Temporal-Contextual Recommendation in Real-Time

@article{Ma2020TemporalContextualRI,
  title={Temporal-Contextual Recommendation in Real-Time},
  author={Yifei Ma and Balakrishnan Narayanaswamy and Haibin Lin and Hao Ding},
  journal={Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
  year={2020}
}
  • Y. Ma, B. Narayanaswamy, +1 author Hao Ding
  • Published 2020
  • Computer Science
  • Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Personalized real-time recommendation has had a profound impact on retail, media, entertainment and other industries. However, developing recommender systems for every use case is costly, time consuming and resource-intensive. To fill this gap, we present a black-box recommender system that can adapt to a diverse set of scenarios without the need for manual tuning. We build on techniques that go beyond simple matrix factorization to incorporate important new sources of information: the temporal… Expand
Recurrent Intensity Modeling for User Recommendation and Online Matching
Many applications such as recommender systems (RecSys) are built upon streams of events, each associated with a type in a large-cardinality set and a timestamp in the continuous domain. To date, mostExpand
Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Offline RL
TLDR
A new batch RL algorithm called Short Horizon Policy Improvement (SHPI) is developed that approximates policy-induced distribution shifts across sessions and recovers well-known policy improvement schemes in the RL literature. Expand
Scalable representation learning and retrieval for display advertising
TLDR
This work shows that combining large-scale matrix factorization with lightweight embedding fine-tuning unlocks state-of-the-art performance at scale, and proposes an efficient model (LED, for Lightweight EncoderDecoder) reaching a new trade-off between complexity, scale and performance. Expand
Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Reinforcement Learning
TLDR
A new reinforcement learning algorithm called Short Horizon Policy Improvement (SHPI) is developed that approximates policy-induced drift in user behavior across sessions and can outperform state-of-the-art recommendation techniques like matrix factorization with offline proxy signals, bandits with myopic online proxies, and RL baselines with limited amounts of user interaction. Expand
Bootstrapping Recommendations at Chrome Web Store
TLDR
This paper describes how three recommender systems for discovering relevant extensions in CWS were developed and deployed, namely non-personalized recommendations, related extension recommendations, and personalized recommendations, which significantly reduces development cycles and bypasses various real-world difficulties. Expand
Recommender systems based on graph embedding techniques: A comprehensive review
  • Yue Deng
  • Computer Science
  • ArXiv
  • 2021
TLDR
Comparing several representative graph embedding-based recommendation models with the most common-used conventional recommendation models, on simulations, manifests that the conventional models overall outperform the graph embeding-based ones in predicting implicit user-item interactions, revealing the relative weakness of graph embeddings- based recommendation in these tasks. Expand
SIGIR 2021 E-Commerce Workshop Data Challenge
TLDR
The need for efficient procedures for personalization is even clearer if the authors consider the e-commerce landscapemore broadly, and the constraints of the problem are stricter, due to smaller user bases and the realization that most users are not frequently returning customers. Expand
Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Models
  • D. Mudigere, Yuchen Hao, +50 authors Vijay Rao
  • Computer Science
  • 2021
TLDR
Neo is a software-hardware co-designed system for high-performance distributed training of large-scale DLRMs that employs a novel 4D parallelism strategy that combines table- wise, rowwise, column-wise, and data parallelism for training massive embedding operators inDLRMs. Expand
Temporal Modeling of User Preferences in Recommender System
TLDR
A dynamic model of user preferences is proposed for refining recommendations in the recommender system's online mode and allows selecting the data that recorded users' choices with similar changes in preferences over time, making possible to reduce the time for buildingRecommendations in the online mode while maintaining their accuracy. Expand
Collaborative Learning and Personalization in Multi-Agent Stochastic Linear Bandits
TLDR
It is shown that, an agent i whose parameter deviates from the population average by i, attains a regret scaling of Õ( i √ T ), which demonstrates that if the user representations are close, the resulting regret is low, and vice-versa. Expand
...
1
2
...

References

SHOWING 1-10 OF 49 REFERENCES
Session-based Recommendations with Recurrent Neural Networks
TLDR
It is argued that by modeling the whole session, more accurate recommendations can be provided by an RNN-based approach for session-based recommendations, and introduced several modifications to classic RNNs such as a ranking loss function that make it more viable for this specific problem. Expand
Learning Tree-based Deep Model for Recommender Systems
TLDR
A novel tree-based method which can provide logarithmic complexity w.r.t. corpus size even with more expressive models such as deep neural networks is proposed and can be jointly learnt towards better compatibility with users' interest distribution and hence facilitate both training and prediction. Expand
Top-K Off-Policy Correction for a REINFORCE Recommender System
TLDR
This work presents a general recipe of addressing biases in a production top-K recommender system at Youtube, built with a policy-gradient-based algorithm, i.e. REINFORCE, and proposes a noveltop-K off-policy correction to account for the policy recommending multiple items at a time. Expand
Recurrent Recommender Networks
TLDR
Recurrent Recommender Networks (RRN) are proposed that are able to predict future behavioral trajectories by endowing both users and movies with a Long Short-Term Memory (LSTM) autoregressive model that captures dynamics, in addition to a more traditional low-rank factorization. Expand
Collaborative filtering with temporal dynamics
TLDR
Two leading collaborative filtering recommendation approaches are revamp and a more sensitive approach is required, which can make better distinctions between transient effects and long term patterns. Expand
Sequential User-based Recurrent Neural Network Recommendations
TLDR
This paper extends Recurrent Neural Networks by considering unique characteristics of the Recommender Systems domain and shows how individual users can be represented in addition to sequences of consumed items in a new type of Gated Recurrent Unit to effectively produce personalized next item recommendations. Expand
Collaborative Filtering beyond the User-Item Matrix
TLDR
A comprehensive introduction to a large body of research, more than 200 key references, is provided, with the aim of supporting the further development of recommender systems exploiting information beyond the U-I matrix. Expand
Inter-Session Modeling for Session-Based Recommendation
TLDR
This work proposes a novel approach that extends an RNN recommender to be able to process the user's recent sessions, in order to improve recommendations, and is therefore able to deal with the cold start problem within sessions. Expand
Neural Collaborative Filtering
TLDR
This work strives to develop techniques based on neural networks to tackle the key problem in recommendation --- collaborative filtering --- on the basis of implicit feedback, and presents a general framework named NCF, short for Neural network-based Collaborative Filtering. Expand
BPR: Bayesian Personalized Ranking from Implicit Feedback
TLDR
This paper presents a generic optimization criterion BPR-Opt for personalized ranking that is the maximum posterior estimator derived from a Bayesian analysis of the problem and provides a generic learning algorithm for optimizing models with respect to B PR-Opt. Expand
...
1
2
3
4
5
...