Graph Convolutional Neural Network for Multimodal Movie Recommendation

IF 0.4 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS Applied Computing Review Pub Date : 2023-03-27 DOI:10.1145/3555776.3577853
Prabir Mondal, Daipayan Chakder, Subham Raj, S. Saha, N. Onoe
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引用次数: 2

Abstract

The Recommendation System (RS) development and recommending customers' preferred products to the customer are highly desirable motives in today's digital market. Most of the RSs are mainly based on textual information of the engaged entities in the platform and the ratings provided by the users to the products. This paper develops a movie recommendation system where the cold-start problem relating to rating information dependency has been dealt with and the multi-modality approach is introduced. The proposed method differs from existing approaches in three main aspects: (a) implementation of knowledge graph for text embedding, (b) besides textual information, other modalities of movies like video, and audio are employed rather than rating information for generating movie/user representation and this approach deals with the cold-start problem effectively, (c) utilization of graph convolutional network (GCN) for generating some further hidden features and also for developing regression system.
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多模态电影推荐的图卷积神经网络
在当今的数字市场中,推荐系统(RS)的开发和向客户推荐客户喜欢的产品是非常可取的动机。大多数RSs主要基于平台中参与实体的文本信息和用户对产品的评分。本文开发了一个电影推荐系统,解决了评级信息依赖的冷启动问题,并引入了多模态方法。所提出的方法与现有方法的不同之处主要有三个方面:(a)实现用于文本嵌入的知识图;(b)除了文本信息,还使用视频和音频等其他电影形式而不是评级信息来生成电影/用户表示,这种方法有效地处理了冷启动问题;(c)利用图卷积网络(GCN)来生成一些进一步的隐藏特征,并用于开发回归系统。
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来源期刊
Applied Computing Review
Applied Computing Review COMPUTER SCIENCE, INFORMATION SYSTEMS-
自引率
40.00%
发文量
8
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