HyperRS: Hypernetwork-Based Recommender System for the User Cold-Start Problem

Lu, Yuxun; Nakamura, Kôsuke; Ichise, Ryutaro

doi:10.1109/access.2023.3236391

Cited by 7 publications

(6 citation statements)

References 31 publications

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“…In addition, data sparsity always gives rise to coverage problems, namely the percentage of items in the system that can be used as recommendations. [33] .…”

Section: Recommender Systemsmentioning

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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“…In addition, data sparsity always gives rise to coverage problems, namely the percentage of items in the system that can be used as recommendations. [33] .…”

Section: Recommender Systemsmentioning

confidence: 99%

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

Saifudin,

Widiyaningtyas

2024

IEEE Access

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“…Start RS [14] x [15] x [16] x [17] x [18] x [19] x [20] x x x x [21] x [22] x x x [23] x [24] x [9] x [25] x [26] x [27] x [28] x [29] x [30] x [31] x…”

Section: Cold-mentioning

confidence: 99%

“…In Ref. [31] a new system called HyperRS is proposed, which does not rely on demographic information for personalized recommendations. Instead, a hypernetwork generates all weights in the underlying recommender system, allowing weights to adapt quickly to capture user interest in item attributes and their contents.…”

Section: Cold-mentioning

confidence: 99%

Towards Hyper-Relevance in Marketing: Development of a Hybrid Cold-Start Recommender System

Fernandes,

Miguéis,

Pereira

et al. 2023

Applied Sciences

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Recommender systems position themselves as powerful tools in the support of relevance and personalization, presenting remarkable potential in the area of marketing. The cold-start customer problematic presents a challenge within this topic, leading to the need of distinguishing user features and preferences based on a restricted set of transactional information. This paper proposes a hybrid recommender system that aims to leverage transactional and portfolio information as indicating characteristics of customer behaviour. Four independent systems are combined through a parallelised weighted hybrid design. The first individual system utilises the price, target age, and brand of each product to develop a content-based recommender system, identifying item similarities. Secondly, a keyword-based content system uses product titles and descriptions to identify related groups of items. The third system utilises transactional data, defining similarity between products based on purchasing patterns, categorised as a collaborative model. The fourth system distinguishes itself from the previous approaches by leveraging association rules, using transactional information to establish antecedent and precedence relationships between items through a market basket analysis. Two datasets were analysed: product portfolio and transactional datasets. The product portfolio had 17,118 unique products and the included 4,408,825 instances from 2 June 2021 until 2 June 2022. Although the collaborative system demonstrated the best evaluation metrics when comparing all systems individually, the hybridisation of the four systems surpassed each of the individual systems in performance, with a 8.9% hit rate, 6.6% portfolio coverage, and with closer targeting of customer preferences and smaller bias.

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“…Plenty of researchers are currently employing meta-learning to solve the new user issue. [54] Three categories of meta-learning exist: metrics-based, model-based, and optimisation-based. [55] The following Table.…”

Section: ) Approach-driven Techniquesmentioning

confidence: 99%

“…Recall, Precision, F1-Score, AUC [37], [38], [40], [42], [45], [49], [50], [53], [55], [61], [67], [73], [75] Top-K accuracy metric ndcg@K, Hit@K, recall@K, precision@K, AP@K [31], [32], [34]- [38], [46], [49], [52], [54], [55], [57], [58], [60], [63]- [66], [74] Some literature uses only one type of evaluation metric in the performance evaluation process. As in the literature [42] and [53] the AUC was used to evaluate the model.…”

Section: Classification Accuracy Metricmentioning

confidence: 99%

User Cold Start Problem in Recommendation Systems: A Systematic Review

Yuan,

Hernandez

2023

IEEE Access

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The recommendation system makes recommendations based on the preferences of the users. These user preferences usually come from the user's basic information, item rating, historical data, and so on. The "user cold start problem" happens when a new user cannot be appropriately suggested due to a lack of more detailed preference information. In many instances, the user cold start problem hinders the use of the recommendation system. Many researchers are currently trying to discover a solution to the user cold start problem. Unfortunately, there are two drawbacks in the current systematic reviews of how to deal with the user cold start problem. First, systematic reviews on how to deal with the user cold start problem are scarce or outdated. Second, existing reviews lack the distinction between the user cold start problem and the item cold start problem. Nevertheless, the solutions to the two problems differ. To address these problems, our study thorough review of all literature published by researchers from January 2016 to April 2023 about 8 years. Firstly, this study analyzes the literatures on approaches that addressed the user cold start problem during the past eight years and divides them into two categories: data-driven technology and approach-driven technology, and then describes and classifies each type of technology in detail. Secondly, this study also analyzes the main evaluation criteria currently used in these methods to provide a reference for researchers in related fields. Finally, this paper also points out the future research direction of this field.

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HyperRS: Hypernetwork-Based Recommender System for the User Cold-Start Problem

Cited by 7 publications

References 31 publications

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

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

Towards Hyper-Relevance in Marketing: Development of a Hybrid Cold-Start Recommender System

User Cold Start Problem in Recommendation Systems: A Systematic Review

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