A literature review and classification of recommender systems research
DH Park, HK Kim, IY Choi, JK Kim - Expert systems with applications, 2012 - Elsevier
Recommender systems have become an important research field since the emergence of
the first paper on collaborative filtering in the mid-1990s. Although academic research on …
the first paper on collaborative filtering in the mid-1990s. Although academic research on …
Approaches and algorithms to mitigate cold start problems in recommender systems: a systematic literature review
Cold Start problems in recommender systems pose various challenges in the adoption and
use of recommender systems, especially for new item uptake and new user engagement …
use of recommender systems, especially for new item uptake and new user engagement …
Recommender systems survey
Recommender systems have developed in parallel with the web. They were initially based
on demographic, content-based and collaborative filtering. Currently, these systems are …
on demographic, content-based and collaborative filtering. Currently, these systems are …
A collaborative filtering approach to mitigate the new user cold start problem
The new user cold start issue represents a serious problem in recommender systems as it
can lead to the loss of new users who decide to stop using the system due to the lack of …
can lead to the loss of new users who decide to stop using the system due to the lack of …
A hybrid recommendation approach for a tourism system
Many current e-commerce systems provide personalization when their content is shown to
users. In this sense, recommender systems make personalized suggestions and provide …
users. In this sense, recommender systems make personalized suggestions and provide …
Simultaneous co-clustering and learning to address the cold start problem in recommender systems
ALV Pereira, ER Hruschka - Knowledge-Based Systems, 2015 - Elsevier
Abstract Recommender Systems (RSs) are powerful and popular tools for e-commerce. To
build their recommendations, RSs make use of varied data sources, which capture the …
build their recommendations, RSs make use of varied data sources, which capture the …
A collaborative filtering similarity measure based on singularities
Recommender systems play an important role in reducing the negative impact of information
overload on those websites where users have the possibility of voting for their preferences …
overload on those websites where users have the possibility of voting for their preferences …
Collaborative filtering based on significances
It seems reasonable to think that there may be some items and some users in a
recommender system that could be highly significant in making recommendations. For …
recommender system that could be highly significant in making recommendations. For …
Combining community-based knowledge with association rule mining to alleviate the cold start problem in context-aware recommender systems
I Viktoratos, A Tsadiras, N Bassiliades - Expert systems with applications, 2018 - Elsevier
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 …
Multi-feature hierarchical template matching using distance transforms
DM Gavrila - … conference on pattern recognition (Cat. No …, 1998 - ieeexplore.ieee.org
We describe a multi-feature hierarchical algorithm to efficiently match N objects (templates)
with am image using distance transforms (DTs). The matching is under translation, but it can …
with am image using distance transforms (DTs). The matching is under translation, but it can …