A survey of collaborative filtering-based recommender systems: From traditional methods to hybrid methods based on social networks
R Chen, Q Hua, YS Chang, B Wang, L Zhang… - IEEE …, 2018 - ieeexplore.ieee.org
In the era of big data, recommender system (RS) has become an effective information
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …
filtering tool that alleviates information overload for Web users. Collaborative filtering (CF) …
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 …
Explainable outfit recommendation with joint outfit matching and comment generation
Most previous work on outfit recommendation focuses on designing visual features to
enhance recommendations. Existing work neglects user comments of fashion items, which …
enhance recommendations. Existing work neglects user comments of fashion items, which …
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 …
Combining community-based knowledge with association rule mining to alleviate the cold start problem in context-aware recommender systems
Abstract Successful Location-Based Services should offer accurate and timely information
consumption recommendations to their customers, relevant to their contextual situation. To …
consumption recommendations to their customers, relevant to their contextual situation. To …
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 …
Leveraging semantic features for recommendation: Sentence-level emotion analysis
Personalized recommendation systems can help users to filter redundant information from a
large amount of data. Previous relevant researches focused on learning user preferences by …
large amount of data. Previous relevant researches focused on learning user preferences by …
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 …
Local representative-based matrix factorization for cold-start recommendation
Cold-start recommendation is one of the most challenging problems in recommender
systems. An important approach to cold-start recommendation is to conduct an interview for …
systems. An important approach to cold-start recommendation is to conduct an interview for …
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 …