Abstract
An essential problem in real-world recommender systems is that user preferences are not static and users are likely to change their preferences over time. Recent studies have shown that the modelling and capturing the dynamics of user preferences lead to significant improvements on recommendation accuracy and, consequently, user satisfaction. In this paper, we develop a framework to capture user preference dynamics in a personalized manner based on the fact that changes in user preferences can vary individually. We also consider the plausible assumption that older user activities should have less influence on a user’s current preferences. We introduce an individual time decay factor for each user according to the rate of his preference dynamics to weigh the past user preferences and decrease their importance gradually. We exploit users’ demographics as well as the extracted similarities among users over time, aiming to enhance the prior knowledge about user preference dynamics, in addition to the past weighted user preferences in a developed coupled tensor factorization technique to provide top-K recommendations. The experimental results on the two real social media datasets—Last.fm and Movielens—indicate that our proposed model is better and more robust than other competitive methods in terms of recommendation accuracy and is more capable of coping with problems such as cold-start and data sparsity.
Highlights
Recommender systems have become an important tool for addressing the information overload problem of web users (Shi et al 2014) by providing personalized recommendations to a user that he might like based on past preferences, interest, or observed behavior about one or various items
First of all, based on the intuition that the time change pattern for each user may differ, how can the temporal dimension be incorporated to capture each individual user preference dynamics? how can different types of heterogeneous side information be blended to alleviate the cold-start user and sparsity issues? what is the efficient approach to model the dynamics of user preferences in order to generate more accurate recommendations? To this end, in this paper, we propose a social temporal recommendation model by extending the UPDCTF method
A temporal matrix factorization (MF) (TMF) approach has been proposed by Zhang et al (2014) that captures the temporal dynamics of user preferences by designing a transition matrix for each user latent feature vectors between two consecutive time periods
Summary
Recommender systems have become an important tool for addressing the information overload problem of web users (Shi et al 2014) by providing personalized recommendations to a user that he might like based on past preferences, interest, or observed behavior about one or various items. These methods suffer from some inherent limitations including cold-start (Barjasteh et al 2016) as well as data sparsity (Hafshejani et al 2018) problems and generally perform poorly for users who interacted with a few items To alleviate these problems in temporal recommendation systems, several methods (Rafailidis and Nanopoulos 2016; Liu et al 2013; Aravkin et al 2016; Bao et al 2013; Yin et al 2015) have been proposed which commonly exploit the side information such as user profile or trust relations among users, in addition to the interaction data that are usually available (Barjasteh et al 2016; Lee and Ma 2016). Based on the fact that user preferences may be influenced by friends’ opinions over time (Rafailidis et al 2017), we extract the similarity information among users as implicit social information from users’ interactions with items and exploit it to enhance the prior knowledge about user preference dynamics in each time period which can help alleviate the cold-start and data sparsity problems, in addition to using user demographics.
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