IceBreaker: Solving Cold Start Problem for Video Recommendation Engines

Kumar, Yaman; Sharma, Anuradha; Khaund, Abhigyan; Kumar, Akash; Kumaraguru, Ponnurangam; Shah, Rajiv Ratn; Zimmermann, Roger

doi:10.1109/ism.2018.000-3

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…Y. Kumar et al (2019) [34] provide a video recommendation system that overcomes the cold start problem and makes use of intrinsic information to suggest films. The deep neural network of the architecture is fed with the output of the C3D model, mainly video-level characteristics.…”

Section: Movielensmentioning

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%

See 1 more Smart Citation

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 First, systematic reviews on how to deal with the user cold start problem are scarce or outdated.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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Section: Movielensmentioning

confidence: 99%

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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“…For example, without enough user history, Facebook would use friends with similar interests to alleviate the cold-start problem. But, if the information is little, the system is still unable to make recommendations [24][25][26][27].…”

Section: Problem Statementmentioning

confidence: 99%

Accuracy improvements for cold-start recommendation problem using indirect relations in social networks

Tey

Lin

et al. 2021

J Big Data

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Recent advances in Internet applications have facilitated information spreading and, thanks to a wide variety of mobile devices and the burgeoning 5G networks, users easily and quickly gain access to information. Great amounts of digital information moreover have contributed to the emergence of recommender systems that help to filter information. When the rise of mobile networks has pushed forward the growth of social media networks and users get used to posting whatever they do and wherever they visit on the Web, such quick social media updates already make it difficult for users to find historical data. For this reason, this paper presents a social network-based recommender system. Our purpose is to build a user-centered recommender system to exclude the products that users are disinterested in according to user preferences and their friends' shopping experiences so as to make recommendations effective. Since there might be no corresponding reference value for new products or services, we use indirect relations between friends and “friends’ friends” as well as sentinel friends to improve the recommendation accuracy. The simulation result has proven that our proposed mechanism is efficient in enhancing recommendation accuracy.

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