RecSys
#RecommenderSystems#Evaluation
Issue Date: 2025-04-10 Revisiting BPR: A Replicability Study of a Common Recommender System Baseline, Aleksandr Milogradskii+, RecSys24 CommentBPR、実装によってまるで性能が違う…実装の違い#PEFT(Adaptor/LoRA)#Zero/FewShotLearning
Issue Date: 2025-03-30 TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation, Keqin Bao+, RecSys23 Comment下記のようなユーザのプロファイルとターゲットアイテムと、binaryの明示的なrelevance feedbackデータを用いてLoRA、かつFewshot Learningの設定でSFTすることでbinaryのlike/dislikeの予測性能を向上。PromptingだけでなくSFTを実施した初 ... #NeuralNetwork#CollaborativeFiltering#Pocket#Evaluation
Issue Date: 2025-04-15 Revisiting the Performance of iALS on Item Recommendation Benchmarks, Steffen Rendle+, arXiv21
Issue Date: 2025-04-10 Revisiting BPR: A Replicability Study of a Common Recommender System Baseline, Aleksandr Milogradskii+, RecSys24 CommentBPR、実装によってまるで性能が違う…実装の違い#PEFT(Adaptor/LoRA)#Zero/FewShotLearning
Issue Date: 2025-03-30 TALLRec: An Effective and Efficient Tuning Framework to Align Large Language Model with Recommendation, Keqin Bao+, RecSys23 Comment下記のようなユーザのプロファイルとターゲットアイテムと、binaryの明示的なrelevance feedbackデータを用いてLoRA、かつFewshot Learningの設定でSFTすることでbinaryのlike/dislikeの予測性能を向上。PromptingだけでなくSFTを実施した初 ... #NeuralNetwork#CollaborativeFiltering#Pocket#Evaluation
Issue Date: 2025-04-15 Revisiting the Performance of iALS on Item Recommendation Benchmarks, Steffen Rendle+, arXiv21
#RecommenderSystems#NeuralNetwork#CollaborativeFiltering#Pocket#MatrixFactorization#read-later#Reproducibility
Issue Date: 2025-05-16 Neural Collaborative Filtering vs. Matrix Factorization Revisited, Steffen Rendle+, RecSys20 #RecommenderSystems#read-later#Reproducibility
Issue Date: 2025-05-14 Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison, Zun+, RecSys20 Comment日本語解説:https://qiita.com/smochi/items/c4cecc48e4aba0071ead ... #NeuralNetwork#Embeddings#Pocket#CTRPrediction#SIGKDD#numeric
Issue Date: 2025-04-22 An Embedding Learning Framework for Numerical Features in CTR Prediction, Huifeng Guo+, arXiv20 Comment従来はdiscretizeをするか、mlpなどでembeddingを作成するだけだった数値のinputをうまく埋め込みに変換する手法を提案し性能改善数値情報を別の空間に写像し自動的なdiscretizationを実施する機構と、各数値情報のフィールドごとのglobalな情報を保持するmeta-e ... #RecommenderSystems
Issue Date: 2022-04-05 Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison, Sun+, RecSys20 Comment日本語解説：https://qiita.com/smochi/items/c4cecc48e4aba0071ead ... #RecommenderSystems#NeuralNetwork#CollaborativeFiltering#Evaluation
Issue Date: 2022-04-11 Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches, Politecnico di Milano, Maurizio+, RecSys19 CommentRecSys'19のベストペーパー 日本語解説：https://qiita.com/smochi/items/98dbd9429c15898c5dc7重要研究 ... #RecommenderSystems#NeuralNetwork#NaturalLanguageGeneration#Pocket#NLP#ReviewGeneration
Issue Date: 2019-08-17 Improving Explainable Recommendations with Synthetic Reviews, Ouyang+, RecSys18 #RecommenderSystems#NeuralNetwork#Pocket#Admin'sPick
Issue Date: 2018-12-27 Deep Neural Networks for YouTube Recommendations, Covington+, RecSys16 #RecommenderSystems#Tutorial#InteractiveRecommenderSystems#Slide
Issue Date: 2017-12-28 Interactive Recommender Systems, Netflix, RecSys15, 2015.09 #Article#RecommenderSystems#Novelty
Issue Date: 2017-12-28 “I like to explore sometimes”: Adapting to Dynamic User Novelty Preferences, Kapoor et al. （with Konstan）, RecSys’15 Comment・典型的なRSは，推薦リストのSimilarityとNoveltyのcriteriaを最適化する．このとき，両者のバランスを取るためになんらかの定数を導入してバランスをとるが，この定数はユーザやタイミングごとに異なると考えられるので（すなわち人やタイミングによってnoveltyのpreference ...
Issue Date: 2025-05-16 Neural Collaborative Filtering vs. Matrix Factorization Revisited, Steffen Rendle+, RecSys20 #RecommenderSystems#read-later#Reproducibility
Issue Date: 2025-05-14 Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison, Zun+, RecSys20 Comment日本語解説:https://qiita.com/smochi/items/c4cecc48e4aba0071ead ... #NeuralNetwork#Embeddings#Pocket#CTRPrediction#SIGKDD#numeric
Issue Date: 2025-04-22 An Embedding Learning Framework for Numerical Features in CTR Prediction, Huifeng Guo+, arXiv20 Comment従来はdiscretizeをするか、mlpなどでembeddingを作成するだけだった数値のinputをうまく埋め込みに変換する手法を提案し性能改善数値情報を別の空間に写像し自動的なdiscretizationを実施する機構と、各数値情報のフィールドごとのglobalな情報を保持するmeta-e ... #RecommenderSystems
Issue Date: 2022-04-05 Are We Evaluating Rigorously? Benchmarking Recommendation for Reproducible Evaluation and Fair Comparison, Sun+, RecSys20 Comment日本語解説：https://qiita.com/smochi/items/c4cecc48e4aba0071ead ... #RecommenderSystems#NeuralNetwork#CollaborativeFiltering#Evaluation
Issue Date: 2022-04-11 Are We Really Making Much Progress? A Worrying Analysis of Recent Neural Recommendation Approaches, Politecnico di Milano, Maurizio+, RecSys19 CommentRecSys'19のベストペーパー 日本語解説：https://qiita.com/smochi/items/98dbd9429c15898c5dc7重要研究 ... #RecommenderSystems#NeuralNetwork#NaturalLanguageGeneration#Pocket#NLP#ReviewGeneration
Issue Date: 2019-08-17 Improving Explainable Recommendations with Synthetic Reviews, Ouyang+, RecSys18 #RecommenderSystems#NeuralNetwork#Pocket#Admin'sPick
Issue Date: 2018-12-27 Deep Neural Networks for YouTube Recommendations, Covington+, RecSys16 #RecommenderSystems#Tutorial#InteractiveRecommenderSystems#Slide
Issue Date: 2017-12-28 Interactive Recommender Systems, Netflix, RecSys15, 2015.09 #Article#RecommenderSystems#Novelty
Issue Date: 2017-12-28 “I like to explore sometimes”: Adapting to Dynamic User Novelty Preferences, Kapoor et al. （with Konstan）, RecSys’15 Comment・典型的なRSは，推薦リストのSimilarityとNoveltyのcriteriaを最適化する．このとき，両者のバランスを取るためになんらかの定数を導入してバランスをとるが，この定数はユーザやタイミングごとに異なると考えられるので（すなわち人やタイミングによってnoveltyのpreference ...