A novel model based collaborative filtering recommender system via truncated ULV decomposition

Horasan, Fahrettin; Yurttakal, Ahmet Haşim; Gündüz, Selçuk

doi:10.1016/j.jksuci.2023.101724

Cited by 7 publications

(3 citation statements)

References 51 publications

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“…As depicted in Figure 2, the training and validation RMSE for the MovieLens 100K dataset showed consistent improvement over epochs, validating the effectiveness of the TKGRS algorithm. We benchmarked TKGRS against the existing state-of-the-art models, i.e., LFM-SPE [28] GHRS [29], Glocal-K [30], MG-GAT [31], T-ULVD [32], JK-DMC [33], SparseFC [34], and CF-NADE [35], as shown in Table 2. As indicated in Table 2, TKGRS not only validated the efficacy of integrating GCNs and temporal decay factors but also set a new performance standard.…”

Section: Methodsmentioning

confidence: 99%

Temporal-Guided Knowledge Graph-Enhanced Graph Convolutional Network for Personalized Movie Recommendation Systems

Chen,

Huang

2023

Future Internet

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Traditional movie recommendation systems are increasingly falling short in the contemporary landscape of abundant information and evolving user behaviors. This study introduced the temporal knowledge graph recommender system (TKGRS), a ground-breaking algorithm that addresses the limitations of existing models. TKGRS uniquely integrates graph convolutional networks (GCNs), matrix factorization, and temporal decay factors to offer a robust and dynamic recommendation mechanism. The algorithm’s architecture comprises an initial embedding layer for identifying the user and item, followed by a GCN layer for a nuanced understanding of the relationships and fully connected layers for prediction. A temporal decay factor is also used to give weightage to recent user–item interactions. Empirical validation using the MovieLens 100K, 1M, and Douban datasets showed that TKGRS outperformed the state-of-the-art models according to the evaluation metrics, i.e., RMSE and MAE. This innovative approach sets a new standard in movie recommendation systems and opens avenues for future research in advanced graph algorithms and machine learning techniques.

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Section: Methodsmentioning

confidence: 99%

Temporal-Guided Knowledge Graph-Enhanced Graph Convolutional Network for Personalized Movie Recommendation Systems

Chen,

Huang

2023

Future Internet

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“…The site uses various recommender algorithms, including collaborative filtering algorithms like item-item, user-user, and regulated SVD. Additionally, to overcome the cold-start problem for new users, MovieLens uses a preference elicitation method [101]. The system asks new users to rate how much they enjoyed watching different groups of films (for example, films with dark humor versus romantic comedies).…”

Section: Datasetsmentioning

confidence: 99%

Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets

Saifudin,

Widiyaningtyas

2024

IEEE Access

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Recommender systems are needed with the presence of the internet and social media. The benefits that are felt in the recommender system can make it easier for users to find suitable products and recommend other products, specifically with lots of information. Recommender systems continue to develop over time. This has led many researchers to continue to find the latest approach and evaluation techniques by comparing the performance of previously existing recommender systems. The main approaches that are often used in recommender systems are Content-Based Filtering (CBF), Collaborative Filtering (CF), and Hybrid Filtering (HBF). This time, we focus on conducting a Systematic Literature Review (SLR) of several research articles and analyzing methods for algorithms developed in building recommender systems. The SLR method consists of three stages: planning, implementation, and reporting. The research used as a comparison is between 2019-2023 using various existing data sets. There were 72 primary studies where the Collaborative Filtering method was used in 46 studies, Content-Based Filtering was used in 11 studies, and the Hybrid Filtering method was used in 15 studies. The results of this SLR process show the advantages and disadvantages of each method and type of evaluation developed in building a recommender system. Apart from that, several challenges arise with various existing problems. However, the model-based collaborative filtering method is one method that can minimize the problems of cold start, data sparsity, and scalability.

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“…Some computer science methods proposed and applied to recommender systems by authors in previous studies include particle swam optimization, deep reinforcement learning, natural language processing, knowledge graph, collaborative filtering and genetic algorithm [20][21][22][23][24][25][26][27][28]. Others include hybrid deep learning and other artificial intelligence models such as fuzzy logic recommender systems [29][30][31].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Social Filtering Model for Event Recommendation Services

Oyemade,

Aworonye

2024

AJAI

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In the contemporary time, technology has made the determination and discovery of human preferences, priorities and personal inclinations possible through the use of recommender systems. Activities of users on the internet can be monitored, extracted, stored, analyzed and used by the recommender systems for suggesting future events to users on the web. This paper aims at developing and analyzing a model for event services recommendation for visitors to events. Event seekers, organizers and event service providers get notified, plan and book for upcoming events from their comfort zones without hassles of gallivanting nooks and crannies to enquire about prospective events. There is not any compelling need to interface with under-enthusiasts and intermediaries in the course of organizing, visiting and providing services for an event. However, it is obvious that massive amount of available information on the web exhibit frustrating attributes, hence it is increasingly a difficult task for users to find the content of interest; in other words, a huge chunk of information undiscovered on the network is left behind as “dark information”. In context, event service recommendation uses deep learning social filtering base techniques which adopt similarity computation measures with a bias for Pearson correlation coefficient, cosine similarity, and Euclidean similarity to recommend related and most relevant events/services to the targeted online audience. In this paper, the aim is to develop a deep learning model which integrates social filtering technique for enhancing the quality of event recommendation for users. A model based on the deep learning algorithm of multilayered perceptron and Neural Collaborative Filtering is proposed for event recommender services. The results from various simulations using meetup website dataset shows that the proposed model performs better than other techniques. The results yield 70% accuracy, 66% precision and 98% recall.

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A novel model based collaborative filtering recommender system via truncated ULV decomposition

Cited by 7 publications

References 51 publications

Temporal-Guided Knowledge Graph-Enhanced Graph Convolutional Network for Personalized Movie Recommendation Systems

Temporal-Guided Knowledge Graph-Enhanced Graph Convolutional Network for Personalized Movie Recommendation Systems

Systematic Literature Review on Recommender System: Approach, Problem, Evaluation Techniques, Datasets

Deep Learning Social Filtering Model for Event Recommendation Services

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