scholarly journals Recommendation Based on Trust Diffusion Model

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Jinfeng Yuan ◽  
Li Li
Keyword(s):  
Matrix Factorization ◽  
High Performance ◽  
Recommendation System ◽  
Online Information ◽  
Trust Network ◽  
Data Sparsity ◽  
Pmf Model ◽  
Probabilistic Matrix Factorization ◽  
Sparsity Problem ◽  
Relationship Of

Recommender system is emerging as a powerful and popular tool for online information relevant to a given user. The traditional recommendation system suffers from the cold start problem and the data sparsity problem. Many methods have been proposed to solve these problems, but few can achieve satisfactory efficiency. In this paper, we present a method which combines the trust diffusion (DiffTrust) algorithm and the probabilistic matrix factorization (PMF). DiffTrust is first used to study the possible diffusions of trust between various users. It is able to make use of the implicit relationship of the trust network, thus alleviating the data sparsity problem. The probabilistic matrix factorization (PMF) is then employed to combine the users' tastes with their trusted friends' interests. We evaluate the algorithm on Flixster, Moviedata, and Epinions datasets, respectively. The experimental results show that the recommendation based on our proposed DiffTrust + PMF model achieves high performance in terms of the root mean square error (RMSE), Recall, andFMeasure.

scholarly journals A New Weighted-learning Approach for Exploiting Data Sparsity in Tag-based Item Recommendation Systems

2021 ◽  
Vol 14 (1) ◽  
pp. 387-399
Author(s):  
Noor Ifada ◽  
◽  
Richi Nayak ◽  
Keyword(s):  
Recommendation System ◽  
Recommendation Systems ◽  
Learning Approach ◽  
Tensor Model ◽  
Learning Approaches ◽  
Data Sparsity ◽  
Tensor Models ◽  
Sparsity Problem ◽  
Weighted Rank

The tag-based recommendation systems that are built based on tensor models commonly suffer from the data sparsity problem. In recent years, various weighted-learning approaches have been proposed to tackle such a problem. The approaches can be categorized by how a weighting scheme is used for exploiting the data sparsity – like employing it to construct a weighted tensor used for weighing the tensor model during the learning process. In this paper, we propose a new weighted-learning approach for exploiting data sparsity in tag-based item recommendation system. We introduce a technique to represent the users’ tag preferences for leveraging the weighted-learning approach. The key idea of the proposed technique comes from the fact that users use different choices of tags to annotate the same item while the same tag may be used to annotate various items in tag-based systems. This points out that users’ tag usage likeliness is different and therefore their tag preferences are also different. We then present three novel weighting schemes that are varied in manners by how the ordinal weighting values are used for labelling the users’ tag preferences. As a result, three weighted tensors are generated based on each scheme. To implement the proposed schemes for generating item recommendations, we develop a novel weighted-learning method called as WRank (Weighted Rank). Our experiments show that considering the users' tag preferences in the tensor-based weightinglearning approach can solve the data sparsity problem as well as improve the quality of recommendation.

scholarly journals Information Fusion-Based Deep Neural Attentive Matrix Factorization Recommendation

Algorithms ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 281
Author(s):  
Zhen Tian ◽  
Lamei Pan ◽  
Pu Yin ◽  
Rui Wang
Keyword(s):  
Information Fusion ◽  
Matrix Factorization ◽  
Recommendation System ◽  
Information Overload ◽  
High Order Interaction ◽  
Attribute Information ◽  
Sparsity Problem ◽  
The Rich ◽  
Social Demographic ◽  
Fully Connected

The emergence of the recommendation system has effectively alleviated the information overload problem. However, traditional recommendation systems either ignore the rich attribute information of users and items, such as the user’s social-demographic features, the item’s content features, etc., facing the sparsity problem, or adopt the fully connected network to concatenate the attribute information, ignoring the interaction between the attribute information. In this paper, we propose the information fusion-based deep neural attentive matrix factorization (IFDNAMF) recommendation model, which introduces the attribute information and adopts the element-wise product between the different information domains to learn the cross-features when conducting information fusion. In addition, the attention mechanism is utilized to distinguish the importance of different cross-features on prediction results. In addition, the IFDNAMF adopts the deep neural network to learn the high-order interaction between users and items. Meanwhile, we conduct extensive experiments on two datasets: MovieLens and Book-crossing, and demonstrate the feasibility and effectiveness of the model.

Enhanced PMF Model to Predict User Interest for Web API Recommendation

2022 ◽  
pp. 131-139
Author(s):  
T. Ramathulasi ◽  
M. Rajasekhar Babu
Keyword(s):  
Matrix Factorization ◽  
Short Term Memory ◽  
Contextual Information ◽  
Factorization Method ◽  
Experimental Results ◽  
User Interest ◽  
Pmf Model ◽  
Probabilistic Matrix Factorization ◽  
Memory Network ◽  
Latent Attribute

Many methods focus solely on the relationship between the API and the user and fail to capture their contextual value. Because of this, they could not get better accuracy. The accuracy of the API recommendation can be improved by considering the effect of API contextual information on their latent attribute and the effect of the user time factor on the latent attribute of the user through the deep learning-based matrix factorization method (DL-PMF). In this chapter, a CNN (convolutional neural network) with an attention mechanism for the hidden features of web API elements and an LSTM (long-term and short-term memory) network is introduced to find the hidden features of service users. Finally, the authors combined PMF (probabilistic matrix factorization) to estimate the value of the recommended results. Experimental results obtained by the DL-PMF method show better than the experimental results obtained by the PMF and the ConvMF (convolutional matrix factorization) method in the recommended accuracy.

