Recommender systems

Alyari, Fatemeh; Navimipour, Nima Jafari

doi:10.1108/k-06-2017-0196

Cited by 81 publications

(36 citation statements)

References 150 publications

(174 reference statements)

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“…The NMF personalized recommendation algorithm combining the scoring matrix and the user's subjective preference is to extract and generate each user's comment [ 23 ] and calculate the weight, as shown in Table 6 :…”

Section: Research On the Recommendation Algorithm Of Scoring Matrix A...mentioning

confidence: 99%

Personalized Recommendation Algorithm for Movie Data Combining Rating Matrix and User Subjective Preference

Liu¹

2022

Computational Intelligence and Neuroscience

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The film industry has also caught the fast train of Internet development. Various movie resources have come into view. Users need to spend a lot of time searching for movies they are interested in. This method wastes time and is very bad. The article proposes an NMF personalized movie recommendation algorithm, which can recommend movies to users based on their historical behavior and preference. The research results of the article show the following: (1) the experiment counts movie reviews of different users in the same time span. The results show that 48.42% of users have only commented on a movie once, 79.76% of users have posted less than or equal to 5 comments, and 89.92% of user reviews have posted less than or equal to 10 times. (2) In the comparative experiments of the NMF algorithm in different dimensions, the effect of the NMF-E algorithm is much better than that of the NMF-A algorithm. The accuracy, recall, and F1 value of the NME-E algorithm are all 3 types. The experimental results show that the NME-E algorithm is the best among all algorithms. (3) In the experiment to test the effectiveness of the NMF personalized recommendation algorithm, comparing the experimental results, the MAE value of the improved NMF personalized recommendation algorithm is lower than that of the unimproved algorithm. When the number of neighbors is 10, the highest value of the improved MAE of the previous algorithm is 0.837. After the improved algorithm, the MAE value is the highest (0.83), and the MAE value has dropped by 0.007, indicating that the error is smaller after the improved algorithm, and the result of recommending movies is more accurate. The recall value of the four algorithms will increase as the number of neighbors increases. Among them, the recall value of the NMF algorithm proposed in the article is the highest among several algorithms. The highest value can reach 0.200, which is higher than the highest value of other algorithms. It shows that the recommendation effect of NMF algorithm is the best. (4) According to the results of the questionnaire, after using the NMF personalized recommendation algorithm, users’ satisfaction increased from 20% to 50%, an increase of 30%, and their dissatisfaction decreased from 15% to 8%, a decrease of 7%. Relative satisfaction increased from 52% to 55%, an increase of 3%, satisfaction increased from 35% to 60%, an increase of 25%, and dissatisfaction decreased from 40% to 20%, a decrease of 20%, indicating that the algorithm can meet the requirements of most people.

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Section: Research On the Recommendation Algorithm Of Scoring Matrix A...mentioning

confidence: 99%

Personalized Recommendation Algorithm for Movie Data Combining Rating Matrix and User Subjective Preference

Liu¹

2022

Computational Intelligence and Neuroscience

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“…Recommendation systems are information filtering systems, developed to retrieve a list of similar results to users’ interests by recommending the most appropriate posts by considering user’s interactions with each other (Ishida et al , 2017; Alyari and Jafari Navimipour, 2018).…”

Section: Related Workmentioning

confidence: 99%

Incorporating LDA with LSTM for followee recommendation on Twitter network

Dib

Kalloubi

Nfaoui

et al. 2021

IJWIS

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Purpose The purpose of this study is to facilitate the task of finding appropriate information to read about, and searching for people who are in the same field of interest. Knowing that more people keep up with new streaming information on Twitter micro-blogging service. With the immense number of micro-posts shared via the follower/followee network graph, Twitter users find themselves in front of millions of tweets, which makes the task crucial. Design/methodology/approach In this paper, a long short–term memory (LSTM) model that relies on the latent Dirichlet allocation (LDA) output vector for followee recommendation, the LDA model applied as a topic modeling strategy is proposed. Findings This study trains the model using a real-life data set extracted based on Twitter follower/followee architecture. It confirms the effectiveness and scalability of the proposed approach. The approach improves the state-of-the-art models average-LSTM and time-LSTM. Research limitations/implications This study improves mainly the existing followee recommendation systems. Because, unlike previous studies, it applied a non-hand-crafted method which is the LSTM neural network with LDA model for topics extraction. The main limitation of this study is the cold-start users cannot be treated, also some active fake accounts may not be detected. Practical implications The aim of this approach is to assist users seeking appropriate information to read about, by choosing appropriate profiles to follow. Social implications This approach consolidates the social relationship between users in a microblogging platform by suggesting like-minded people to each other. Thus, finding users with the same interests will be easy without spending a lot of time seeking relevant users. Originality/value Instead of classic recommendation models, the paper provides an efficient neural network searching method to make it easier to find appropriate users to follow. Therefore, affording an effective followee recommendation system.

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“…User-item interactions, such as ratings or buying behaviour, attribute information about users and items such as textual profiles or relevant keywords, are fundamental input data to feed basic models of RS. Collaborative filtering (CF) methods use the user-item interactions (Schafer et al 2007, Alyari andJafari Navimipour 2018), and content-based filtering (CBF) approaches employ user and item features (Lops et al 2011). Specifying user requirements and external knowledge bases and constraints are essential information for KBF methods (Burke 2000).…”

Section: Introductionmentioning

confidence: 99%

Image-based Product Recommendation Method for E-commerce Applications Using Convolutional Neural Networks

Alamdari¹,

Navimipour²,

Hosseinzadeh³

et al. 2022

AIP

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Recommender systems (RS) are designed to eliminate the information overload problem in today's e-commerce platforms and other data-centric online services. They help users explore and exploit the system's information environment utilizing implicit and explicit data from internal e-commerce systems and user interactions. Today's product catalogues include pictures to provide visual detail at a glance. This approach can effectively convert potential buyers into customers. Since most e-commerce stores use product images to promote, arouse users' visual desires and encourage them to buy products, this paper develops an image-based RS using deep learning techniques. To perform the research, we use five convolutional neural network (CNN) models to extract the features of the products' images. Then, the system uses the features to calculate the similarity between images. The selected CNN models are VGG16, VGG19, ResNet50, Inception V3 and Xception. We also analysed four versions of the MovieLens dataset to demonstrate the accuracy improvement of the recommendations, including 100k, 1M, 10M and 20M. Results of the experiment showed a significant increase in accuracy compared with traditional approaches. Also, we express many related open issues including use of multiple images per item, different similarity metrics, other CNN models, and the hybridization of image-based and different RS techniques for future studies. This method also provides more accurate product recommendations on e-commerce platforms than traditional methods.

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Recommender systems

Cited by 81 publications

References 150 publications

Personalized Recommendation Algorithm for Movie Data Combining Rating Matrix and User Subjective Preference

Personalized Recommendation Algorithm for Movie Data Combining Rating Matrix and User Subjective Preference

Incorporating LDA with LSTM for followee recommendation on Twitter network

Image-based Product Recommendation Method for E-commerce Applications Using Convolutional Neural Networks

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