A hybrid recommendation system using association rule mining, i-ALS algorithm, and SVD++ approach: A case study of a B2B company
Bibliographic record
Abstract
In the field of recommendation systems, collaborative filtering is a widely used technique. It provides recommendations to active users based on the ratings provided by similar users. However, this method may reduce the accuracy of user preference predictions and lead to lower-quality recommendations in cases of high data sparsity. This issue is often observed in the Business-to-Business (B2B) context, where user-generated reviews are often sparse. To overcome this challenge, we present a novel hybrid approach that explores product taxonomies and association rule mining combined with an advanced method for initialization. Our approach first involves generating a new explicit taxonomy based solely on textual product descriptions and extending the user-product matrix using association rule mining results. Second, complementary items are added to the user-item matrix based on users’ purchasing behaviors, as emphasized by the extracted association rules. Finally, we use the implicit Alternating Least Squares (i-ALS) algorithm and initialize the latent factor matrices with values obtained through the singular value decomposition approach (BLS-SVD++). This hybrid approach is tested and compared with conventional approaches, considering a real-world case study of a distributor located in Quebec. The results obtained from feedback implicitly inferred from sales data demonstrated improved RS performance compared to conventional approaches. • A novel hybrid recommendation system is proposed. • We used association rule mining, the i-ALS algorithm, and the SVD++ approach. • This hybrid system is particularly relevant for overcoming the sparsity problem in B2B settings. • Real-world B2B sales transactions are considered. • The proposed methodology can be generalized to other real-world cases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".