Comparative Study of Filtering Methods for Scientific Research Article Recommendations
Abstract
1. Introduction
- Classical Classification [9]: This widely known classification divides recommendation systems into three main methods (which will be discussed in detail throughout this article):
- –
- –
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- Hybrid filtering: This method combines both CF and CB techniques to capitalize on the strengths of each, resulting in more precise recommendations.
- Rao and Talwar’s Classification [15]: This method expands on classical classification by introducing additional categories and categorizing systems based on the source of information used. In addition to the collaborative filtering, content-based filtering and hybrid filtering, we find:
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- Demographic Filtering [16]: This method uses demographic information about users to make recommendations.
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- Knowledge-Based Filtering [17]: This method uses specific domain knowledge and user requirements to recommend items.
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- Community Filtering: Also called social RSs [18], this method enhances personalized recommendations by incorporating social relationships. However, the impact of social relationships on recommendation accuracy in niche domains is not well understood, and methods often ignore implicit social influences and their evolution over time. Key research areas include the influence of community interactions, best practices for integrating community data, balancing explicit and implicit social influences, and maintaining scalability while preserving social relationship information.
2. Related Works
2.1. Collaborative Filtering (CF)
2.1.1. Memory-Based
2.1.2. Model-Based
- Bayesian Networks [68]:These probabilistic models represent dependencies among variables (users and items) and use these relationships to make predictions.
- Clustering [69]: Users or items are grouped into clusters based on their similarities, and recommendations are made based on the preferences of the cluster members.
- Markov Decision Processe [70]: These models consider the sequences of user interactions and make recommendations by predicting the next item in a user’s sequence.
- Machine Learning Techniques [71]: Algorithms such as neural networks, decision trees, and support vector machines can be trained on user-item interaction data to predict user preferences.
2.2. Content-Based Filtering (CB)
2.3. Hybrid Recommendation Systems
- Weighted: Assigns weights to each of the methods, combining their scores into a single recommendation score based on the weighted sum.
- Switching: Alternates between different recommendation methods depending on specific criteria like user type, item characteristics, or the recommendation context.
- Mixed: Independently generates recommendations using various methods and then merges the results into a unified list presented to the user.
- Cascade: Uses one recommendation method to produce an initial list and refines it with another method. For example, CF can create a broad list, which is then fine-tuned by content-based filtering.
- Feature augmentation: Enhances one recommendation method by incorporating additional features derived from another. For example, user similarity scores from CF can improve content-based filtering.
- Meta-level: Utilizes the model output from one recommendation technique as input features for another. For example, the results of a content-based model can serve as features in a CF model.
3. Proposed Methods
- Collaborative Filtering: For this method, we employed a model-based approach, specifically using the singular value decomposition (SVD) algorithm [73], as it is a robust and reliable choice for building high-quality CF recommendation systems. The SVD algorithm (see Figure 2) decomposes the user-item interaction Y matrix into three matrices , , and that capture the latent factors representing users and items.
- Content-Based Filtering: Since the task involves recommending scientific articles, we decided to use common techniques from the fields of information retrieval and text mining. Specifically, we used Term Frequency–Inverse Document Frequency (TF-IDF) [74] to convert text data into numerical vectors and extract features, where each word is represented by a position in the vector, and the value indicates the relevance of that word to a particular article. We calculate the similarity between the articles because all the elements will be represented in the same vector space model. The weight of term t in document d is given by:
- Hybrid Filtering: Concerning this approach, we used the dynamic weighted hybridization method (see the algorithm flowchart in Figure 3). This technique combines the recommendations from both CF and CB by assigning different weights ( and ), we can adjust them based on performance evaluation until convergence, resulting in final recommendations. This flexibility is particularly useful when dealing with diverse datasets, as it enables us to tailor the hybrid model to better suit the specific characteristics of the data. We update weights using the gradient of the loss function as follows:
4. Experimental Section
4.1. Experimental Settings and Datasets
4.2. Metrics of Evaluation
4.3. Results
4.4. Discussion
- Switching and Cascade techniques: NDCG@5 values range from 0.180 to 0.293, showing varying performance across systems.
- Traditional Weighted technique: NDCG@5 values range from 0.183 to 0.305, indicating performance variability.
- Dynamic Weighted technique: Highest NDCG@5 values ranging from 0.200 to 0.312, suggesting superior effectiveness.
