User Centric Recommendations

Aliases Session Based Predictions

Intent

Train networks that incorporate user information.

Motivation

How build a scalable predictor that incorporates user information?

Sketch

This section provides alternative descriptions of the pattern in the form of an illustration or alternative formal expression. By looking at the sketch a reader may quickly understand the essence of the pattern. Discussion

This is the main section of the pattern that goes in greater detail to explain the pattern. We leverage a vocabulary that we describe in the theory section of this book. We don’t go into intense detail into providing proofs but rather reference the sources of the proofs. How the motivation is addressed is expounded upon in this section. We also include additional questions that may be interesting topics for future research.

Known Uses

Here we review several projects or papers that have used this pattern.

Related Patterns In this section we describe in a diagram how this pattern is conceptually related to other patterns. The relationships may be as precise or may be fuzzy, so we provide further explanation into the nature of the relationship. We also describe other patterns may not be conceptually related but work well in combination with this pattern.

Relationship to Canonical Patterns

Relationship to other Patterns

Further Reading

We provide here some additional external material that will help in exploring this pattern in more detail.

References

http://www-users.cs.umn.edu/~mcnee/mcnee-chi06-acc.pdf Accurate is not always good: How Accuracy Metrics have hurt Recommender Systems

http://www.bgu.ac.il/~shanigu/Publications/EvaluationMetrics.17.pdf Evaluating Recommendation Systems

http://technocalifornia.blogspot.com/2014/12/ten-lessons-learned-from-building-real.html

http://users.cecs.anu.edu.au/~u5098633/papers/www15.pdf AutoRec: Autoencoders Meet Collaborative Filtering

https://alexiskz.files.wordpress.com/2016/06/feature-rnn-paper1.pdf Parallel Recurrent Neural Network Architectures for Feature-rich Session-based Recommendations

We show that p-RNN architectures with proper training have significant performance improvements over feature-less session models while all session-based models outperform the item-to-item type baseline.

https://arxiv.org/abs/1603.04259 Item2Vec: Neural Item Embedding for Collaborative Filtering

We show that item-based CF can be cast in the same framework of neural word embedding.

http://arxiv.org/abs/1606.07792 Wide & Deep Learning for Recommender Systems

https://github.com/amznlabs/amazon-dsstne

http://arxiv.org/abs/1511.06939 Session-based Recommendations with Recurrent Neural Networks

Real-life recommender systems often face the problem of having to base recommendations only on short session-based data (e.g. a small sportsware website) instead of long user histories (as in the case of Netflix). In this situation the frequently praised matrix factorization approaches are not accurate. This problem is usually overcome in practice by resorting to item-to-item recommendations, i.e. recommending similar items. We argue that by modeling the whole session, more accurate recommendations can be provided.

http://googleresearch.blogspot.ca/2016/05/chat-smarter-with-allo.html The best part about these suggestions is that over time they are personalized to you so that your individual style is reflected in your conversations. For example, if you often reply to “How are you?” with “Fine.” instead of “I am good.”, it will learn your preference and your future suggestions will take that into account. This was accomplished by incorporating a user's “style” as one of the features in a Neural Network that is used to predict the next word in a response, resulting in suggestions that are customized for your personality and individual preferences. The user's style is captured in a sequence of numbers that we call the user embedding. These embeddings can be generated as part of the regular model training, but this approach requires waiting for many days for training to be complete and it cannot handle more than a handful of millions of users. To solve this issue, Alon Shafrir implemented a L-BFGS based technique to generate user embeddings quickly and at scale. Now, you'll be able to enjoy personalized suggestions after only a short time of using Allo.

https://arxiv.org/abs/1604.01252 Comparative Deep Learning of Hybrid Representations for Image Recommendations

We design a dual-net deep network, in which the two sub-networks map input images and preferences of users into a same latent semantic space, and then the distances between images and users in the latent space are calculated to make decisions. We further propose a comparative deep learning (CDL) method to train the deep network, using a pair of images compared against one user to learn the pattern of their relative distances. The CDL embraces much more training data than naive deep learning, and thus achieves superior performance than the latter, with no cost of increasing network complexity.

http://arxiv.org/abs/1603.06155 A Persona-Based Neural Conversation Model

We present persona-based models for handling the issue of speaker consistency in neural response generation. A speaker model encodes personas in distributed embeddings that capture individual characteristics such as background information and speaking style. A dyadic speaker-addressee model captures properties of interactions between two interlocutors. Our models yield qualitative performance improvements in both perplexity and BLEU scores over baseline sequence-to-sequence models, with similar gains in speaker consistency as measured by human judges.

http://research.google.com/pubs/pub45530.html Deep Neural Networks for YouTube Recommendations

http://openreview.net/pdf?id=r1w7Jdqxl COLLABORATIVE DEEP EMBEDDING VIA DUAL NETWORKS

