A TF-IDF AND LOGISTIC REGRESSION PIPELINE FOR SCHOLARLY ARTICLE CLASSIFICATION AND RECOMMENDATION: IEEE XPLORE BENCHMARK STUDY
Keywords:
Scholarly article classification, scholarly article recommendation, abstract-only text classification, TF-IDF, Logistic Regression, IEEE Xplore benchmark, content-based recommendation, cosine similarity, calibrated probabilities, classifier-aware re-ranking, supervised machine learningAbstract
The rapid growth of scholarly publications has made automated topical organization
and recommendation essential for efficient literature search. However, most existing
approaches treat classification and recommendation as two separate tasks with independent representations. This paper proposes a unified content-based framework in
which a single TF-IDF representation of the article abstract drives both multi-class
topical classification and top-k article recommendation. A new benchmark of 11,744
abstracts is constructed from the IEEE Xplore digital library in six topical queries. The
abstract text and the topical query label are retained for every record, so the entire
pipeline operates on abstracts alone without titles, author keywords, or indexer-supplied
terms. A preliminary confusion analysis reveals that two queries (Big Data Analysis
and Cloud Computing) exhibit near-complete vocabulary collapse and are consolidated
into a single class, yielding a five-domain benchmark: Big Data & Cloud Computing, Data Science, Robotics, Wireless Communication, and Breast Cancer. On the
classification side, five supervised learners (Logistic Regression, Linear SVM, SGD,
k-Nearest Neighbours, and Decision Tree) are compared under identical 80/20 stratified
hold-out and 10-fold cross-validation protocols. The grid-searched Logistic Regression
attains 85.01% accuracy (weighted F1 = 0.850), and a soft-voting ensemble of Logistic
Regression, Linear SVM, and SGD reaches 85.57% (weighted F1 = 0.855). On the
recommendation side, the same TF-IDF representation powers a top-10 recommender
that achieves MAP@10 = 0.7664 with pure cosine ranking. Reusing the classifier’s
calibrated class probabilities to re-rank cosine candidates lifts Precision@10 by +12.5
percentage points (to 0.7715) and MAP@10 by +7.6 points (to 0.8428), with consistent
gains on NDCG@10 and MRR. The dataset, preprocessing pipeline, trained models,
and replication scripts are released to support reproducibility.
