161 research outputs found
Learning informative priors from heterogeneous domains to improve recommendation in cold-start user domains
© 2016 ACM. In the real-world environment, users have sufficient experience in their focused domains but lack experience in other domains. Recommender systems are very helpful for recommending potentially desirable items to users in unfamiliar domains, and cross-domain collaborative filtering is therefore an important emerging research topic. However, it is inevitable that the cold-start issue will be encountered in unfamiliar domains due to the lack of feedback data. The Bayesian approach shows that priors play an important role when there are insufficient data, which implies that recommendation performance can be significantly improved in cold-start domains if informative priors can be provided. Based on this idea, we propose a Weighted Irregular Tensor Factorization (WITF) model to leverage multi-domain feedback data across all users to learn the cross-domain priors w.r.t. both users and items. The features learned from WITF serve as the informative priors on the latent factors of users and items in terms of weighted matrix factorization models. Moreover, WITF is a unified framework for dealing with both explicit feedback and implicit feedback. To prove the effectiveness of our approach, we studied three typical real-world cases in which a collection of empirical evaluations were conducted on real-world datasets to compare the performance of our model and other state-of-the-art approaches. The results show the superiority of our model over comparison models
CoNet: Collaborative Cross Networks for Cross-Domain Recommendation
The cross-domain recommendation technique is an effective way of alleviating
the data sparse issue in recommender systems by leveraging the knowledge from
relevant domains. Transfer learning is a class of algorithms underlying these
techniques. In this paper, we propose a novel transfer learning approach for
cross-domain recommendation by using neural networks as the base model. In
contrast to the matrix factorization based cross-domain techniques, our method
is deep transfer learning, which can learn complex user-item interaction
relationships. We assume that hidden layers in two base networks are connected
by cross mappings, leading to the collaborative cross networks (CoNet). CoNet
enables dual knowledge transfer across domains by introducing cross connections
from one base network to another and vice versa. CoNet is achieved in
multi-layer feedforward networks by adding dual connections and joint loss
functions, which can be trained efficiently by back-propagation. The proposed
model is thoroughly evaluated on two large real-world datasets. It outperforms
baselines by relative improvements of 7.84\% in NDCG. We demonstrate the
necessity of adaptively selecting representations to transfer. Our model can
reduce tens of thousands training examples comparing with non-transfer methods
and still has the competitive performance with them.Comment: Deep transfer learning for recommender system
Potential Passenger Flow Prediction: A Novel Study for Urban Transportation Development
Recently, practical applications for passenger flow prediction have brought
many benefits to urban transportation development. With the development of
urbanization, a real-world demand from transportation managers is to construct
a new metro station in one city area that never planned before. Authorities are
interested in the picture of the future volume of commuters before constructing
a new station, and estimate how would it affect other areas. In this paper,
this specific problem is termed as potential passenger flow (PPF) prediction,
which is a novel and important study connected with urban computing and
intelligent transportation systems. For example, an accurate PPF predictor can
provide invaluable knowledge to designers, such as the advice of station scales
and influences on other areas, etc. To address this problem, we propose a
multi-view localized correlation learning method. The core idea of our strategy
is to learn the passenger flow correlations between the target areas and their
localized areas with adaptive-weight. To improve the prediction accuracy, other
domain knowledge is involved via a multi-view learning process. We conduct
intensive experiments to evaluate the effectiveness of our method with
real-world official transportation datasets. The results demonstrate that our
method can achieve excellent performance compared with other available
baselines. Besides, our method can provide an effective solution to the
cold-start problem in the recommender system as well, which proved by its
outperformed experimental results
A Survey on Cross-domain Recommendation: Taxonomies, Methods, and Future Directions
Traditional recommendation systems are faced with two long-standing
obstacles, namely, data sparsity and cold-start problems, which promote the
emergence and development of Cross-Domain Recommendation (CDR). The core idea
of CDR is to leverage information collected from other domains to alleviate the
two problems in one domain. Over the last decade, many efforts have been
engaged for cross-domain recommendation. Recently, with the development of deep
learning and neural networks, a large number of methods have emerged. However,
there is a limited number of systematic surveys on CDR, especially regarding
the latest proposed methods as well as the recommendation scenarios and
recommendation tasks they address. In this survey paper, we first proposed a
two-level taxonomy of cross-domain recommendation which classifies different
recommendation scenarios and recommendation tasks. We then introduce and
summarize existing cross-domain recommendation approaches under different
recommendation scenarios in a structured manner. We also organize datasets
commonly used. We conclude this survey by providing several potential research
directions about this field
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