A Hybrid Movie Recommendation Method Based on Social Similarity and Item Attributes

Yang, Chen; Chen, Xiaohong; Liu, Lei; Liu, Tingting; Geng, Shuang

doi:10.1007/978-3-319-93818-9_26

Cited by 10 publications

(5 citation statements)

References 6 publications

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“…In the recommendation algorithm they used, tagging information is used to infer user preferences, especially when there is little or no information about the user (cold start new user), and tags can provide some clues for recommendations [27]. In addition, Yang et al [28] proposed a hybrid algorithm that combines reviews with textual information of movies to achieve recommendations. In this case, the textual information is represented by movie labels and genres.…”

Section: Hybrid Recommendation Algorithmmentioning

confidence: 99%

A Personalized Short Video Recommendation Method Based on Multimodal Feature Fusion

Lin

Wang

et al. 2022

Preprint

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In order to achieve accurate parsing of multiple elements of short videos, multimodal short video processing with text, image, and speech as the three elements needs to be realized. Specifically, text information is the external marker data presented by short videos, and text information retrieval can currently be achieved by crawler crawling technology. Image information is the frame data extracted from the short video at certain time intervals by means of frame processing, and there are many types of image formats. The frame structure and pixel composition of different types of image files are completely different. Voice information is the stereo background music present in short videos, and this music voice also has different formats. Different voice formats also require different technical processing. Therefore, for short video recommendation techniques, the processing results of the above three text, image, and voice information need to be fully understood and analyzed comprehensively in order to come up with the best recommendation results. In this paper, based on multimodal content analysis techniques, we propose a framework for integrated processing of the three elements of information of short videos to form a unified information representation and to realize the elemental processing of the three elements of multimodal short videos at the semantic level. Multimodal content analysis plays an important role in short video recommendation applications. In this paper, a multimodal data fusion short video recommendation method is proposed, in which the key issue is how to effectively combine information from different modalities in order to provide recommendations to users with target information. The method not only considers user behavior features and short video label features, but also maps video from high-dimensional space to low-dimensional dense space, and extract the short video word vector features. At the same time, we consider that there is a relationship between the user's clicking behavior and the short video cover image and profile. The short video image features and text features are fused with the structured features and trained together to complete the short video recommendation task. The method makes full use of the differences and similarities between different models to increase the model's understanding of user behavior and improve the short video recommendation.

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Section: Hybrid Recommendation Algorithmmentioning

confidence: 99%

A Personalized Short Video Recommendation Method Based on Multimodal Feature Fusion

Lin

Wang

et al. 2022

Preprint

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“…But we can see that 6) else (7) while (the number of collected ! � j) do (8) choose j Agents which satisfy: 􏽐…”

Section: Efficiency Comparisonmentioning

confidence: 99%

“…In other aspects, the concept of similarity is often introduced in order to achieve better recommendations [7,8]. Generally, in order to ensure the accuracy of recommended items, many Recommender Systems need to calculate two parameters [9].…”

Section: Introductionmentioning

confidence: 99%

Efficient Personalized Recommendation Based on Federated Learning with Similarity Ciphertext Calculation

Wang

Huang

et al. 2022

Security and Communication Networks

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With the popularity of big data, people get less useful information because of the large amount of data, which makes the Recommender System come into being. However, the privacy and accuracy of the Recommender System still have great challenges. To address these challenges, an efficient personalized recommendation scheme is proposed based on Federated Learning with similarity ciphertext calculation. In this paper, we first design a Similarity calculation algorithm based on Orthogonal Matrix in Ciphertext (SOMC), which can compute the Similarity between users’ demand and Items’ attributes under ciphertext with a low calculation cost. Based on SOMC, we construct an efficient recommendation scheme by employing the Federated Learning framework. The important feature of the proposed approach is improving the accuracy of recommendation while ensuring the privacy of both the users and the Agents. Furthermore, the Agents with good performance are selected according to their Reliability scores to participate in the federal recommendation, so as to further make the accuracy of recommendation better. Under the defined threat model, it is proved that the proposed scheme can meet the privacy requirements of users and Agents. Experiments show that the proposed scheme has optimized accuracy and efficiency compared with existing schemes.

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“…The experiments performed here shows that proposed work gives better result on sparse dataset and has higher efficiency on large dataset. In [52] C. Yang et al put forth a hybrid approach based on social similarity and item attribute. The author used collaborative filtering method combined with social similarities and genres of the movie.…”

Section: Hybrid Filteringmentioning

confidence: 99%

A review of movie recommendation system: Limitations, Survey and Challenges

Sunilkumar

2020

ELCVIA

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Recommendation System is a major area which is very popular and useful for people to take proper automated decisions. It is a method that helps user to find out the information which is beneficial to him/her from variety of data available. When it comes to Movie Recommendation System, recommendation is done based on similarity between users (Collaborative Filtering) or by considering particular user's activity (Content Based Filtering) which he wants to engage with. To overcome the limitations of collaborative and content based filtering generally, combination of collaborative and content based filtering is used so that a better recommendation system can be developed. Also various similarity measures are used to find out similarity between users for recommendation. In this paper, we have surveyed state-of-the-art methods of Content Based Filtering, Collaborative Filtering, Hybrid Approach and Deep Learning Based Methods for movie recommendation. We have also reviewed different similarity measures. Various companies like facebook which recommends friends, LinkedIn which recommends job, Pandora recommends music, Netflix recommends movies, Amazon recommends products etc. use recommendation system to increase their profit and also benefit their customers. This paper mainly concentrates on the brief review of the different techniques and its methods for movie recommendation, so that research in recommendation system can be explored.

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A Hybrid Movie Recommendation Method Based on Social Similarity and Item Attributes

Cited by 10 publications

References 6 publications

A Personalized Short Video Recommendation Method Based on Multimodal Feature Fusion

A Personalized Short Video Recommendation Method Based on Multimodal Feature Fusion

Efficient Personalized Recommendation Based on Federated Learning with Similarity Ciphertext Calculation

A review of movie recommendation system: Limitations, Survey and Challenges

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