Improving matrix factorization recommendations for examples in cold start

Ocepek, Uroš; Rugelj, Jože; Bosnić, Zoran

doi:10.1016/j.eswa.2015.04.071

Cited by 37 publications

(19 citation statements)

References 31 publications

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“…Based on the above two modules, various solutions are being investigated. Matrix factorization is one of the most commonly used methods to solve the cold-start user problem [10][11][12]. Social network analysis (SNA) is another common method.…”

Section: Related Workmentioning

confidence: 99%

User Group-based Method for Cold-Start Recommendation

He¹,

Yuan²,

Xiang³

et al. 2018

IJPE

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Recommendation algorithms seek to predict user ratings or preferences. Due to limited information, it is difficult to make these predictions for new users. Therefore, a dynamic cold-start recommendation algorithm would be highly helpful in such quick-changing social networks. In this paper, a novel user group-based collaborative method, called UCFRA (User group-based Cold-start Friend Recommendation Algorithm) is proposed. UCFRA integrates a graphical model and statistical population characteristics into a user group model and then combines this extended user group model with cold-start information to generate a new recommendation algorithm. Moreover, a content popularity model based on user groups and a user rating matrix is designed. In order to improve recommend precision, a user group Top-N recommendation model based on k-nearest neighbors is provided. A series of experiments involving collection of a huge data set was developed to evaluate the effectiveness of UCFRA. The experimental results showed that UCFRA is a valid algorithm.

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Section: Related Workmentioning

confidence: 99%

User Group-based Method for Cold-Start Recommendation

He¹,

Yuan²,

Xiang³

et al. 2018

IJPE

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“…Authors (Lika, Kolomvatsos, & Hadjiefthymiades, 2014) adopt users' demographic data and apply a simple prediction rule by summing weighted ratings made by similar users to produce ratings for new users. Authos (Ocepeka, Rugeljb, & Bosnica, 2015) combines attribute selection and local learning into the recommendation model for CS users. Both (Lika, Kolomvatsos, & Hadjiefthymiades, 2014) and (Ocepeka, Rugeljb, & Bosnica, 2015) used one of our baseline approaches (the ToU approach) in models to recommend products to CS users.…”

Section: Related Workmentioning

confidence: 99%

Collaborative Filtering and Deep Learning Based Hybrid Recommendation for Cold Start Problem

Jian

Jiang

Chen

et al. 2016

2016 IEEE 14th Intl Conf on Dependable, Autonomic and Secure Computing, 14th Intl Conf on Pervasive Intelligence and Computing,

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Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications.Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items.Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem

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“…Many other papers propose matrix factorization for solving specific problems in recommender systems; see for instance (Ocepek et al, 2015).…”

Section: Related Workmentioning

confidence: 99%

Mapping preferences into Euclidean space

Luaces

Díez

Joachims

et al. 2015

Expert Systems with Applications

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Understanding and modeling human preferences is one of the key problems in applications ranging from marketing to automated recommendation. In this paper, we focus on learning and analyzing the preferences of consumers regarding food products. In particular, we explore machine learning methods that embed consumers and products in an Euclidean space such that their relationship to each other models consumer preferences. In addition to predicting preferences that were not explicitly stated, the Euclidean embedding enables visualization and clustering to understand the overall structure of a population of consumers and their preferences regarding the set of products.Notice that consumers' clusters are market segments, and products clusters can be seen as groups of similar items with respect to consumer tastes. We explore two types of Euclidean embedding of preferences, one based on inner products and one based on distances. Using a real world dataset about consumers of beef meat, we find that both embeddings produce more accurate models than a tensorial approach that uses a SVM to learn preferences. The * Corresponding author: Tel: +34 985 182 588Email addresses: oluaces@uniovi.es (Oscar Luaces), jdiez@uniovi.es (Jorge Díez), tj@cs.cornell.edu (Thorsten Joachims), abahamonde@uniovi.es (Antonio Bahamonde) Preprint submitted to Expert Systems with ApplicationsJuly 15, 2015 reason is that the number of parameters to learned in embeddings can be considerably lower than in the tensorial approach. Furthermore, we demonstrate that the visualization of the learned embeddings provides interesting insights into the structure of the consumer and product space, and that it provides a method for qualitatively explaining consumer preferences. Additionally, it is important to emphasize that the approach presented here is flexible enough to allow its use with different levels of knowledge about consumers or products; therefore the application field is very wide to grasp an accurate understanding of consumers' preferences.

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Improving matrix factorization recommendations for examples in cold start

Cited by 37 publications

References 31 publications

User Group-based Method for Cold-Start Recommendation

User Group-based Method for Cold-Start Recommendation

Collaborative Filtering and Deep Learning Based Hybrid Recommendation for Cold Start Problem

Mapping preferences into Euclidean space

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