KDD Cup2012简单回顾

最新推荐文章于 2021-01-03 16:00:59 发布

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最新推荐文章于 2021-01-03 16:00:59 发布 · 1.2w 阅读

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#数据挖掘 #merge #算法

本文介绍了Shanda Innovations团队在KDD Cup 2012比赛中获得亚军的解决方案。提出了一种名为Multifaceted Factorization Models的方法，结合了社交网络中的各种特征，如用户关系、行为、关键字等，提高了推荐的准确性。论文详细阐述了上下文感知的集成框架和用户行为建模，该方法在比赛的测试集上取得了0.41874的分数。

经过2个月辛苦的拼搏，我们最终获得了KDD Cup 2012比赛的亚军(track1)！
能赢得数据挖掘界这个著名赛事的好名次，我认为靠的是实力+运气+坚持到底的意志
比赛使用的算法，整理成论文后，会发表在今年SIGKDD workshop上

最终的Leaderboard截图如下，以资留念，我们的队名是Shanda Innovations，最终分数是0.41874

比赛中所使用的算法已经整理成论文，标题和摘要如下

Context-aware Ensemble of Multifaceted Factorization Models for Recommendation Prediction in Social Networks

Abstract

This paper describes the solution of Shanda Innovations team to Task 1 of KDD-Cup 2012. A novel approach called Multifaceted Factorization Models is proposed to incorporate a great variety of features in social networks. Social relationships and actions between users are integrated as implicit feedbacks to improve the recommendation accuracy. Keywords, tags, profiles, time and some other features are also utilized for modeling user interests. In addition, user behaviors are modeled from the durations of recommendation records. A context-aware ensemble framework is then applied to combine multiple predictors and produce final recommendation results. The proposed approach obtained $0.43959$ (public score)/$0.41874$(private score) on the testing dataset, which achieved the 2nd place in the KDD-Cup competition.

Introduction