽

Factor Model ◽

Latent Factor ◽

Latent Factor Model

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Deep latent factor model for collaborative filtering

Signal Processing ◽

10.1016/j.sigpro.2019.107366 ◽

2020 ◽

Vol 169 ◽

pp. 107366 ◽

Cited By ~ 3

Author(s):

Aanchal Mongia ◽

Neha Jhamb ◽

Emilie Chouzenoux ◽

Angshul Majumdar

Keyword(s):

Collaborative Filtering ◽

Factor Model ◽

Latent Factor ◽

Latent Factor Model

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Collaborative filtering recommendation algorithm optimization based on latent factor model clustering

2017 13th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD) ◽

10.1109/fskd.2017.8393024 ◽

2017 ◽

Cited By ~ 1

Author(s):

Erxin Wang ◽

Wenbin Yao ◽

Dongbin Wang

Keyword(s):

Collaborative Filtering ◽

Factor Model ◽

Algorithm Optimization ◽

Recommendation Algorithm ◽

Latent Factor ◽

Latent Factor Model

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A Deep Framework for Cross-Domain and Cross-System Recommendations

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/516 ◽

2018 ◽

Cited By ~ 15

Author(s):

Feng Zhu ◽

Yan Wang ◽

Chaochao Chen ◽

Guanfeng Liu ◽

Mehmet Orgun ◽

...

Keyword(s):

State Of The Art ◽

Target Domain ◽

Latent Factors ◽

Rating Data ◽

Cross Domain ◽

Recommendation Accuracy ◽

Real World Datasets ◽

Complex Relationships ◽

Fully Connected ◽

Cross System

Cross-Domain Recommendation (CDR) and Cross-System Recommendations (CSR) are two of the promising solutions to address the long-standing data sparsity problem in recommender systems. They leverage the relatively richer information, e.g., ratings, from the source domain or system to improve the recommendation accuracy in the target domain or system. Therefore, finding an accurate mapping of the latent factors across domains or systems is crucial to enhancing recommendation accuracy. However, this is a very challenging task because of the complex relationships between the latent factors of the source and target domains or systems. To this end, in this paper, we propose a Deep framework for both Cross-Domain and Cross-System Recommendations, called DCDCSR, based on Matrix Factorization (MF) models and a fully connected Deep Neural Network (DNN). Specifically, DCDCSR first employs the MF models to generate user and item latent factors and then employs the DNN to map the latent factors across domains or systems. More importantly, we take into account the rating sparsity degrees of individual users and items in different domains or systems and use them to guide the DNN training process for utilizing the rating data more effectively. Extensive experiments conducted on three real-world datasets demonstrate that DCDCSR framework outperforms the state-of-the-art CDR and CSR approaches in terms of recommendation accuracy.

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Matrix-Based Collaborative Filtering Employing Personal Values-Based Modeling and Model Relationship Learning

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2020.p0719 ◽

2020 ◽

Vol 24 (6) ◽

pp. 719-727

Author(s):

Yasufumi Takama ◽

◽

Hiroki Shibata ◽

Yuya Shiraishi

Keyword(s):

Collaborative Filtering ◽

Recommender Systems ◽

Matrix Factorization ◽

Recent Trend ◽

State Of The Art ◽

Personal Values ◽

Relationship Matrix ◽

Learning Approaches ◽

Latent Factors ◽

Factorization Methods

This paper proposes a matrix-based collaborative filtering (CF) employing personal values (MCFPV). Introduction of various factors such as diversity and long-tailedness in addition to accuracy is a recent trend in the study of recommender systems. We think recommending acceptable items while satisfying users’ preference is important when considering other factors than accuracy. Also, interpretability is one of important characteristics recommender systems should have. To recommend acceptable items on the basis of an interpretable mechanism, this paper proposes a matrix-based recommendation method based on personal values-based modeling. Whereas existing CF based on matrix factorization methods are known to be more accurate than neighborhood-based CF, latent factors obtained by existing methods are difficult to interpret. On the other hand, user/item models of the propose method (MCFPV) is expected to be interpretable, because it represents the effect of each attribute items have on user’s decision making. Regarding a model relationship matrix that connects user and item models, this paper proposes two approaches: manual setting and machine learning approaches. Experimental results using 5 datasets generated from actual review sites show that the proposed methods recommend much unpopular items than the state-of-the art matrix factorization-based methods while keeping precision and recall.

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