Large-scale Collaborative Filtering with Product Embeddings

Abstract

The application of machine learning techniques to large-scale personalized recommendation problems is a challenging task. Such systems must make sense of enormous amounts of implicit feedback in order to understand user preferences across numerous product categories. This paper presents a deep learning based solution to this problem within the collaborative filtering with implicit feedback framework. Our approach combines neural attention mechanisms, which allow for context dependent weighting of past behavioral signals, with representation learning techniques to produce models which obtain extremely high coverage, can easily incorporate new information as it becomes available, and are computationally efficient. Offline experiments demonstrate significant performance improvements when compared to several alternative methods from the literature. Results from an online setting show that the approach compares favorably with current production techniques used to produce personalized product recommendations.

Deep personalized re-targeting

“…Recurrent neural networks have shown to be effective in capturing temporal dependencies between user item views [38] [39] [40] [35] [32] and convolutional neural networks have shown success in capturing latent intent structure in the item images [41] [40] [42]. Recently, a new architecture called attention networks have gained popularity, due to its ability to automatically reweigh all signals that users can capture, resembling user memory attention [43] [44] [45] [46]. In this study, we evaluate the merits of various candidate solutions such as Deep Average Networks, Long Short Term Memory, and Attention networks in adding value to shopping intent and value prediction.…”

Automatic Collection Creation and Recommendation

“…For example, in movie recommendation, we need to recommend personalized collections based on specific genres such as thriller and comedy [8,12]. In case of e-commerce, collections are recommended based on the product categories [20]. Search and information retrieval is another area where collection based presentation is relatively a better approach.…”

Time-based Sequence Model for Personalization and Recommendation Systems

“…Recommendation systems play an important role in many e-commerce applications as well as search and ranking services [6,15,21,26,30,31,41,48]. There are two main strategies to perform recommendations: content and collaborative filtering.…”