Factorization-based primary dimension modelling for multidimensional data in recommender systems

In recent years, multidimensional data is becoming increasingly popular in recommender systems. For example, social tagging systems encourage users to employ user-defined keywords to help manage content in a personalized way. Recommender systems built upon social tagging systems utilize social taggi...

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Bibliographic Details
Published in:International journal of machine learning and cybernetics Vol. 10; no. 8; pp. 2209 - 2228
Main Authors: Tang, Xiaoyu, Xu, Yue, Geva, Shlomo
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2019
Springer Nature B.V
Subjects:
Accuracy
Algorithms
Artificial Intelligence
Collaboration
Complex Systems
Computational Intelligence
Context
Control
Customization
Data analysis
Eigenvectors
Engineering
Factorization
Filtration
Mechatronics
Methods
Multidimensional data
Multidimensional methods
Neighborhoods
Original Article
Pattern Recognition
Recommender systems
Robotics
Systems Biology
Tagging
Tags
Tensors
User behavior
User profiles
Multidimensional data
Social tagging data
Collaborative filtering
Tensor factorization
Recommender systems
User profiling
ISSN:1868-8071, 1868-808X
Online Access:Get full text
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Summary:In recent years, multidimensional data is becoming increasingly popular in recommender systems. For example, social tagging systems encourage users to employ user-defined keywords to help manage content in a personalized way. Recommender systems built upon social tagging systems utilize social tagging data to improve recommendation accuracy. Context-aware recommender systems incorporate context information (e.g. time, location, weather, etc.) into recommendation models to make recommendations more personalized. User profiles play a crucial role in recommender systems because user profiles provide the information about users’ information needs based on which personalized recommendations can be generated. Existing user profiling techniques for collaborative filtering (CF) recommender systems based on multidimensional data mostly analyze data through splitting the multidimensional relations into lower-dimensional relations. However, this leads to the loss of multidimensionality in user-item interactions. A different type of methods for addressing multidimensional data is factorization techniques such as tensor factorization (TF), which discover holistic latent relations between all dimensions in multidimensional data. However, TF models are not constructed to analyze any particular dimension of multidimensional data. But for many application domains, certain dimensions are of particular interest such as the user dimension in recommender systems. However, user profiles are not explicitly reflected in TF models. In this paper, we propose a unified approach to profile any particular dimension in multidimensional data based on partially-decomposed Tucker models (TM). We prove that the proposed profiling approach is a compatible higher-order extension of the matrix factorization based approaches. Moreover, we prove that the proposed TM-based profiles can be updated incrementally with new data without reconstructing the entire profiles. We integrate the proposed TF-based user/item profiling approach into neighborhood-based collaborative filtering recommenders for making top-N item recommendations. Extensive experiments have been conducted using real-world social tagging datasets to demonstrate that the CF recommenders integrated with the proposed user/item profiles outperform state-of-the-art CF recommendation approaches in terms of recommendation accuracy.
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ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-018-0816-7