scholarly journals Optimizing Latent Factors and Collaborative Filtering for Students’ Performance Prediction

2020 ◽  
Vol 10 (16) ◽  
pp. 5601
Author(s):  
Juan A. Gómez-Pulido ◽  
Arturo Durán-Domínguez ◽  
Francisco Pajuelo-Holguera
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Prediction Accuracy ◽  
Search Space ◽  
Learning Rate ◽  
Latent Factors ◽  
Gradient Descent Algorithm ◽  
Real World Datasets ◽  
The Right ◽  
Key Aspects

The problem of predicting students’ performance has been recently tackled by using matrix factorization, a popular method applied for collaborative filtering based recommender systems. This problem consists of predicting the unknown performance or score of a particular student for a task s/he did not complete or did not attend, according to the scores of the tasks s/he did complete and the scores of the colleagues who completed the task in question. The solving method considers matrix factorization and a gradient descent algorithm in order to build a prediction model that minimizes the error in the prediction of test data. However, we identified two key aspects that influence the accuracy of the prediction. On the one hand, the model involves a pair of important parameters: the learning rate and the regularization factor, for which there are no fixed values for any experimental case. On the other hand, the datasets are extracted from virtual classrooms on online campuses and have a number of implicit latent factors. The right figures are difficult to ascertain, as they depend on the nature of the dataset: subject, size, type of learning, academic environment, etc. This paper proposes some approaches to improve the prediction accuracy by optimizing the values of the latent factors, learning rate, and regularization factor. To this end, we apply optimization algorithms that cover a wide search space. The experimental results obtained from real-world datasets improved the prediction accuracy in the context of a thorough search for predefined values. Obtaining optimized values of these parameters allows us to apply them to further predictions for similar datasets.

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 Modeling Implicit Trust in Matrix Factorization-Based Collaborative Filtering

2019 ◽  
Vol 9 (20) ◽  
pp. 4378 ◽  
Author(s):  
Yuan ◽  
Zahir ◽  
Yang
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Prediction Accuracy ◽  
State Of The Art ◽  
Side Information ◽  
Initial Trust ◽  
The Social ◽  
Implicit Trust ◽  
Value Decomposition ◽  
Better Than

Recommendation systems often use side information to both alleviate problems, such as the cold start problem and data sparsity, and increase prediction accuracy. One such piece of side information, which has been widely investigated in addressing such challenges, is trust. However, the difficulty in obtaining explicit relationship data has led researchers to infer trust values from other means such as the user-to-item relationship. This paper proposes a model to improve prediction accuracy by applying the trust relationship between the user and item ratings. Two approaches to implement trust into prediction are proposed: one involves the use of estimated trust, and the other involves the initial trust. The efficiency of the proposed method is verified by comparing the obtained results with four well-known methods, including the state-of-the-art deep learning-based method of neural graph collaborative filtering (NGCF). The experimental results demonstrate that the proposed method performs significantly better than the NGCF, and the three other matrix factorization methods, namely, the singular value decomposition (SVD), SVD++, and the social matrix factorization (SocialMF).

IMPROVING LATENT FACTOR MODEL BASED COLLABORATIVE FILTERING VIA INTEGRATED FOLKSONOMY FACTORS

2011 ◽  
Vol 19 (02) ◽  
pp. 307-327 ◽  
Author(s):  
LUO XIN ◽  
YUANXIN OUYANG ◽  
XIONG ZHANG
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Factor Model ◽  
State Of The Art ◽  
Latent Factors ◽  
Latent Factor ◽  
Rating Data ◽  
Latent Factor Model ◽  
Probabilistic Matrix Factorization ◽  
Recommendation Accuracy

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.

scholarly journals Metric Factorization with Item Cooccurrence for Recommendation

Symmetry ◽  
2020 ◽  
Vol 12 (4) ◽  
pp. 512 ◽  
Author(s):  
Honglin Dai ◽  
Liejun Wang ◽  
Jiwei Qin
Keyword(s):  
Matrix Factorization ◽  
Euclidean Distance ◽  
Metric Learning ◽  
Sparse Data ◽  
Inner Product ◽  
Interaction Matrix ◽  
Latent Factors ◽  
Rating Prediction ◽  
Real World Datasets ◽  
Item Item

In modern recommender systems, matrix factorization has been widely used to decompose the user–item matrix into user and item latent factors. However, the inner product in matrix factorization does not satisfy the triangle inequality, and the problem of sparse data is also encountered. In this paper, we propose a novel recommendation model, namely, metric factorization with item cooccurrence for recommendation (MFIC), which uses the Euclidean distance to jointly decompose the user–item interaction matrix and the item–item cooccurrence with shared latent factors. The item cooccurrence matrix is obtained from the colike matrix through the calculation of pointwise mutual information. The main contributions of this paper are as follows: (1) The MFIC model is not only suitable for rating prediction and item ranking, but can also well overcome the problem of sparse data. (2) This model incorporates the item cooccurrence matrix into metric learning so it can better learn the spatial positions of users and items. (3) Extensive experiments on a number of real-world datasets show that the proposed method substantially outperforms the compared algorithm in both rating prediction and item ranking.

