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最近读的Pinterest发表的paper

(2017-03-26 20:49:45)
标签:

用户意图预测

图像搜索引擎

pinterest

分类: 数据挖掘
文章主要是关于用户意图预测和pinterest视觉搜索引擎相关的探索

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paper:Predicting Intent Using Activity Logs: How Goal Specificity and Temporal Range Affect User Behavior

We find that goal specificity is bimodal – users tend to be either strongly goal-specific or goal- nonspecific. 

Goal-specific users search more and consume less content in greater detail than goal-nonspecific users: 

they spend more time using Pinterest, but are less likely to return in the near future. 

Users with short-term goals are also more focused and more likely to refer to past saved content than users with long-term goals, but less likely to save content for the future. 

Further, intent can vary by demographic, and with the topic of interest. Last, we show that user’s intent and activity are intimately related by building a model that can predict a user’s intent for using Pinterest after observing their activity for only two minutes.

paper:Understanding Behaviors that Lead to Purchasing: A Case Study of Pinterest

We analyze the purchasing behavior of nearly three million Pinterest users to determine short-term and long-term signals in user behavior that indicate higher purchase intent. We find that users with long-term purchas- ing intent tend to save and clickthrough on more content. However, as users approach the time of purchase their ac- tivity becomes more topically focused and actions shift from saves to searches.

We further find that purchase signals in online behavior can exist weeks before a purchase is made and can also be traced across different purchase categories.

paper:Visual Discovery at Pinterest

pinterest 视觉发现引擎,经验总结:

(1)advances in computer vision, especially the use of convolutional networks and GPU acceleration, have led to significant improvements in tasks such as image classifica- tion and object detection.

(2)a substantial number of users prefer using discovery systems to browse rather than to search.

实验

(1) FEATURE REPRESENTATION 特征表示学习

基于已有的模型,adopted and evaluated several popular classification models such as AlexNet[20], GoogLeNet [35], VGG16 [32], and variants ResNet101 and ResNet152[12].

(2) OBJECT DETECTION

Extracting objects from images not only allows us to build novel discovery experiences (e.g. Section 8), but also improves user engagement metrics;算法Faster R-CNN

(3)PINTEREST RELATED PINS

相关性推荐Related Pins is a pin recommendation system that lever- ages the vast amount of human-curated content on Pinter- est to provide personalized recommendations of pins based on a given query pin.Covnet features for recommendations;



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