Integrating Sentiment Analysis on Hybrid Collaborative Filtering Method in a Big Data Environment

2020 ◽  
Vol 19 (02) ◽  
pp. 385-412 ◽  
Author(s):  
P. Shanmuga Sundari ◽  
M. Subaji
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Semantic Analysis ◽  
Least Square ◽  
Data Sparsity ◽  
Filtering Method ◽  
Sparsity Problem ◽  
Hybrid Collaborative Filtering ◽  
User Ratings ◽  
Hybrid Recommendation

Most of the traditional recommendation systems are based on user ratings. Here, users provide the ratings towards the product after use or experiencing it. Accordingly, the user item transactional database is constructed for recommendation. The rating based collaborative filtering method is well known method for recommendation system. This system leads to data sparsity problem as the user is unaware of other similar items. Web cataloguing service such as tags plays a significant role to analyse the user’s perception towards a particular product. Some system use tags as additional resource to reduce the data sparsity issue. But these systems require lot of specific details related to the tags. Existing system either focuses on ratings or tags based recommendation to enhance the accuracy. So these systems suffer from data sparsity and efficiency problem that leads to ineffective recommendations accuracy. To address the above said issues, this paper proposed hybrid recommendation system (Iter_ALS Iterative Alternate Least Square) to enhance the recommendation accuracy by integrating rating and emotion tags. The rating score reveals overall perception of the item and emotion tags reflects user’s feelings. In the absence of emotional tags, scores found in rating is assumed as positive or negative emotional tag score. Lexicon based semantic analysis on emotion tags value is adopted to represent the exclusive value of tag. Unified value is represented into Iter_ALS model to reduce the sparsity problem. In addition, this method handles opinion bias between ratings and tags. Experiments were tested and verified using a benchmark project of MovieLens dataset. Initially this model was tested with different sparsity levels varied between 0%-100 percent and the results obtained from the experiments shows the proposed method outperforms with baseline methods. Further tests were conducted to authenticate how it handles opinion bias by users before recommending the item. The proposed method is more capable to be adopted in many real world applications

scholarly journals Recommendation System Using Autoencoders

2020 ◽  
Vol 10 (16) ◽  
pp. 5510 ◽  
Author(s):  
Diana Ferreira ◽  
Sofia Silva ◽  
António Abelha ◽  
José Machado
Keyword(s):  
Collaborative Filtering ◽  
Recommendation System ◽  
Mean Squared Error ◽  
Singular Value ◽  
High Dimensions ◽  
Data Sparsity ◽  
Filtering Method ◽  
Squared Error ◽  
Sparsity Problem ◽  
Value Decomposition

The magnitude of the daily explosion of high volumes of data has led to the emergence of the Big Data paradigm. The ever-increasing amount of information available on the Internet makes it increasingly difficult for individuals to find what they need quickly and easily. Recommendation systems have appeared as a solution to overcome this problem. Collaborative filtering is widely used in this type of systems, but high dimensions and data sparsity are always a main problem. With the idea of deep learning gaining more importance, several works have emerged to improve this type of filtering. In this article, a product recommendation system is proposed where an autoencoder based on a collaborative filtering method is employed. A comparison of this model with the Singular Value Decomposition is made and presented in the results section. Our experiment shows a very low Root Mean Squared Error (RMSE) value, considering that the recommendations presented to the users are in line with their interests and are not affected by the data sparsity problem as the datasets are very sparse, 0.996. The results are quite promising achieving an RMSE value of 0.029 in the first dataset and 0.010 in the second one.

A Recommender System for Inverse Design of Polycarbonates and Polyesters

2020 ◽  
Author(s):  
Nathaniel Park ◽  
Dmitry Yu. Zubarev ◽  
James L. Hedrick ◽  
Vivien Kiyek ◽  
Christiaan Corbet ◽  
...  
Keyword(s):  
Ring Opening ◽  
High Performance ◽  
Recommendation System ◽  
Polymeric Materials ◽  
Monomer Conversion ◽  
Design Strategy ◽  
Ring Opening Polymerization ◽  
Inverse Design ◽  
False Negatives ◽  
Accelerate Development

The convergence of artificial intelligence and machine learning with material science holds significant promise to rapidly accelerate development timelines of new high-performance polymeric materials. Within this context, we report an inverse design strategy for polycarbonate and polyester discovery based on a recommendation system that proposes polymerization experiments that are likely to produce materials with targeted properties. Following recommendations of the system driven by the historical ring-opening polymerization results, we carried out experiments targeting specific ranges of monomer conversion and dispersity of the polymers obtained from cyclic lactones and carbonates. The results of the experiments were in close agreement with the recommendation targets with few false negatives or positives obtained for each class.<br>

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