Search Results for author:
Found 13 papers, 6 papers with code
Lot or Not: Identifying Multi-Quantity Offerings in E-Commerce
The term lot in is defined to mean an offering that contains a collection of multiple identical items for sale.
Active learning with biased non-response to label requests
We conceptualise this degradation by considering the type of non-response present in the data, demonstrating that biased non-response is particularly detrimental to model performance.
TCE: A Test-Based Approach to Measuring Calibration Error
This paper proposes a new metric to measure the calibration error of probabilistic binary classifiers, called test-based calibration error (TCE).
tBDFS: Temporal Graph Neural Network Leveraging DFS
A common approach employed by most previous works is to apply a layer that aggregates information from the historical neighbors of a node.
Sequential Modeling with Multiple Attributes for Watchlist Recommendation in E-Commerce
In e-commerce, the watchlist enables users to track items over time and has emerged as a primary feature, playing an important role in users' shopping journey.
E-Commerce Dispute Resolution Prediction
E-Commerce marketplaces support millions of daily transactions, and some disagreements between buyers and sellers are unavoidable.
Time Masking for Temporal Language Models
Our world is constantly evolving, and so is the content on the web.
Event-Driven Query Expansion
A significant number of event-related queries are issued in Web search.
Reciprocal Recommender Systems: Analysis of State-of-Art Literature, Challenges and Opportunities towards Social Recommendation
Unlike most conventional recommender approaches where items are inanimate entities recommended to the users and success is solely determined upon the end users reaction to the recommendation(s) received, in a Reciprocal Recommender System (RRS) users become the item being recommended to other users.
Cross-Cultural Transfer Learning for Text Classification
In this work, we show that cross-cultural differences can be harnessed for natural language text classification.
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes.
Node Embedding over Temporal Graphs
In this work, we present a method for node embedding in temporal graphs.
Implicit Dimension Identification in User-Generated Text with LSTM Networks
In the process of online storytelling, individual users create and consume highly diverse content that contains a great deal of implicit beliefs and not plainly expressed narrative.
