A Self-Attention Bidirectional Long Short-Term Memory for Cold Start Movie Recommendation Models
- Title
- A Self-Attention Bidirectional Long Short-Term Memory for Cold Start Movie Recommendation Models
- Creator
- Manohar M.; Reddy R.A.; Alzubaidi L.H.; Hameed Abdul Hussein A.; Buvaneswari P.R.
- Description
- Movie recommendation systems are useful tools that help users find relevant results and prevent information overload. On the other hand, the user cold-start issue has arisen because the system lacks sufficient user data. Furthermore, they are not very scalable for use in extensive real-world applications. One of the key strategies to address the sparsity and cold-start problems is to leverage other sources of information, including item or user profiles or user reviews. Processing client feedback is typically a challenging process that involves challenging the interpretation and analysis of the textual data. Thus, this research implements an efficient deep learning-based recommendation architecture. Following the acquisition of textual data from the Amazon product reviews database, stop word removal, lemmatization, and stemming techniques are applied to the data pre-processing which eliminate inconsistent and redundant data, facilitating the process of interpreting and utilising data. Then, the Term Frequency-Inverse Document Frequency (TF-IDF) method is applied to extract the feature values from the pre-processed text data. The extracted feature values are fed to the Self-Attention Bidirectional Long Short-Term Memory (SA-BiLSTM) that utilises the matrix factorization method framework's information sources. The SA-BiLSTM model obtained 95.93% of recall, 94.76% of precision, and 97.84% of accuracy on the amazon product reviews database. 2023 IEEE.
- Source
- IEEE 1st International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Bidirectional Long Short-Term Memory; Lemmatization; Movie recommendation; Self-attention; Stemming; Term frequency-inverse document frequency
- Coverage
- Manohar M., Christ (Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Reddy R.A., School of Computer Science and Artificial Intelligence, Sr University, Warangal, India; Alzubaidi L.H., The Islamic University, Najaf, Iraq; Hameed Abdul Hussein A., College of Pharmacy Ahl Al Bayt University, Karbala, Iraq; Buvaneswari P.R., School of Electrical and Electronic Engineering Vit Bhopal University, Sehhore, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835031646-9
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Manohar M.; Reddy R.A.; Alzubaidi L.H.; Hameed Abdul Hussein A.; Buvaneswari P.R., “A Self-Attention Bidirectional Long Short-Term Memory for Cold Start Movie Recommendation Models,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 25, 2025, https://archives.christuniversity.in/items/show/19645.