The primary method for news recommendations revolves around leveraging the user’s browsing history to gauge their interests. Existing models prioritize analyzing news content to infer user interests, ignoring the role of label category information ...

In recommendation systems, users often click on some items that are distinct from historically clicked items. This verifies the existence of interest gaps between the historical interests reflected by historical behaviors and current interests. ...

• We are the first to apply the smoothing mechanism to bridge interest gaps in the interest modeling task.
• We propose two types of data-driven smoothing approaches based on the assumption of collaborative filtering.
• A novel iterative ...

Nowadays, e-commerce platforms of hotels have become a new trend to help people book hotels online. Interest modeling aims to automatically construct user interests that are critical for e-commerce platforms. Although interest modeling has ...

• We study interest modeling for reranking hotels and identify two key challenges.
• Deep click concept is formally defined to model the multi-view semantics of a click.
• Mutual attention calibration unit is designed to calibrate the ...

In the advertising and marketing of e-commerce platform, click rate prediction is directly related to the revenue of e-commerce platform. In this paper, we propose an advertising click-through rate prediction model based on user ...

• TW-GRU is proposed to model the user’s historical behavior in a temporal order.

In this paper, we focus on semantic interest modeling and present SIMT as a toolkit that harnesses the semantic information to effectively generate user interest models and compute their similarities. SIMT follows a mixed-method approach that combines ...

At present, one of the key directions of MOOC research is to meet the individual learning needs of learners, while the focus of personalized learning is to model learners’ interest in learning, and whether the model can accurately reflect learners’ ...

This paper proposes a method to quantify interest. In common terminology, when we engage with an object, e.g. Online Games, Social Networking Websites, Mobile Apps, etc., there is a degree of interest between us and the object. But, ...

• A general method to predict interest towards any entity (e.g. Facebook, WhatsAPP, Twitter etc.) is proposed.

Capturing user interest accurately is a key task for predicting personalized sequential action in recommender systems. Through preliminary investigation, we find that user interest is stable in short term, while changeable in long term. The user ...