In this method, a pair of dual networks, one for encoding items and the other for users, are jointly trained in a collaborative fashion. Particularly, both networks produce embeddings at multiple aligned levels, which, when combined together, can accurately predict the matching between items and users.

https://arxiv.org/abs/1409.2944 Collaborative Deep Learning for Recommender Systems

Collaborative filtering (CF) is a successful approach commonly used by many recommender systems. Conventional CF-based methods use the ratings given to items by users as the sole source of information for learning to make recommendation. However, the ratings are often very sparse in many applications, causing CF-based methods to degrade significantly in their recommendation performance. To address this sparsity problem, auxiliary information such as item content information may be utilized. Collaborative topic regression (CTR) is an appealing recent method taking this approach which tightly couples the two components that learn from two different sources of information. Nevertheless, the latent representation learned by CTR may not be very effective when the auxiliary information is very sparse. To address this problem, we generalize recent advances in deep learning from i.i.d. input to non-i.i.d. (CF-based) input and propose in this paper a hierarchical Bayesian model called collaborative deep learning (CDL), which jointly performs deep representation learning for the content information and collaborative filtering for the ratings (feedback) matrix. Extensive experiments on three real-world datasets from different domains show that CDL can significantly advance the state of the art.

https://arxiv.org/pdf/1608.07400v1.pdf Collaborative Filtering with Recurrent Neural Networks

We show that collaborative filtering can be viewed as a sequence prediction problem, and that given this interpretation, recurrent neural networks offer very competitive approach. In particular we study how the long short-term memory (LSTM) can be applied to collaborative filtering, and how it compares to standard nearest neighbors and matrix factorization methods on movie recommendation. We show that the LSTM is competitive in all aspects, and largely outperforms other methods in terms of item coverage and short term predictions.

https://arxiv.org/pdf/1702.05512.pdf soc2seq: Social Embedding meets Conversation Model

While liking or upvoting a post on a mobile app is easy to do, replying with a written note is much more difficult, due to both the cognitive load of coming up with a meaningful response as well as the mechanics of entering the text. Here we present a novel textual reply generation model that goes beyond the current auto-reply and predictive text entry models by taking into account the content preferences of the user, the idiosyncrasies of their conversational style, and even the structure of their social graph. Specifically, we have developed two types of models for personalized user interactions: a content-based conversation model, which makes use of location together with user information, and a social-graph-based conversation model, which combines content-based conversation models with social graphs.

https://arxiv.org/abs/1706.02263v1 Graph Convolutional Matrix Completion

n this paper we revisit matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges representing observed ratings. Building on recent progress in deep learning on graph-structured data, we propose a graph auto-encoder framework based on differentiable message passing on the bipartite interaction graph. This framework can be viewed as an important first step towards end-to-end learning in settings where the interaction data is integrated into larger graphs such as social networks or knowledge graphs, circumventing the need for multistage frameworks. Our model achieves competitive performance on standard collaborative filtering benchmarks, significantly outperforming related methods in a recommendation task with side information.

http://delivery.acm.org/10.1145/3020000/3018712/p791-bendersky.pdf Learning from User Interactions in Personal Search via Attribute Parameterization

https://github.com/Leavingseason/OpenLearning4DeepRecsys

https://arxiv.org/abs/1706.02263 Graph Convolutional Matrix Completion We consider matrix completion for recommender systems from the point of view of link prediction on graphs. Interaction data such as movie ratings can be represented by a bipartite user-item graph with labeled edges denoting observed ratings. Building on recent progress in deep learning on graph-structured data, we propose a graph auto-encoder framework based on differentiable message passing on the bipartite interaction graph. Our model shows competitive performance on standard collaborative filtering benchmarks. In settings where complimentary feature information or structured data such as a social network is available, our framework outperforms recent state-of-the-art methods.

https://engineering.pinterest.com/sites/engineering/files/article/fields/field_image/human-curation-convnets%20%281%29.pdf Human Curation and Convnets: Powering Item-to-Item Recommendations on Pinterest

https://github.com/ylongqi/openrec

https://medium.com/@Pinterest_Engineering/pinsage-a-new-graph-convolutional-neural-network-for-web-scale-recommender-systems-88795a107f48 PinSage: A New Graph Convolutional Neural Network for Web-Scale Recommender Systems

Unlike Euclidean spaces, Hyperbolic spaces are intrinsically equipped to handle hierarchical structure, encouraged by its property of exponentially increasing distances away from origin. We propose HyperBPR (Hyperbolic Bayesian Personalized Ranking), a conceptually simple but highly effective model for the task at hand. Our proposed HyperBPR not only outperforms their Euclidean counterparts, but also achieves state-of-the-art performance on multiple benchmark datasets, demonstrating the effectiveness of personalized recommendation in Hyperbolic space.