An Efficient Deep Learning Approach for Collaborative Filtering Recommender System

Aljunid, Mohammed Fadhel; Dh, Manjaiah

doi:10.1016/j.procs.2020.04.090

Cited by 64 publications

(25 citation statements)

References 22 publications

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“…A movie recommendation system that predicted the Top-N recommendation list of movies using singular value decomposition and cosine similarity is proposed in [43]. In the Deep Learning model for a Collaborative Recommender System (DLCRS) [44], user and movie IDs are concatenated as one-hot vectors. Unlike the basic MF approach, where inner products combine the user and movie ratings, DLCRS performs the element-wise multiplication of user and movie ratings.…”

Section: Literature Reviewmentioning

confidence: 99%

TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation System

et al. 2022

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In recent years, commercial platforms have embraced recommendation algorithms to provide customers with personalized recommendations. Collaborative Filtering is the most widely used technique of recommendation systems, whose accuracy is primarily reliant on the computed similarity by a similarity measure. Data sparsity is one problem that affects the performance of the similarity measures. In addition, most recommendation algorithms do not remove noisy data from datasets while recommending the items, reducing the accuracy of the recommendation. Furthermore, existing recommendation algorithms only consider historical ratings when recommending the items to users, but users’ tastes may change over time. To address these issues, this research presents a Deep Neural Network based on Time Decay (TD-DNN). In the data preprocessing phase of the model, noisy ratings are detected from the dataset and corrected using the Matrix Factorization approach. A power decay function is applied to the preprocessed input to provide more weightage to the recent ratings. This non-noisy weighted matrix is fed into the Deep Learning model, consisting of an input layer, a Multi-Layer Perceptron, and an output layer to generate predicted ratings. The model’s performance is tested on three benchmark datasets, and experimental results confirm that TD-DNN outperforms other existing approaches.

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Section: Literature Reviewmentioning

confidence: 99%

TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation System

et al. 2022

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“…We tested several embedding sizes [4,8,16,32,64]. We plotted the MAE metric vs. the embedding sizes and obtained the results shown in Figure 3.…”

Section: Settings Of Criteria Ratings Dnnmentioning

confidence: 99%

“…DL in RSs is an emerging area of research that shows promising results [8]. The strength of DL lies in its ability to represent complex models that describe user behavior for the recommendation process, and its ability to deal with sparse data in big data systems.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Deep Multi-Criteria Model for Recommender System

Nassar¹

2021

Preprint

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Recommender systems are everywhere books, products, movies, and more. Traditional recommender systems typically use a single criterion in the recommendation, while studies have shown that multi-criteria recommending is more accurate. Novel deep learning techniques have produced remarkable achievements in many fields. The use of such techniques in recommendation systems has started to get attention recently, and several models of recommendation have been proposed based on deep learning. However, there is still no work for using deep learning in hybrid multi-criteria recommender systems. In this work, a model for a hybrid deep multi-criteria recommender system was presented. The model mainly includes two major parts: In the first one, the model obtains the user ID, item ID, and the item metadata to be used as input to a deep neural network in order to predict the criteria ratings. In the second part, the obtained ratings act as an input to another deep neural network, where the overall rating is predicted. Our experiments were conducted on a real-world dataset. They demonstrated the superiority of the proposed novel model over the other models in all measures used to evaluate performance. This indicates the successful use of hybrid deep multi-criteria in the recommendation systems.

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“…There are two main types of CF algorithms, namely memory‐based CF models and model‐based CF algorithms. The memory‐based model uses the historical behaviour data to find a possible association between a user and an item to make recommendations [13]. Memory‐based CF can be divided into two sub‐categories, namely, user‐based CF (UCF) and item‐based CF (ICF).…”

Section: Introductionmentioning

confidence: 99%

An efficient hybrid recommendation model based on collaborative filtering recommender systems

Aljunid

Huchaiah

2021

CAAI Trans on Intel Tech

Self Cite

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In recent years, collaborative filtering (CF) techniques have become one of the most popularly used techniques for providing personalized services to users. CF techniques collect users' previous information about items such as books, music, movies, ideas, and so on. Memory-based models are generally referred to as similarity-based CF models, which are one of the most widely agreeable approaches for providing service recommendations. The memory-based approach includes user-based CF (UCF) and item-based CF (ICF) algorithms. The UCF model recommends items by finding similar users, while the ICF model recommends items by finding similar items based on the user-item rating matrix. However, consequent to the ingrained sparsity of the user-item rating matrix, a large number of ratings are missing. This results in the availability of only a few ratings to make predictions for the unknown ratings. The result is the poor prediction quality of the CF model. A model to find the best algorithm is provided here, which gives the most accurate recommendation based on different similarity metrics. Here a hybrid recommendation model, namely ΓUICF, is proposed. The ΓUICF model integrates the UCF and ICF models with the Γ linear regression model to model the sparsity and scalability issue of the user-item rating matrix. Detailed experimentation on two different real-world datasets shows that the proposed model demonstrates substantial performance when compared with the existing methods.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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An Efficient Deep Learning Approach for Collaborative Filtering Recommender System

Cited by 64 publications

References 22 publications

TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation System

TD-DNN: A Time Decay-Based Deep Neural Network for Recommendation System

A Novel Hybrid Deep Multi-Criteria Model for Recommender System

An efficient hybrid recommendation model based on collaborative filtering recommender systems

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