Proposes using frequent sequences to improve sequential recommendation models.
problem Combining user history and recent actions for personalized recommendations.
method Uses frequent sequences to identify relevant parts of user history, embedding items based on preferences and dynamics in a unified metric model.
result Outperforms state-of-the-art methods, especially on sparse datasets.
Improved item recommendations for repeat interactions using sequence analysis.
problem Limited effectiveness of traditional recommender systems in handling repeated user-item interactions.
method Designed a recommender system that considers sequences of item interactions for each user.
result Empirically shown to give highly accurate predictions and increase sales by 5%.
Improved neural models for diverse user event sequences.
problem Challenges in modeling diverse user event sequences.
method Mixtures of latent embeddings with amortized variational inference.
result Systematic improvements over existing work for various predictive metrics.
DynGraph2Seq predicts health stages from user activity graphs in online forums.
problem Predicting health stages from changing user activities in online forums.
method Formulated user activities as dynamic graphs, used DynGraph2Seq model with hierarchical attention.
result Demonstrated effectiveness and interpretability of DynGraph2Seq.
Time-based model improves recommendation performance.
problem Efficiently handling sequences of varying lengths in recommendation systems.
method Embedding layer and TSL attention-like mechanism with inner products.
result Model outperforms complex models on Taobao User Behavior dataset.
In this paper we consider an online recommendation setting, where a platform recommends a sequence of items to its users at every time period. The users respond by selecting one of the items recommended or abandon the platform due to fatigue from seeing less useful items. Assuming a parametric stochastic model of user …
Paper uses user engagement signals to automatically label training data for AI assistants.
problem Lack of annotated training data for AI assistants.
method Leverages user engagement signals for unsupervised entity labeling and data augmentation.
result Significant accuracy gains in sequence labeling tasks and user-facing results.
Study proposes a time-aware model to predict user conversion intent.
problem Weak predictive signals from users not suitable for conversion prediction.
method Time-aware approach to model user activities and capture conversion intent signals.
result Approach outperforms other models on real-world datasets.
Recommender systems aim to find an accurate and efficient mapping from historic data of user-preferred items to a new item that is to be liked by a user. Towards this goal, energy-based sequence generative adversarial nets (EB-SeqGANs) are adopted for recommendation by learning a generative model for the time series of…
A new sequencing rule prevents miners from front-running transactions in decentralized exchanges.
problem Miners exploit their privileged position to front-run transactions, leading to unfair profits.
method Introduce verifiable sequencing rules that constrain transaction execution order and are verifiable.
result A verifiable sequencing rule ensures users receive at least fair execution prices, preventing front-running.
Enhances social spam detection using multi-level dependency of relational sequences.
problem Social spam detection in multi-relation social networks.
method Developed the Multi-level Dependency Model (MDM) to exploit long-term and short-term dependencies in user relational sequences.
result MDM improves social spam detection accuracy on a real-world multi-relational social network.
Neural M3 model adapts to diverse user behaviors over short and long timeframes.
problem Adapting to diverse user behaviors over short and long timeframes.
method Neural Multi-temporal-range Mixture Model (M3) combining short-term and long-term models with a learned gating mechanism.
result M3 consistently outperforms state-of-the-art sequential recommendation methods.
Calendar graph neural networks model user behavior with location and time data.
problem Modeling user behavior with location and time information for demographic prediction.
method Graph neural networks with a tripartite network of items, sessions, and locations, and a hierarchical calendar network.
result User embeddings preserve spatial and temporal patterns of various periodicity.
Federated learning optimizes task and resource allocation in balloon networks.
problem Minimizing energy and time consumption in task computation and transmission.
method SVM-based federated learning algorithm to dynamically adjust user associations, service sequences, and task allocations.
result Reduces the weighted sum of energy and time consumption by up to 16.1%.
We present our solution to the job recommendation task for RecSys Challenge 2016. The main contribution of our work is to combine temporal learning with sequence modeling to capture complex user-item activity patterns to improve job recommendations. First, we propose a time-based ranking model applied to historical obs…
Paper tackles embedding attributed sequences in unsupervised learning.
problem Mining tasks over attributed sequences with dependencies between sequences and attributes.
method Proposes a deep multimodal learning framework, NAS, for unsupervised learning of attributed sequences.
result NAS produces task-independent embeddings for various mining tasks on real-world datasets.
