Query: user embedding

title: PinnerSage: Multi Modal User Embedding Framework for Recommendations at Pinterest
abstract: Latent user representations are widely adopted in the tech industry for powering personalized recommender systems. Most prior work infers a single high dimensional embedding to represent a user, which is a good starting point but falls short in delivering a full understanding of the user's interests. In this work, we introduce PinnerSage, an end to end recommender system that represents each user via multi modal embeddings and leverages this rich representation of users to provides high quality personalized recommendations. PinnerSage achieves this by clustering users' actions into conceptually coherent clusters with the help of a hierarchical clustering method (Ward) and summarizes the clusters via representative pins (Medoids) for efficiency and interpretability. PinnerSage is deployed in production at Pinterest and we outline the several design decisions that makes it run seamlessly at a very large scale. We conduct several offline and online A/B experiments to show that our method significantly outperforms single embedding methods.

title: Beyond User Embedding Matrix: Learning to Hash for Modeling Large Scale Users in Recommendation
abstract: Modeling large scale and rare interaction users are the two major challenges in recommender systems, which derives big gaps between researches and applications. Facing to millions or even billions of users, it is hard to store and leverage personalized preferences with a user embedding matrix in real scenarios. And many researches pay attention to users with rich histories, while users with only one or several interactions are the biggest part in real systems. Previous studies make efforts to handle one of the above issues but rarely tackle efficiency and cold start problems together. In this work, a novel user preference representation called Preference Hash (PreHash) is proposed to model large scale users, including rare interaction ones. In PreHash, a series of buckets are generated based on users' historical interactions. Users with similar preferences are assigned into the same buckets automatically, including warm and cold ones. Representations of the buckets are learned accordingly. Contributing to the designed hash buckets, only limited parameters are stored, which saves a lot of memory for more efficient modeling. Furthermore, when new interactions are made by a user, his buckets and representations will be dynamically updated, which enables more effective understanding and modeling of the user. It is worth mentioning that PreHash is flexible to work with various recommendation algorithms by taking the place of previous user embedding matrices. We combine it with multiple state of the art recommendation methods and conduct various experiments. Comparative results on public datasets show that it not only improves the recommendation performance but also significantly reduces the number of model parameters. To summarize, PreHash has achieved significant improvements in both efficiency and effectiveness for recommender systems.

title: Author2Vec: A Framework for Generating User Embedding
abstract: Online forums and social media platforms provide noisy but valuable data every day. In this paper, we propose a novel end to end neural network based user embedding system, Author2Vec. The model incorporates sentence representations generated by BERT (Bidirectional Encoder Representations from Transformers) with a novel unsupervised pre training objective, authorship classification, to produce better user embedding that encodes useful user intrinsic properties. This user embedding system was pre trained on post data of 10k Reddit users and was analyzed and evaluated on two user classification benchmarks: depression detection and personality classification, in which the model proved to outperform traditional count based and prediction based methods. We substantiate that Author2Vec successfully encoded useful user attributes and the generated user embedding performs well in downstream classification tasks without further finetuning.

title: User Embedding for Rating Prediction in SVD+ Based Collaborative Filtering
abstract: The collaborative filtering algorithm based on the singular value decomposition plus plus (SVD+ model employs the linear interactions between the latent features of users and items to predict the rating in the recommendation systems. Aiming to further enrich the user model with explicit feedback, this paper proposes a user embedding model for rating prediction in SVD+ based collaborative filtering, named UE SVD+ We exploit the user potential explicit feedback from the rating data and construct the user embedding matrix by the proposed user wise mutual information values. In addition, the user embedding matrix is added to the existing user bias and implicit parameters in the SVD+ to increase the accuracy of the user modeling. Through extensive studies on four different datasets, we found that the rating prediction performance of the UE SVD+ model is improved compared with other models, and the proposed model's evaluation indicators root mean square error (RMSE) and mean absolute error (MAE) are decreased by 1.002 2.110% and 1.182 1.742% respectively.

title: Neural User Embedding from Browsing Events
abstract: The deep understanding of online users on the basis of their behavior data is critical to providing personalized services to them. However, the existing methods for learning user representations are usually based on supervised frameworks such as demographic prediction and product recommendation. In addition, these methods highly rely on labeled data to learn user representation models, and the user representations learned using these methods can only be used in specific tasks. Motivated by the success of pretrained word embeddings in many natural language processing (NLP) tasks, we propose a simple but effective neural user embedding approach to learn the deep representations of online users by using their unlabeled behavior data. Once the users are encoded to low dimensional dense embedding vectors, these hidden user vectors can be used as additional user features in various user involved tasks, such as demographic prediction, to enrich user representation. In our neural user embedding (NEU) approach, the behavior events are represented in two ways. The first one is the ID based event embedding, which is based on the IDs of these events, and the second one is the text based event embedding, which is based on the textual content of these events. Furthermore, we conduct experiments on a real world web browsing dataset. The results show that our approach can learn informative user embeddings by using the unlabeled browsing behavior data and that these user embeddings can facilitate many tasks that involve user modeling such as user age prediction and gender prediction.