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Year | Author(s) | Purpose | Sample and Methods | Key Findings |
---|---|---|---|---|
2019 | Collins and Beel [52] | To evaluate different document embedding methods for paper recommendations | Used Doc2Vec and TF-IDF in Mr. DLib recommender-as-service | Demonstrated the effectiveness of different document embedding approaches for paper recommendations |
2019 | Chen and Ban [53] | To develop a user interest clustering model | Applied LDA and pattern equivalence class mining in CPM model | Successfully clustered user interests using topic modeling and pattern mining techniques |
2020 | Ali et al. [54] | To create a personalized probabilistic recommendation model | Developed PR-HNE using citations, co-authorships, and topical relevance with SBERT and LDA | Effectively integrated multiple graph information sources with semantic embeddings |
2020 | Du et al. [55] | To develop a heterogeneous network-based recommendation system | Created HNPR using random walks on citation and co-author networks | Demonstrated the effectiveness of using heterogeneous network structures for recommendations |
2020 | Nishioka et al. [56] | To incorporate users’ recent interests for serendipitous recommendations | Integrated user tweets to capture current interests | Successfully enhanced recommendation serendipity through social media integration |
2020 | Rahdari & Brusilovsky [57] | To develop a customizable recommendation system for conference participants | Created a system allowing users to control feature impacts | Showed the benefits of user-controlled feature weighting in recommendations |
2020 | Wang et al. [58] | To develop a knowledge-aware recommendation system | Implemented LSTM-based path recurrent network with TF-IDF representations | Successfully mined knowledge graph paths for enhanced recommendations |
2021 | Bereczki [59] | To model user–paper interactions in a bipartite graph | Used Word2Vec/BERT embeddings with graph convolution | Demonstrated effective integration of text embeddings with graph-based approaches |
2021 | Chaudhuri et al. [60] | To incorporate indirect features for recommendations | Developed Hybrid Topic Model combining LDA and Word2Vec | Successfully utilized keyword diversification and citation analysis for improved recommendations |
2022 | Kreutz et al. [61] | To review contemporary paper recommendation systems | Surveyed studies from January 2019 to October 2021 | Provided comprehensive overview of methods, datasets, and challenges |
2022 | Aymen et al. [62] | To review academic works on paper recommendations | Analyzed content-based, CF, and hybrid methods | Compared methodologies and identified open issues in the field |
2023 | Zhang et al. [63] | To survey scholarly recommendation systems | Reviewed challenges and approaches in scholarly recommendations | Provided insights into broader scholarly recommendation systems |
Approach | Strengths | Weaknesses |
---|---|---|
Collaborative Filtering (CF) |
|
|
Content-Based (CB) |
|
|
CI&T Deskdrop | Citeulike-t | |
---|---|---|
# of Users | 1895 | 7947 |
# of Articles | 3122 | 25,975 |
# of Interactions | 72,312 | 134,860 |
Sparsity | 7.99% | 99.93% |
CI&T Deskdrop | Citeulike-t | |||||||
---|---|---|---|---|---|---|---|---|
Hybridizing Technique | NDCG@5 | NDCG@10 | Novelty@5 | Novelty@10 | NDCG@5 | NDCG@10 | Novelty@5 | Novelty@10 |
Switching | 0.293 | 0.287 | 0.180 | 0.090 | 0.225 | 0.215 | 0.125 | 0.065 |
Cascade | 0.295 | 0.290 | 0.175 | 0.088 | 0.230 | 0.210 | 0.120 | 0.063 |
Traditional Weighted | 0.305 | 0.300 | 0.183 | 0.094 | 0.240 | 0.230 | 0.138 | 0.074 |
Dynamic Weighted | 0.312 | 0.308 | 0.200 | 0.100 | 0.250 | 0.240 | 0.150 | 0.080 |
CI&T Deskdrop | Citeulike-t | |||
---|---|---|---|---|
NDCG@5 | NDCG@10 | NDCG@5 | NDCG@10 | |
Collaborative-filtering(CF) | 0.259 | 0.257 | 0.200 | 0.190 |
Content based (CB) | 0.289 | 0.290 | 0.230 | 0.220 |
Hybrid | 0.312 | 0.308 | 0.250 | 0.240 |
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El Alaoui, D.; Riffi, J.; Sabri, A.; Aghoutane, B.; Yahyaouy, A.; Tairi, H. Comparative Study of Filtering Methods for Scientific Research Article Recommendations. Big Data Cogn. Comput. 2024, 8, 190. https://doi.org/10.3390/bdcc8120190
El Alaoui D, Riffi J, Sabri A, Aghoutane B, Yahyaouy A, Tairi H. Comparative Study of Filtering Methods for Scientific Research Article Recommendations. Big Data and Cognitive Computing. 2024; 8(12):190. https://doi.org/10.3390/bdcc8120190
Chicago/Turabian StyleEl Alaoui, Driss, Jamal Riffi, Abdelouahed Sabri, Badraddine Aghoutane, Ali Yahyaouy, and Hamid Tairi. 2024. "Comparative Study of Filtering Methods for Scientific Research Article Recommendations" Big Data and Cognitive Computing 8, no. 12: 190. https://doi.org/10.3390/bdcc8120190
APA StyleEl Alaoui, D., Riffi, J., Sabri, A., Aghoutane, B., Yahyaouy, A., & Tairi, H. (2024). Comparative Study of Filtering Methods for Scientific Research Article Recommendations. Big Data and Cognitive Computing, 8(12), 190. https://doi.org/10.3390/bdcc8120190