胡斌斌-基于大模型的推荐算法及应用.pdf

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1、DataFunSummit#2024Large language model based Recommender System and Application胡斌斌蚂蚁集团算法专家01Background0302LLM as Knowledge Extractor04LLM as a Reasoning Pool目录 CONTENTLLM as Teacher RecommenderWorkflow of RSs1.Train recommender on collected interaction data to capture user preferences.2.Recommender

2、generates recommendations based on estimated preferences.3.User engage with the recommended items,forming new data,affected by open world.4.train recommender with new data again,either refining user interests or capturing new ones.Current recommender systems are oftentrainedonaclosed-loopuser-itemin

3、teraction dataset,inevitably sufferingfrom severe exposure bias and popularitybias.Jizhi Zhang et al.Large Language Models for Recommendation:Progresses and Future Directions.WWW 2024.Jiawei Chen et al.Bias and debias in recommender system:A survey and future directions.TOIS 2023.Development of LMsL

4、arge Language Model：billions of parameters,emergent capabilities Rich knowledge&Language Capabilities Instruction following In-context learning Chain-of-thought Planning Jizhi Zhang et al.Large Language Models for Recommendation:Progresses and Future Directions.WWW 2024.From of CF to LLM based RSFro

5、m Shallow Models,to Deep Models,to Large ModelsShallow ModelsDeep ModelsLarge ModelsKoren et al.Matrix factorization techniques for recommender systems.Computer 2009.Heng-Tze Cheng et al.Wide&deep learning for recommender systems.”DLRS 2016.Jizhi Zhang et al.Large Language Models for Recommendation:

6、Progresses and Future Directions.WWW 2024.Key Challengesp Tend to rely on semantics,and another important aspect of recommendation tasks is collaborative information.p Balance the trade-off between the cost and effectiveness.p Adapt the reasoning ability of LLMs to Recommendation.LLM for Recommendat