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Found 11 papers, 2 papers with code
Learning Personalized User Preference from Cold Start in Multi-turn Conversations
This paper presents a novel teachable conversation interaction system that is capable of learning users preferences from cold start by gradually adapting to personal preferences.
Personalized Search Via Neural Contextual Semantic Relevance Ranking
Existing neural relevance models do not give enough consideration for query and item context information which diversifies the search results to adapt for personal preference.
STREET: A Multi-Task Structured Reasoning and Explanation Benchmark
We introduce STREET, a unified multi-task and multi-domain natural language reasoning and explanation benchmark.
Robust Consensus Clustering and its Applications for Advertising Forecasting
Consensus clustering aggregates partitions in order to find a better fit by reconciling clustering results from different sources/executions.
Do not Waste Money on Advertising Spend: Bid Recommendation via Concavity Changes
In computational advertising, a challenging problem is how to recommend the bid for advertisers to achieve the best return on investment (ROI) given budget constraint.
Demystifying Advertising Campaign Bid Recommendation: A Constraint target CPA Goal Optimization
In cost-per-click (CPC) or cost-per-impression (CPM) advertising campaigns, advertisers always run the risk of spending the budget without getting enough conversions.
Language Agnostic Multilingual Information Retrieval with Contrastive Learning
Multilingual information retrieval (IR) is challenging since annotated training data is costly to obtain in many languages.
DeepLight: Deep Lightweight Feature Interactions for Accelerating CTR Predictions in Ad Serving
To address the issue of significantly increased serving delay and high memory usage for ad serving in production, this paper presents \emph{DeepLight}: a framework to accelerate the CTR predictions in three aspects: 1) accelerate the model inference via explicitly searching informative feature interactions in the shallow component; 2) prune redundant layers and parameters at intra-layer and inter-layer level in the DNN component; 3) promote the sparsity of the embedding layer to preserve the most discriminant signals.
Ranked #7 on
Click-Through Rate Prediction
on Avazu
Bayesian Time Series Forecasting with Change Point and Anomaly Detection
However, their performance is easily undermined by the existence of change points and anomaly points, two structures commonly observed in real data, but rarely considered in the aforementioned methods.
DeepRebirth: Accelerating Deep Neural Network Execution on Mobile Devices
As observed in the experiment, DeepRebirth achieves more than 3x speed-up and 2. 5x run-time memory saving on GoogLeNet with only 0. 4% drop of top-5 accuracy on ImageNet.
Exclusive Feature Learning on Arbitrary Structures via \ell_{1,2}-norm
Group lasso is widely used to enforce the structural sparsity, which achieves the sparsity at inter-group level.