Modeling a sequence of interactions between users and items (e.g., products, posts, or courses) is crucial in domains such as e-commerce, social networking, and education to predict future interactions. Representation learning presents an attractive solution to model the dynamic evolution of user and item properties, w…
Many-to-one RNN predicts user hotel clicks from browsing history.
problem Predicting user hotel clicks from browsing history.
method Combines rule-based algorithm with Gated RNN to sort accommodations.
result Promising results but computationally demanding.
Platform learns user preferences to avoid abandonment due to marketing fatigue.
problem Maximizing platform's cumulative payoff while avoiding user abandonment.
method Dynamic sequential choice model to balance exploration and exploitation.
result Proposed algorithm achieves optimal regret bound for online learning.
Algorithm improves query recommendations with immediate user feedback.
problem Lack of adaptability to immediate user feedback in query recommendation algorithms.
method Augmented transformer-based causal language models with multi-armed bandit framework.
result Substantial improvement in per-round regret compared to state-of-the-art models.
ProSeNet provides interpretable deep sequence models with natural explanations.
problem Challenges in explaining deep neural network predictions for sequence modeling.
method Prototypes derived from case-based reasoning, with criteria for simplicity, diversity, and sparsity.
result Achieves accuracy on par with state-of-the-art models while providing interpretable explanations.
RLINK uses deep reinforcement learning to improve user identity linkage across social networks.
problem Recognizing the same user across different social networks.
method Converts user identity linkage into a sequence decision problem and uses deep reinforcement learning to optimize the linkage strategy.
result Achieves better performance than state-of-the-art methods in experiments on various datasets.
CHAMELEON uses RNNs to recommend news sequences better than other methods.
problem Improving news recommendation accuracy and catalog coverage.
method Hybrid meta-architecture CHAMELEON with RNNs for sequence modeling and side information.
result Significantly higher recommendation accuracy and catalog coverage.
ComiRec framework predicts user interests for personalized recommendations.
problem Predicting user interests from sequential behavior data.
method ComiRec framework captures multiple user interests and balances recommendation accuracy and diversity.
result ComiRec achieves significant improvements over state-of-the-art models in sequential recommendation.
Seq2Seq models perform well in generating If-Then programs from natural language.
problem Creating If-Then programs for business process automation without technical expertise.
method Modeling If-Then programs as a sequence learning task using Seq2Seq approaches.
result Seq2Seq models can effectively generate If-Then programs from natural language.
COHORTNEY groups web users based on activity patterns.
problem Lack of academic discussion on cohort analysis for user behavior.
method Unsupervised non-parametric machine learning approach.
result COHORTNEY outperforms traditional methods in cohort analysis.
We present Piano Genie, an intelligent controller which allows non-musicians to improvise on the piano. With Piano Genie, a user performs on a simple interface with eight buttons, and their performance is decoded into the space of plausible piano music in real time. To learn a suitable mapping procedure for this proble…
Information extraction and user intention identification are central topics in modern query understanding and recommendation systems. In this paper, we propose DeepProbe, a generic information-directed interaction framework which is built around an attention-based sequence to sequence (seq2seq) recurrent neural network…
Predicting fine-grained interests of users with temporal behavior is important to personalization and information filtering applications. However, existing interest prediction methods are incapable of capturing the subtle degreed user interests towards particular items, and the internal time-varying drifting attention …
Inferring user characteristics such as demographic attributes is of the utmost importance in many user-centric applications. Demographic data is an enabler of personalization, identity security, and other applications. Despite that, this data is sensitive and often hard to obtain. Previous work has shown that purchase …
Improves music composition with user-defined constraints using continuous time models.
problem Combining sequence models with user-defined constraints in continuous time.
method Introduces a novel particle filter scheme for continuous time point processes.
result The particle filter scheme yields superior results in a human listening test.
