000			02498nam a22001457a 4500
003			FT8925
050			_aQA76.9 A43 M36 2025
100	1		_a Mangune, Anne Christine M.; Ragudo, Sophia Erin H.
245			_aAn enhancement of item-based collaborative filtering algorithm applied in book recommendations
264		1	_cc2025
300			_bUndergraduate Thesis: (Bachelor of Science in Computer Science) - Pamantasan ng Lungsod ng Maynila, 2025
338			_2volume _avolume _bvolume
505			_aABSTRACT: This study focused on enhancing the Item-Based Collaborative Filtering Algorithm tom improve similarly calculation and recommendation generation. The study addressed key challenges of the algorithm: (1) cold start for new items which restricts item similarly, (2) susceptibility to data sparsity and skewed distribution which compromises item representations, and (3) failure to consider the weight of the interaction date and the interaction progress in the calculation. To address these challenges, the following enhancements were implemented: (1) temporary boost for new items to establish item similarity, (2) virtual interactions based on item attributes association and matrix factorization to represent less interacted items and highly interacted items on the same level, and (3) item interaction monitoring progress and time-based weight – depending on the interaction’s aging days. Testing was applied in book recommendation with sequences of 10 users to 20 items, random interactions, item genres, authors, series, dates the item were added to the system, interaction progress, and date of interaction, showed that the enhanced algorithm addressed the key challenges. (1) Temporary boost for new items with similarity scores averaging 49.17% higher (0.270 ≠ 0.050 vs. 0.181 ± 0.019), establishing item similarity for new items, (2) Virtual interactions and matrix factorization resulted in a 2.89% decrease in interaction data (0.369 ≠0.041 vs. 0.380 ±0.067) and a 21.42% reduction in similarity scores (0.202 ≠0.041 vs. 0.257 ±0.017), improving item representations, (3) The consideration of interaction date and progress decreased interaction data by 42.89% (0.217 ≠ 0.050 vs 0.380 ± 0.067), demonstrating the varying weights of the interaction date and the interaction progress. The Enhanced Item-Based Collaborative Filtering Algorithm provides a more reliable assessment of item interaction, enabling a better generation of item recommendations.
942			_2lcc _cMS
999			_c37397 _d37397