To Cluster or Not to Cluster: The Impact of Clustering on the Performance of Aspect-Based Collaborative Filtering Article Swipe
Sumaia Mohammed Al-Ghuribi
,
Shahrul Azman Mohd Noah
,
Mawal A. Mohammed
,
Sultan Noman Qasem
,
Belal Abdullah Hezam Murshed
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/access.2023.3270260
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/access.2023.3270260
Collaborative filtering (CF) is one of the most widely utilised approaches in recommendation techniques. It suggests items to users based on the ratings of other users who share their preferences. Thus, one of the aims of CF is to find reliable neighbours. Typically, CF produces a sparse user-item rating matrix, when relying only on the ratings to identify the precise neighbours, resulting in poor performance. User reviews can be essential in overcoming those situations because of the diverse elements available in reviews. The most popular element is aspects, which can provide a fine-grained analysis of users’ behaviours, thus improving personalised recommendations. However, increasing the number of aspects also results in sparsity, therefore may deteriorate the recommendation performance. As a result, clustering of aspects may lessen this sparsity, but it is yet unclear how much this would affect the performance of CF systems. This study proposes a CF approach based on aspect clustering that addresses the above issue in terms of rating prediction. The approach aims to reduce the sparseness in the multi-criteria rating matrix by grouping aspects into clusters based on their semantic similarity, which will be less expensive and require less memory to discover the neighbourhood set. Our approach extracts aspects and represents them using Google’s pre-trained Word2vec model. Then, aspects are organised into clusters using the K-means clustering algorithm. Multi-dimensional Euclidean distance is used as a similarity measure for finding the appropriate neighbours and predicted ratings of unseen items are then made using the NN algorithm. This study also identifies the number of aspects that significantly impacts CF performance. Experiments are carried out using a real large-scale dataset: the Amazon movie dataset. Evaluation is also performed by comparing CF performance of the proposed approach with three different baseline approaches. Results show that the proposed approach improves CF performance compared to other approaches in terms of three predictive accuracy metrics.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2023.3270260
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/10107993.pdf
- OA Status
- gold
- Cited By
- 11
- References
- 62
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4367031915
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4367031915Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2023.3270260Digital Object Identifier
- Title
-
To Cluster or Not to Cluster: The Impact of Clustering on the Performance of Aspect-Based Collaborative FilteringWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-01-01Full publication date if available
- Authors
-
Sumaia Mohammed Al-Ghuribi, Shahrul Azman Mohd Noah, Mawal A. Mohammed, Sultan Noman Qasem, Belal Abdullah Hezam MurshedList of authors in order
- Landing page
-
https://doi.org/10.1109/access.2023.3270260Publisher landing page
- PDF URL
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/10107993.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://ieeexplore.ieee.org/ielx7/6287639/6514899/10107993.pdfDirect OA link when available
- Concepts
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Collaborative filtering, Computer science, Cluster analysis, Recommender system, Similarity (geometry), Data mining, Word2vec, Set (abstract data type), Euclidean distance, Information retrieval, Machine learning, Artificial intelligence, Embedding, Image (mathematics), Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
11Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 6, 2023: 2Per-year citation counts (last 5 years)
- References (count)
-
62Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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