Cross-domain collaborative recommendation in a cold-start context: The impact of user profile size on the quality of recommendation

Most of the research studies on recommender systems are focused on single-domain recommendations. With the growth of multi-domain internet stores such as iTunes, Google Play, and Amazon.com, an opportunity to offer recommendations across different domains become more and more attractive. But there are few research studies on cross-domain recommender systems. In this paper, we study both the cold-start problem and the hypothesis that cross-domain recommendations provide more accuracy using a large volume of user data from a true multi-domain recommender service. Our results indicate that cross-domain collaborative filtering could significantly improve the quality of recommendation in cold start context and the auxiliary profile size plays an important role in it. © 2013 Springer-Verlag

A Cross-Domain Recommender System with Kernel-Induced Knowledge Transfer for Overlapping Entities

© 2012 IEEE. 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

Cross domain recommender systems using matrix and tensor factorization

Today, the amount and importance of available data on the internet are growing exponentially. These digital data has become a primary source of information and the people’s life bonded to them tightly. The data comes in diverse shapes and from various resources and users utilize them in almost all their personal or social activities. However, selecting a desirable option from the huge list of available options can be really frustrating and time-consuming. Recommender systems aim to ease this process by finding the proper items which are more likely to be interested by users. Undoubtedly, there is not even one social media or online service which can continue its’ work properly without using recommender systems. On the other hand, almost all available recommendation techniques suffer from some common issues: the data sparsity, the cold-start, and the new-user problems. This thesis tackles the mentioned problems using different methods. While, most of the recommender methods rely on using single domain information, in this thesis, the main focus is on using multi-domain information to create cross-domain recommender systems. A cross-domain recommender system is not only able to handle the cold-start and new-user situations much better, but it also helps to incorporate different features exposed in diverse domains together and capture a better understanding of the users’ preferences which means producing more accurate recommendations. In this thesis, a pre-clustering stage is proposed to reduce the data sparsity as well. Various cross-domain knowledge-based recommender systems are suggested to recommend items in two popular social media, the Twitter and LinkedIn, by using different information available in both domains. The state of art techniques in this field, namely matrix factorization and tensor decomposition, are implemented to develop cross-domain recommender systems. The presented recommender systems based on the coupled nonnegative matrix factorization and PARAFAC-style tensor decomposition are evaluated using real-world datasets and it is shown that they superior to the baseline matrix factorization collaborative filtering. In addition, network analysis is performed on the extracted data from Twitter and LinkedIn

Debiasing Graph Transfer Learning via Item Semantic Clustering for Cross-Domain Recommendations

Deep learning-based recommender systems may lead to over-fitting when lacking training interaction data. This over-fitting significantly degrades recommendation performances. To address this data sparsity problem, cross-domain recommender systems (CDRSs) exploit the data from an auxiliary source domain to facilitate the recommendation on the sparse target domain. Most existing CDRSs rely on overlapping users or items to connect domains and transfer knowledge. However, matching users is an arduous task and may involve privacy issues when data comes from different companies, resulting in a limited application for the above CDRSs. Some studies develop CDRSs that require no overlapping users and items by transferring learned user interaction patterns. However, they ignore the bias in user interaction patterns between domains and hence suffer from an inferior performance compared with single-domain recommender systems. In this paper, based on the above findings, we propose a novel CDRS, namely semantic clustering enhanced debiasing graph neural recommender system (SCDGN), that requires no overlapping users and items and can handle the domain bias. More precisely, SCDGN semantically clusters items from both domains and constructs a cross-domain bipartite graph generated from item clusters and users. Then, the knowledge is transferred via this cross-domain user-cluster graph from the source to the target. Furthermore, we design a debiasing graph convolutional layer for SCDGN to extract unbiased structural knowledge from the cross-domain user-cluster graph. Our Experimental results on three public datasets and a pair of proprietary datasets verify the effectiveness of SCDGN over state-of-the-art models in terms of cross-domain recommendations.Comment: 11 pages, 4 figure

Implementation and Usability Testing of a Cross-platform Mood-based Video Recommender System for Older Adults

Previous studies in this domain have discussed about building single-platform mood-based recommender systems for general users using computer technologies using typical algorithmic or emerging machine learning approach. However, they were no researches proposed to build a cross-platform mood-based video recommender system for older adults using target users’ input as domain knowledge. This study proposed the design and implementation of a cross-platform mood-based video recommender system for older adults. The domain knowledge of which video content type to recommend was collected via conducting a video content type preferences survey based on various mood states with both direct and indirect groups. To enable this video recommender system to run on both Android and iOS operating systems with a single code base, the React Native framework was adopted to implement this system. In addition, this study focused on implementing a mobile application with a good user experience targeting older adults. A usability testing experiment with a user group was conducted after the recommender system was developed. In the end this report presents analysis results based on the video content type preferences survey, usability testing, and user satisfaction about this mood-based video recommender system