最新推荐系统综述 | Survey on Recommendations with Side Information

0 前言 这是继[Shi et al. 2014]之后又一篇以附加边信息的推荐系统为视角的综述文章《Research Commentary on Recommendations with Side

文献阅读：Deep Neural Networks for YouTube Recommendations

文献阅读：Deep Neural Networks for YouTube Recommendations 1. 内容简介 2. 整体系统架构 3. recall部分 4. rank部分 5.

最新边信息推荐系统综述 | Survey on Recommendations with Side Information

0 前言 这是继[Shi et al. 2014]之后又一篇以附加边信息的推荐系统为视角的综述文章《Research Commentary on Recommendations with Side

文献阅读：Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations

文献阅读：Mixed Negative Sampling for Learning Two-tower Neural Networks in Recommendations 1. 问题描述 2.

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论文 Deep Neural Networks for YouTube Recommendations 来自google的YouTube团队，发表在16年9月的RecSys会议。 本文基于大家对Deep Neural Networks for YouTube Recommendations已经完成通读的基础上，不会做细致的论文解析，只会涉及到自己实现过程中的一些总结，如果没有论文了解

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这篇论文 Deep Neural Networks for YouTube Recommendations 是google的YouTube团队在推荐系统上DNN方面的尝试，发表在16年9月的RecSys

关于Deep Neural Networks for YouTube Recommendations的一些思考和实现

论文 Deep Neural Networks for YouTube Recommendations 来自google的YouTube团队，发表在16年9月的RecSys会议。 本文基于大家对Deep Neural Networks for YouTube Recommendations已经完成通读的基础上，不会做细致的论文解析，只会涉及到自己实现过程中的一些总结，如果没有论文了解

Recommendations

recommended publications more actively if each category has a different number of publications within daily recommendations

【机器学习】从电影数据集到推荐系统

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[自然语言处理|NLP]NLP在可解释性和公平性的应用：从原理到实践

# 示例代码：再平衡技术在招聘推荐系统中的应用import numpy as npdef rebalance_recommendations(original_recommendations, demographic_information ): # 根据群体信息调整推荐的职位数量 adjusted_recommendations = original_recommendations.copy() for group ): # 如果某群体推荐数量过少，增加推荐数量 additional_recommendations = np.random.choice(original_recommendations (additional_recommendations) return adjusted_recommendations# 示例original_recommendations = ["Software = rebalance_recommendations(original_recommendations, demographic_information)print("Adjusted Recommendations

CISSP考试指南笔记：6.4 报告

Analyzing Results ---- Only after analyzing the results can you provide insights and recommendations Your job is to show that you have considered the options and have sound recommendations that address Recommendations This section should mirror the organization of your Findings and provide the “now what When writing it, you should consider how each key reader will react to your recommendations. should include as much raw data as possible, but you certainly want to include enough to justify your recommendations

电影推荐系统的实现与优化

混合推荐 def get_mixed_recommendations(title, alpha=0.5): recommendations_cf = set(get_recommendations (title)) recommendations_content = set(get_recommendations_by_content(title)) recommendations = list(recommendations_cf.union(recommendations_content)) return recommendations[:10] print(get_mixed_recommendations = get_mixed_recommendations(movie_title) return render_template('recommendations.html', recommendations =recommendations) if __name__ == '__main__': app.run(debug=True) 系统优化与挑战 数据稀疏性 优化方法：数据稀疏性是电影推荐系统中的常见问题

推荐系统中的多样性与新颖性问题

(num_recommendations)# 为用户1生成推荐recommendations = recommend(1)print("推荐结果：", recommendations)# 引入多样性def = recommendations.sample(frac=1).sort_values(ascending=False) return recommendations.head(num_recommendations )# 为用户1生成多样化的推荐diverse_recommendations = diversify_recommendations(1)print("多样化推荐结果：", diverse_recommendations [recommendations.index.isin([104, 105])] # 示例长尾物品 recommendations.update(long_tail_items * novelty_weight ) return recommendations.head(num_recommendations)# 为用户1生成具有新颖性的推荐novelty_recommendations = recommend_with_novelty

围观RecSys2020 | 推荐系统顶会说了啥？(附论文打包下载)

Meta-Bandit for Recommender Systems Selection Deep Bayesian Bandits：Exploring in Online Personalized Recommendations Filtering History-Augmented Collaborative Filtering for Financial Recommendations 7. Point1 ：Debiasing Item-to-Item Recommendations With Small Annotated Datasets 这篇文章关注了小数据集。 适合跑步:马拉松训练的个性化推荐 | Fit to Run：Personalised Recommendations for Marathon Training 免费午餐！ to Marathon Runners 通过实时推荐减少家庭能源浪费 | Reducing energy waste in households through real-time recommendations

数据挖掘实践指南读书笔记1

=person] scores.sort() scores.reverse() return scores[0:n] # Gets recommendations for a person : recommendations[artist] = (neighborRatings[artist] * weight) else: recommendations [artist] = (recommendations[artist] + neighborRatings[artist] * weight) # now make list from dictionary recommendations = list(recommendations.items()) recommendations = [(self.convertProductID2name (k), v) for (k, v) in recommendations] # finally sort and return recommendations.sort

spark杂记：movie recommendation using ALS

: int, top n recommendations Return ------ list of top n similar movie recommendations ) def make_recommendations(self, fav_movie, n_recommendations): """ make top n movie n_recommendations: int, top n recommendations """ # make inference and get raw recommendations print('Recommendation system start to make inference ...') recommender.make_recommendations(movie_name, top_n) # stop spark.stop()

【机器学习】机器学习与推荐系统在电子商务中的融合应用与性能优化新探索

] > 0)[0]) return set(recommendations) - set(np.where(ratings[user_id] > 0)[0]) user_id = 0 recommendations (similar_items) return set(recommendations) - set(np.where(user_ratings > 0)[0]) recommendations : {recommendations}') 性能优化 推荐系统的性能优化是提高推荐效果和系统响应速度的关键。 ] > 0)[0]) return set(recommendations) - set(np.where(ratings[user_id] > 0)[0]) user_id = 0 recommendations (similar_items) return set(recommendations) - set(np.where(user_ratings > 0)[0]) recommendations

Next.js预渲染数据断层：在线商城个性化推荐的时效性困局

排查日志：// 控制台输出（模拟）[Recommendations] componentDidMount: fetching recommendations...// Network 面板未发现 /api /recommendations 请求结论：客户端未发起推荐数据请求，排除客户端获取异常。 -- 页面源码片段 -->

数据挖掘实践指南读书笔记2

表示加权Slope算法给出的用户u对物品j的预测 def slopeOneRecommendations(self, userRatings): recommendations = {} frequencies = {} # for every item and rating in the user's recommendations for (userItem, userRating recommendations.setdefault(diffItem, 0.0) frequencies.setdefault(diffItem, 0) ()] # finally sort and return recommendations.sort(key=lambda artistTuple return recommendations[:50] # test code for SlopeOneRecommendations #r =

RecSys2020推荐系统论文集锦

序列推荐 From the lab to production: A case study of session-based recommendations in the home-improvement Exploring Longitudinal Effects of Session-based Recommendations. Explainable Recommendations via Attentive Multi-Persona Collaborative Filtering. Providing Explainable Race-Time Predictions and Training Plan Recommendations to Marathon Runners. Ensuring Fairness in Group Recommendations by Rank-Sensitive Balancing of Relevance.