Abstract
The aim of recommender systems is to automatically identify user preferences within collected data, then use those preferences to make recommendations that help with decisions. However, recommender systems suffer from data sparsity problem, which is particularly prevalent in newly launched systems that have not yet had enough time to amass sufficient data. As a solution, cross-domain recommender systems transfer knowledge from a source domain with relatively rich data to assist recommendations in the target domain. These systems usually assume that the entities either fully overlap or do not overlap at all. In practice, it is more common for the entities in the two domains to partially overlap. Moreover, overlapping entities may have different expressions in each domain. Neglecting these two issues reduces prediction accuracy of cross-domain recommender systems in the target domain. To fully exploit partially overlapping entities and improve the accuracy of predictions, this paper presents a cross-domain recommender system based on kernel-induced knowledge transfer, called KerKT. Domain adaptation is used to adjust the feature spaces of overlapping entities, while diffusion kernel completion is used to correlate the non-overlapping entities between the two domains. With this approach, knowledge is effectively transferred through the overlapping entities, thus alleviating data sparsity issues. Experiments conducted on four data sets, each with three sparsity ratios, show that KerKT has 1.13%-20% better prediction accuracy compared with six benchmarks. In addition, the results indicate that transferring knowledge from the source domain to the target domain is both possible and beneficial with even small overlaps.
Highlights
R ECOMMENDER systems have developed quickly with the explosion of Web 2.0 technologies [1], and are in wide use
Designed as basic memory-based methods, collaborative filtering has evolved into model-based methods that commonly involve machine learning techniques, such as matrix factorization [6], probabilistic models [7] and deep neural networks [8], [9], [10]
We propose a cross-domain recommender system with kernel-induced knowledge transfer (KerKT) as a knowledge transfer method to improve recommendation performance with partially overlapping entities
Summary
R ECOMMENDER systems have developed quickly with the explosion of Web 2.0 technologies [1], and are in wide use. The methods for knowledge extraction and transfer are different depending on whether and how the entities in each domain overlap. In fully-overlapping methods, the original source and target rating matrixes are collectively factorized, the entities’ features are extracted. Cross-domain recommendation systems present the following challenges: 1) Feature inconsistency caused by data sparsity. To avoid divergence in the feature space caused by data sparsity, we propose a domain adaptation method to adjust the feature spaces through the overlapping entities. Feature divergence caused by domain heterogeneity is eliminated and domain connection is reinforced This means the similarities between entities can be determined through a modest amount of overlapping entity data. The results show that KerKT alleviates the impact on recommendation caused by data sparsity and transfers knowledge even when there are only a few overlapping entities.
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