New algorithms improve decision-making with limited offline data.
problem Using limited offline data to cluster users for better decision-making.
method Proposed two algorithms: Off-C2LUB and Off-CLUB to address data insufficiency.
result Both algorithms outperform existing methods under limited offline user data.
Paper improves recommendation systems by optimizing sequence of items for clicks.
problem Improving recommendation systems robustness against bots and clicks.
method Minimizing pairwise ranking loss over sequences of items, with thresholds to prevent bot influence.
result The proposed algorithms converge and outperform existing methods in various ranking measures.
We investigate an efficient context-dependent clustering technique for recommender systems based on exploration-exploitation strategies through multi-armed bandits over multiple users. Our algorithm dynamically groups users based on their observed behavioral similarity during a sequence of logged activities. In doing s…
We present and test a sequential learning algorithm for the short-term prediction of human mobility. This novel approach pairs the Exponential Weights forecaster with a very large ensemble of experts. The experts are individual sequence prediction algorithms constructed from the mobility traces of 10 million roaming mo…
MTDS improves sequence generation adaptability via latent code control.
problem Lack of adaptability in sequence generation models like RNNs.
method Hierarchical multi-task dynamical systems (MTDS) with latent code control.
result MTDS enables style transfer, interpolation, and morphing in generated sequences.
Proposes Textual Echo Cancellation to improve speech recognition.
problem Improving speech recognition performance and user experience for smart devices.
method A novel sequence-to-sequence model with multi-source attention that processes both the microphone mixture signal and source text of TTS playback.
result Demonstrates enhanced speech recognition performance and reduced latency.
Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform. How to detect fraudsters in time is a challenging problem. Most of the exi…
Click through rate (CTR) prediction is very important for Native advertisement but also hard as there is no direct query intent. In this paper we propose a large-scale event embedding scheme to encode the each user browsing event by training a Siamese network with weak supervision on the users' consecutive events. The …
In this paper, we introduce a methodology that allows to model behavioral trajectories of users in online social media. First, we illustrate how to leverage the probabilistic framework provided by Hidden Markov Models (HMMs) to represent users by embedding the temporal sequences of actions they performed online. We the…
Recommendations are broadly used in marketplaces to match users with items relevant to their interests and needs. To understand user intent and tailor recommendations to their needs, we use deep learning to explore various heterogeneous data available in marketplaces. This paper focuses on the challenge of measuring re…
SIM models user interests from long sequential behavior data, improving click-through rate prediction.
problem Challenges in capturing user interests with long user behavior sequences.
method SIM uses a cascaded search paradigm with two units: General Search Unit and Exact Search Unit.
result SIM achieves significant CTR and RPM lifts in Alibaba's display advertising system.
Recurrent Neural Networks (RNNS) are now widely used on sequence generation tasks due to their ability to learn long-range dependencies and to generate sequences of arbitrary length. However, their left-to-right generation procedure only allows a limited control from a potential user which makes them unsuitable for int…
The problem of session-based recommendation aims to predict user actions based on anonymous sessions. Previous methods model a session as a sequence and estimate user representations besides item representations to make recommendations. Though achieved promising results, they are insufficient to obtain accurate user ve…
Study assesses whether RL algorithm personalizes treatment sequences.
problem Evaluate if RL algorithm truly personalizes treatment sequences.
method Resampling-based methodology to investigate personalization.
result RL algorithm's personalization may be due to stochasticity.
SeER hybrid model improves song recommendations and explains them.
problem Improving song recommendations and explaining them.
method Collaborative filtering and deep learning sequence models on MIDI content.
result Personalized explanations capture user preferences.
Detects rumours using news propagation patterns and user interactions.
problem Rumours' negative impact on social media platforms.
method Deep learning approach that learns user representations and temporal interactions.
result State-of-the-art performance in rumour detection on Twitter and Weibo datasets.
New method uses bandit feedback to better evaluate recommender systems.
problem Traditional offline evaluation of recommender systems is inaccurate.
method Exploits bandit feedback to estimate online performance.
result Bandit feedback provides more accurate offline evaluation.