Deep Representation Learning using Multilayer Perceptron and Stacked Autoencoder for Recommendation Systems

Yengikand, Amir Khani; Meghdadi, Majid; Ahmadian, Sajad; Jalali, Seyed Mohammad Jafar; Khosravi, Abbas; Nahavandi, Saeid

doi:10.1109/smc52423.2021.9658978

Cited by 18 publications

(11 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CF is the most representative recommendation approach and has been investigated widely [1]- [6], [16]- [24]. In recent years, many methods including removing exposure bias based on causal inference approaches [17], [20], utilizing relevant data such as timestamp [24] and social information [21]- [23], and neural network-based CF to obtain higher expressiveness [3]- [6], [18], [19], [21]- [23] have been investigated.…”

Section: Related Work a Cf Approachmentioning

confidence: 99%

“…In the case of Gaussian-based matrix factorization for example, d(•, •) is a squared error, α Ωi,j (•, •) is inner product, and Ω i,j = ∅ for all pairs of (i, j). In addition, many of the matrix factorization-based CF with technique such as weight reduction for unobserved elements [11] that are used in (6) and removal of exposure bias [17], [20] can be represented in this form. Second, we consider CF using neural networks.…”

Section: E the General Form For Gamma-poisson Hybrid Recommendation A...mentioning

confidence: 99%

“…One of the most representative approaches for recommendation systems is based on collaborative filtering (CF) [1]- [6]. CF-based recommendation systems typically use a useritem matrix composed of feedback from each user for each item.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Variational Autoencoder-Based Hybrid Recommendation With Poisson Factorization for Modeling Implicit Feedback

Tanuma

Matsui

2022

IEEE Access

View full text Add to dashboard Cite

Hybrid recommendation, which is based on collaborative filtering and supplemented with auxiliary content information, is being actively researched due to its ability to overcome the coldstart problem. Many proposed hybrid methods make recommendations using Gaussian distribution-based collaborative filtering even though they handle variables that tend to be non-Gaussian, such as the number of interactions. We present a method that uses a hybrid recommendation framework based on collaborative filtering that models the number of interactions as a Poisson-distributed and variational autoencoder-based content information generation process that shares latent variables with collaborative filtering. As a prior for the shared latent variables, we use a gamma distribution, which is a conjugate prior of a Poisson distribution. An implicit-derivative-based reparameterization trick enables the use of a gamma distribution in a variational autoencoder. The latent variables in the generative model are inferred using the stochastic gradient variational Bayes approach, taking the number of interactions corresponding to users and items and content information as input. In accordance with the inference, unobserved interactions between users and items are predicted for recommendation. The use of a neural-network-based generative model for content information enables the framework to handle various types of content information. Experimental results show that the proposed method utilizes content information effectively for predicting the number of interactions and that it should aid in overcoming the cold-start problem.

show abstract

Section: Related Work a Cf Approachmentioning

confidence: 99%

Section: E the General Form For Gamma-poisson Hybrid Recommendation A...mentioning

confidence: 99%

See 1 more Smart Citation

Variational Autoencoder-Based Hybrid Recommendation With Poisson Factorization for Modeling Implicit Feedback

Tanuma

Matsui

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Internet and mobile technologies make it possible for people to access information anytime and anywhere [1][2][3]. People's lifestyles have been intensively altered by online systems, including social media, e-commerce, and different lifestyle application [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Healthy Food Recommendation Using a Time-Aware Community Detection Approach and Reliability Measurement

Ahmadian

Rostami

Jalali

et al. 2022

Int J Comput Intell Syst

Self Cite

View full text Add to dashboard Cite

Food recommendation systems have been increasingly developed in online food services to make recommendations to users according to their previous diets. Although unhealthy diets may cause challenging diseases such as diabetes, cancer, and premature heart diseases, most of the developed food recommendation systems neglect considering health factors in their recommendation process. This emphasizes the importance of the reliability of the recommendation from the health content perspective. This paper proposes a new food recommendation system based on health-aware reliability measurement. In particular, we develop a time-aware community detection approach that groups users into disjoint sets and utilizes the identified communities as the nearest neighbors set in rating prediction. Then, a novel reliability measurement is introduced by considering both the health and accuracy criteria of predictions to evaluate the reliability of predicted ratings. Also, the unreliable predictions are recalculated by removing ineffective users from the nearest neighbors set. Finally, the recalculated predictions are utilized to generate a list of foods as recommendations. Different experiments on a crawled dataset demonstrate that the proposed method enhances the performance around 7.63%, 6.97%, 7.37%, 15.09%, and 16.17% based on precision, recall, F1, normalized discounted cumulative gain (NDCG), and health metrics, respectively, compared to the second-best model.

show abstract

“…e recommendation model based on the Markov chain (MC) [9] method is one of the early methods of sequential recommendation, which assumes that the user's next action is determined by his historical behavior and transforms the recommendation problem into a sequence prediction problem. In recent years, with the continuous breakthroughs of the deep neural networks (DNN) in the field of artificial intelligence [10][11][12], researchers have tried to introduce a series of deep neural network models into the field of recommendation and have achieved a series of results [13][14][15]. For example, Huang et al [16] combined the traditional MC method and the recurrent neural network (RNN) to optimize the recommendation model and improve the recommendation accuracy.…”

Section: Introductionmentioning

confidence: 99%

An Improved Sequential Recommendation Algorithm based on Short-Sequence Enhancement and Temporal Self-Attention Mechanism

Tang

Shen

et al. 2022

Complexity

View full text Add to dashboard Cite

Sequential recommendation algorithm can predict the next action of a user by modeling the user’s interaction sequence with an item. However, most sequential recommendation models only consider the absolute positions of items in the sequence, ignoring the time interval information between items, and cannot effectively mine user preference changes. In addition, existing models perform poorly on sparse data sets, which make a poor prediction effect for short sequences. To address the above problems, an improved sequential recommendation algorithm based on short-sequence enhancement and temporal self-attention mechanism is proposed in this paper. In the proposed algorithm, a backward prediction model is trained first, to predict the prior items in the user sequence. Then, the reverse prediction model is used to generate a batch of pseudo-historical items before the initial items of the short sequence, to achieve the goal of enhancing the short sequence. Finally, the absolute position information and time interval information of the user sequence are modeled, and a time-aware self-attention model is adopted to predict the user’s next action and generate a recommendation list. Various experiments are conducted on two public data sets. The experimental results show that the method proposed in this paper has excellent performance on both dense and sparse data sets, and its effect is better than that of the state of the art.

show abstract

Deep Representation Learning using Multilayer Perceptron and Stacked Autoencoder for Recommendation Systems

Cited by 18 publications

References 45 publications

Variational Autoencoder-Based Hybrid Recommendation With Poisson Factorization for Modeling Implicit Feedback

Variational Autoencoder-Based Hybrid Recommendation With Poisson Factorization for Modeling Implicit Feedback

Healthy Food Recommendation Using a Time-Aware Community Detection Approach and Reliability Measurement

An Improved Sequential Recommendation Algorithm based on Short-Sequence Enhancement and Temporal Self-Attention Mechanism

Contact Info

Product

Resources

About