A novel hybrid approach towards movie recommender systems

Bahl, Dushyant; Kain, Vaibhav; Sharma, Akshay; Sharma, Mugdha

doi:10.1080/09720510.2020.1799503

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…MF Algorithm. MF is the mapping of both users and items into the same d-dimensional hidden factor space [17]. Te hidden factor corresponding to user p is denoted as U p ∈ R d , and the matrix consisting of the hidden factors of all users is denoted as U. Te hidden factor of item x is denoted as V x ∈ R d , and the matrix consisting of the hidden factors of all items is denoted as V. MF is shown in Figure 3.…”

Section: Methodsmentioning

confidence: 99%

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A Normative Approach to Privacy-Preserving Recommender Systems: Integrating Matrix Factorization and Genetic Algorithms

He,

2023

International Journal of Intelligent Systems

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As recommendation systems heavily depend on user data, these systems are susceptible to potential privacy breaches. To mitigate this issue, differential privacy (DP) protection techniques have been developed to offer robust privacy safeguards. Nevertheless, a majority of the extant DP-based recommendation algorithms tend to introduce excessive noise, consequently impairing the quality of recommendations. In response, this study presents a novel DP-preserving recommendation algorithm that integrates matrix factorization (MF) and a genetic algorithm (GA). Initially, the MF problem is transformed into two interrelated optimization problems, namely, the user-hidden factor and the item-hidden factor. Subsequently, GA is employed to address these optimization issues. An enhancement index mechanism is incorporated into the individual selection of GA, while the variation process of GA is devised based on identifying crucial hidden factors. Utilizing the enhancement index mechanism aids in minimizing the algorithm’s perturbation level, thereby achieving an optimal balance between privacy protection and algorithm utility. Experimental analyses, encompassing recommendation accuracy, efficiency, and parameter variation effects, are conducted on Last.fm and Flixster datasets. The findings corroborate that the proposed system outperforms existing alternatives under stringent privacy constraints, thereby attesting to its efficacy.

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Section: Methodsmentioning

confidence: 99%

“…MF and CF. Usually, recommendation systems can be classifed into three categories as follows: content-based recommendations, CF-based recommendations, and hybrid approach-based recommendations [17]. Te specifc classifcation is shown in Figure 1.…”

Section: State Of the Artmentioning

confidence: 99%

A Normative Approach to Privacy-Preserving Recommender Systems: Integrating Matrix Factorization and Genetic Algorithms

He,

2023

International Journal of Intelligent Systems

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“…Hybrid recommender systems combine both content-based recommendation and collaborative filtering to make predictions [28], [47]. Generally, four classes of approaches have been used to construct hybrid recommender systems.…”

Section: B Online Group Recommender Systemmentioning

confidence: 99%

Group Recommendation Based on Heterogeneous Graph Algorithm for EBSNs

Huang

et al. 2023

IEEE Access

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Emerging event-based social networks (EBSNs), such as Meetup, have grown rapidly and become popular in recent years. EBSNs differ from conventional social networks such as Facebook in that they not only involve online social interactions but also include offline, in-person interactions. Thus, EBSNs are naturally heterogeneous and possess more valuable social information. Group recommendations in EBSNs are typically only based on the interest information filled in by users, or friends' group information. Both these methods may not well reflect users' real intentions. In this study, we propose a recommender system to predict groups that may interest EBSN users, based on a novel heterogeneous augmented graph method and a random walk with restart algorithm. In this approach, online and offline social interactions are combined into a single heterogeneous augmented graph capturing all useful relationships, including user-to-group relationships, user-to-event relationships, user-to-attribute relationships, and groupto-attribute relationships, and among others. To our knowledge, this work is the first attempt to apply a random walk algorithm into group recommendation in EBSNs. Extensive experiments on Meetup datasets demonstrate that our proposed recommender system achieves better results in terms of recall, precision, F-Measure and MRR metrics in comparison with the other four commonly used algorithms, including random recommendation, interest-based recommendation, interest-and neighborhood-based recommendation, and Katz Centrality. The significant recommendation performance of our approach may further enhance user satisfaction of EBSNs. Moreover, our approach to group recommendation may also be extended to other recommendation-related applications such as event or friend recommendation.

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