A weighted similarity ensemble unifies user-item and item-item collaborative filtering using shared embeddings to deliver competitive top-N recommendations without extra fine-tuning.
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ConstBERT and ColBERT-v2 reproduce on MS-MARCO but drop 86-97% on long queries because MaxSim cannot filter filler noise, and extra fine-tuning or backend changes do not overcome the architectural constraint.
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Collaborative Filtering Through Weighted Similarities of User and Item Embeddings
A weighted similarity ensemble unifies user-item and item-item collaborative filtering using shared embeddings to deliver competitive top-N recommendations without extra fine-tuning.
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Reproduction Beyond Benchmarks: ConstBERT and ColBERT-v2 Across Backends and Query Distributions
ConstBERT and ColBERT-v2 reproduce on MS-MARCO but drop 86-97% on long queries because MaxSim cannot filter filler noise, and extra fine-tuning or backend changes do not overcome the architectural constraint.