Mamo: Memory-augmented meta-optimization for cold-start recommendation
A common challenge for most current recommender systems is the cold-start problem. Due
to the lack of user-item interactions, the fine-tuned recommender systems are unable to …
to the lack of user-item interactions, the fine-tuned recommender systems are unable to …
Addressing the item cold-start problem by attribute-driven active learning
In recommender systems, cold-start issues are situations where no previous events, eg,
ratings, are known for certain users or items. In this paper, we focus on the item cold-start …
ratings, are known for certain users or items. In this paper, we focus on the item cold-start …
Attribute graph neural networks for strict cold start recommendation
Rating prediction is a classic problem underlying recommender systems. It is traditionally
tackled with matrix factorization. Recently, deep learning based methods, especially graph …
tackled with matrix factorization. Recently, deep learning based methods, especially graph …
Beyond globally optimal: Focused learning for improved recommendations
When building a recommender system, how can we ensure that all items are modeled well?
Classically, recommender systems are built, optimized, and tuned to improve a global …
Classically, recommender systems are built, optimized, and tuned to improve a global …
Preference elicitation as an optimization problem
The new user coldstart problem arises when a recommender system does not yet have any
information about a user. A common solution to it is to generate a profile by asking the user …
information about a user. A common solution to it is to generate a profile by asking the user …
A survey of collaborative filtering algorithms for social recommender systems
This paper introduces the status of social recommender system research in general and
collaborative filtering in particular. For the collaborative filtering, the paper shows the basic …
collaborative filtering in particular. For the collaborative filtering, the paper shows the basic …
Short-term satisfaction and long-term coverage: Understanding how users tolerate algorithmic exploration
Any learning algorithm for recommendation faces a fundamental trade-off between
exploiting partial knowledge of a user» s interests to maximize satisfaction in the short term …
exploiting partial knowledge of a user» s interests to maximize satisfaction in the short term …
Expediting exploration by attribute-to-feature mapping for cold-start recommendations
The item cold-start problem is inherent to collaborative filtering (CF) recommenders where
items and users are represented by vectors in a latent space. It emerges since CF …
items and users are represented by vectors in a latent space. It emerges since CF …
Excuseme: Asking users to help in item cold-start recommendations
The item cold-start problem is of a great importance in collaborative filtering (CF)
recommendation systems. It arises when new items are added to the inventory and the …
recommendation systems. It arises when new items are added to the inventory and the …
[HTML][HTML] Automated test assembly for handling learner cold-start in large-scale assessments
In large-scale assessments such as the ones encountered in MOOCs, a lot of usage data is
available because of the number of learners involved. Newcomers, that just arrive on a …
available because of the number of learners involved. Newcomers, that just arrive on a …