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Cold-start solutions for recommendation systems

2019
Recommendation systems are essential tools to overcome the choice overload problem by suggesting items of interest to users. However, they suffer from a major challenge which is the so-called cold-start problem. The cold-start problem typically happens when the system does not have any form of data on new users and on new items.
Moghaddam, Farshad Bakhshandegan, Elahi, Mehdi   +1 more
openaire   +1 more source

Searching to Modulate for Cold-Start Recommendation

IEEE Transactions on Pattern Analysis and Machine Intelligence
Making personalized recommendation for cold-start users, who only have a few interaction histories, is a challenging problem in recommendation systems. Recent works leverage hypernetworks to directly map interaction histories to user-specific parameters, which are then used to modulate predictor by certain modulation structure.
Shiguang Wu 0002, Yaqing Wang 0002, Quanming Yao   +2 more
openaire   +2 more sources

Tied boltzmann machines for cold start recommendations

Proceedings of the 2008 ACM conference on Recommender systems, 2008
We describe a novel statistical model, the tied Boltzmann machine, for combining collaborative and content information for recommendations. In our model, pairwise interactions between items are captured through a Boltzmann machine, whose parameters are constrained according to the content associated with the items.
Asela Gunawardana, Christopher Meek
openaire   +1 more source

Item cold-start recommendations

Proceedings of the 8th ACM Conference on Recommender systems, 2014
Recommender systems suggest to users items that they might like (e.g., news articles, songs, movies) and, in doing so, they help users deal with information overload and enjoy a personalized experience. One of the main problems of these systems is the item cold-start, i.e., when a new item is introduced in the system and no past information is ...
Martin Saveski, Amin Mantrach
openaire   +2 more sources

A Hybrid Solution For The Cold Start Problem In Recommendation

The Computer Journal, 2023
Abstract Recommender systems are becoming more and more significant in today’s digital world and in the modern economy. They make a substantial contribution to company operations by offering tailored advice and decreasing overwhelm. Collaborative filtering, being popular in the domain of recommendation, is used to offer recommendations ...
Syed Irteza Hussain Jafri, Rozaida Ghazali, Irfan Javid, Yana Mazwin Mohmad Hassim, Mubashir Hayat Khan   +4 more
openaire   +2 more sources

Visually-Aware Video Recommendation in the Cold Start

Proceedings of the 31st ACM Conference on Hypertext and Social Media, 2020
Recommender Systems have become essential tools in any modern video-sharing platform. Although, recommender systems have shown to be effective in generating personalized suggestions in video-sharing platforms, however, they suffer from the so-called New Item problem.
Elahi, M., Hosseini, R., Rimaz, M. H., Moghaddam, F. B., Trattner, C.   +4 more
openaire   +2 more sources

Tools and techniques for solving cold start recommendation

Proceedings of the 1st International Conference on Internet of Things and Machine Learning, 2017
Recommendation Systems are very important systems that saves users time and resources by saving them from searching the bulk data. The best example is googling which searches and gives list of hundreds of pages. Therefore, a major challenge of Recommendation Systems can be how to make recommendations for a new user, that is called cold-start user ...
Nitin Mishra, Vimal Mishra, Saumya Chaturvedi   +2 more
openaire   +1 more source

Functional matrix factorizations for cold-start recommendation

Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, 2011
A key challenge in recommender system research is how to effectively profile new users, a problem generally known as cold-start recommendation. Recently the idea of progressively querying user responses through an initial interview process has been proposed as a useful new user preference elicitation strategy.
Ke Zhou 0002, Shuang-Hong Yang, Hongyuan Zha   +2 more
openaire   +1 more source

Variational cold-start resistant recommendation

Information Sciences, 2022
Joojo Walker, Fengli Zhang, Ting Zhong, Fan Zhou 0002, Edward Yellakuor Baagyere   +4 more
openaire   +2 more sources

Pairwise preference regression for cold-start recommendation

Proceedings of the third ACM conference on Recommender systems, 2009
Recommender systems are widely used in online e-commerce applications to improve user engagement and then to increase revenue. A key challenge for recommender systems is providing high quality recommendation to users in ``cold-start" situations. We consider three types of cold-start problems: 1) recommendation on existing items for new users; 2 ...
Seung-Taek Park, Wei Chu
openaire   +2 more sources