title: Neural Social Recommendation With User Embedding
abstract: Social information is usually jointly utilized with rating information to help the traditional recommendation system providing more personalized services, while how to make full use of social information to build better recommendation models still faces lots of challenges. In this paper, we propose a novel social recommendation model taking advantage of both deep and shallow model, with deep auto encoders acting as the nonlinear feature extractor and MF based method being used to depict the user's preferences. Then, motivated by the idea of word2vec, we provide an appealing method that embeds users into latent space and meanwhile preserves the structural information of social networks during the embedding. Also, the embedded latent features of each user are corresponding to the dual roles the user plays in the recommendation. Furthermore, we design a loss function for the holistic training of the model, and our loss function is made up mainly of three parts which embody the effects of different factors on the rating predictions. Specifically, the loss function (i) captures the personal preference from user item adoption matrix based on matrix factorization, (ii) discriminates the two different social functions of users and further evaluates the effects of interpersonal influence through user embedding and social influence matrix, (iii) and avoids overfitting by imposing a quadratic regularization penalty. As a result, our model can predict the missing ratings with the MF based method by consuming the latent features of users and items extracted by the deep model. The experiments show that our method outperforms existing methods and performs well on cold start users.

title: Social Media based User Embedding: A Literature Review
abstract: Automated representation learning is behind many recent success stories in machine learning. It is often used to transfer knowledge learned from a large dataset (e.g. raw text) to tasks for which only a small number of training examples are available. In this paper, we review recent advance in learning to represent social media users in low dimensional embeddings. The technology is critical for creating high performance social media based human traits and behavior models since the ground truth for assessing latent human traits and behavior is often expensive to acquire at a large scale. In this survey, we review typical methods for learning a unified user embeddings from heterogeneous user data (e.g. combines social media texts with images to learn a unified user representation) Finally we point out some current issues and future directions.

title: Predicting Early Reviewers Using User Embedding
abstract: Online audits have become a significant wellspring of data for clients before settling on an educated buy choice. Early surveys of an item will in general highly affect the consequent item deals. In this paper, we step up and study the conduct attributes of early reviewers through their posted surveys on genuine huge internet business stages, i.e. Amazon. In explicit, we isolate item lifetime into three back to back stages, in particular early, greater part and slow pokes. A client who has posted a survey in the early stage is considered as an early reviewer. We quantitatively portray early reviewers dependent on their rating practices, the accommodation scores got from others and the relationship of their surveys with item prevalence. We have discovered that (1) an early reviewer will in general relegate a higher normal rating score; and (2) an early reviewer will in general post progressively accommodating surveys. Our investigation of item audits likewise shows that early reviewers' appraisals and their got support scores are probably going to impact item notoriety. By survey audit posting process as a multiplayer rivalry game, we propose a novel edge based installing model for early reviewer expectation. Broad examinations on two diverse internet business DATA SET have demonstrated that our proposed methodology beats various focused baselines.

title: Deep Content User Embedding Model for Music Recommendation
abstract: Recently deep learning based recommendation systems have been actively explored to solve the cold start problem using a hybrid approach. However, the majority of previous studies proposed a hybrid model where collaborative filtering and content based filtering modules are independently trained. The end to end approach that takes different modality data as input and jointly trains the model can provide better optimization but it has not been fully explored yet. In this work, we propose deep content user embedding model, a simple and intuitive architecture that combines the user item interaction and music audio content. We evaluate the model on music recommendation and music auto tagging tasks. The results show that the proposed model significantly outperforms the previous work. We also discuss various directions to improve the proposed model further.

title: Cross media User Profiling with Joint Textual and Social User Embedding
abstract: In realistic scenarios, a user profiling model (e.g. gender classification or age regression) learned from one social media might perform rather poorly when tested on another social media due to the different data distributions in the two media. In this paper, we address cross media user profiling by bridging the knowledge between the source and target media with a uniform user embedding learning approach. In our approach, we first construct a cross media user word network to capture the relationship among users through the textual information and a modified cross media user user network to capture the relationship among users through the social information. Then, we learn user embedding by jointly learning the heterogeneous network composed of above two networks. Finally, we train a classification (or regression) model with the obtained user embeddings as input to perform user profiling. Empirical studies demonstrate the effectiveness of the proposed approach to two cross media user profiling tasks, i.e. cross media gender classification and cross media age regression.