IMPROVING MATRIX FACTORIZATION-BASED RECOMMENDER VIA ENSEMBLE METHODS

2011 ◽  
Vol 10 (03) ◽  
pp. 539-561 ◽  
Author(s):  
XIN LUO ◽  
YUANXIN OUYANG ◽  
XIONG ZHANG
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Prediction Accuracy ◽  
Ensemble Methods ◽  
Training Sequences ◽  
Forward Stepwise ◽  
Additive Regression ◽  
Training Examples ◽  
Homogeneous Ensemble

One of the most popular approaches to Collaborative Filtering is based on Matrix Factorization (MF). In this paper, we focus on improving MF-based recommender's accuracy by homogeneous ensemble methods. To build such ensembles, we investigate a series of methods primarily in two aspects: (i) manipulating the training examples, including bagging, AdaBoost, and Forward Stepwise Additive Regression; (ii) injecting randomness to the base models' training settings, including randomizing the initializing parameters and randomizing the training sequences. Each method is evaluated on two large, real datasets, and then the effective methods are combined to form a cascade MF ensemble scheme. The validation results on experiment datasets demonstrate that compared to a single MF-based recommender, our ensemble scheme could obtain a significant improvement in the prediction accuracy.

Collaboration Matrix Factorization on Rate and Review for Recommendation

2019 ◽  
Vol 30 (2) ◽  
pp. 27-43
Author(s):  
Zhicheng Wu ◽  
Huafeng Liu ◽  
Yanyan Xu ◽  
Liping Jing
Keyword(s):  
Neural Networks ◽  
Recommender Systems ◽  
Projection Method ◽  
Matrix Factorization ◽  
Auxiliary Information ◽  
Latent Factors ◽  
Latent Factor ◽  
Real World Datasets ◽  
Rating Information

According to the sparseness of rating information, the quality of recommender systems has been greatly restricted. In order to solve this problem, much auxiliary information has been used, such as social networks, review information, and item description. Convolutional neural networks (CNNs) have been widely employed by recommender systems, it greatly improved the rating prediction's accuracy especially when combined with traditional recommendation methods. However, a large amount of research focuses on the consistency between the rating-based latent factor and review-based latent factor. But in fact, these two parts are completely different. In this article, the authors propose a model named collaboration matrix factorization (CMF) that combines a projection method with a convolutional matrix factorization (ConvMF) to extract the collaboration between rating-based latent factors and review-based latent factors that comes from the results of the CNN process. Extensive experiments on three real-world datasets show that the projection method achieves significant improvements over the existing baseline.

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

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.

scholarly journals Improving the Prediction Quality in Memory-Based Collaborative Filtering Using Categorical Features

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 214
Author(s):  
Lei Chen ◽  
Yuyu Yuan ◽  
Jincui Yang ◽  
Ahmed Zahir
Keyword(s):  
Collaborative Filtering ◽  
Recommender Systems ◽  
Prediction Accuracy ◽  
Nearest Neighbor ◽  
Side Information ◽  
Weight Matrix ◽  
K Nearest Neighbor ◽  
Proposed Model ◽  
Real World Datasets ◽  
Better Than

Despite years of evolution of recommender systems, improving prediction accuracy remains one of the core problems among researchers and industry. It is common to use side information to bolster the accuracy of recommender systems. In this work, we focus on using item categories, specifically movie genres, to improve the prediction accuracy as well as coverage, precision, and recall. We derive the user’s taste for an item using the ratings expressed. Similarly, using the collective ratings given to an item, we identify how much each item belongs to a certain genre. These two vectors are then combined to get a user-item-weight matrix. In contrast to the similarity-based weight matrix in memory-based collaborative filtering, we use user-item-weight to make predictions. The user-item-weights can be used to explain to users why certain items have been recommended. We evaluate our proposed method using three real-world datasets. The proposed model performs significantly better than the baseline methods. In addition, we use the user-item-weight matrix to alleviate the sparsity problem associated with correlation-based similarity. In addition to that, the proposed model has a better computational complexity for making predictions than the k-nearest neighbor (kNN) method.

Process design and equipment selection for optimized steam and power concepts – the “zero” process steam sugar factory

2020 ◽  
pp. 40-50
Author(s):  
Boris Morgenroth ◽  
Thomas Stark ◽  
Julian Pelster ◽  
Harjeet Singh Bola
Keyword(s):  
Process Design ◽  
Sugar Industry ◽  
Electrical Power ◽  
Power Requirement ◽  
Equipment Selection ◽  
Good Potential ◽  
Set Up ◽  
The Right ◽  
Key Aspects ◽  
Steam Drying

Optimization of process steam requirement in order to maximize sugar recovery and export power along with manpower optimization is a must for sugar factories to survive under difficult conditions and to earn additional revenues. The process steam demand of greenfield and revamped plants has been reduced to levels of 32–38% from originally more than 50% steam on cane in the case of the brownfield plants. In addition, significant improvement in the power requirement of the plants has been achieved. Bagasse drying offers a good potential to improve the power export. Different available concepts are compared with a focus on bagasse steam drying and low temperature bagasse drying. In order to set up an optimized highly efficient plant or to optimize an existing plant to achieve competitive benchmarks, good process design and the right equipment selection are very important. Experience has been gained with multiple stage or double effect crystallization in the beet sugar industry offering further steam optimization potential. Vapour recompression is also an option to substitute live steam by electrical power. This even provides options to reduce the steam demand from the power plant for the sugar process down to zero. Key aspects concerning the process design and equipment selection are described.

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