11 research outputs found
Transfer Learning for Content-Based Recommender Systems using Tree Matching
In this paper we present a new approach to content-based transfer learning
for solving the data sparsity problem in cases when the users' preferences in
the target domain are either scarce or unavailable, but the necessary
information on the preferences exists in another domain. We show that training
a system to use such information across domains can produce better performance.
Specifically, we represent users' behavior patterns based on topological graph
structures. Each behavior pattern represents the behavior of a set of users,
when the users' behavior is defined as the items they rated and the items'
rating values. In the next step we find a correlation between behavior patterns
in the source domain and behavior patterns in the target domain. This mapping
is considered a bridge between the two domains. Based on the correlation and
content-attributes of the items, we train a machine learning model to predict
users' ratings in the target domain. When we compare our approach to the
popularity approach and KNN-cross-domain on a real world dataset, the results
show that on an average of 83 of the cases our approach outperforms both
methods
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
Accuracy in Rating and Recommending Item Features
This paper discusses accuracy in processing ratings of and
recommendations for item features. Such processing facilitates featurebased user navigation in recommender system interfaces. Item features, often in the form of tags, categories or meta-data, are becoming important hypertext components of recommender interfaces. Recommending features would help unfamiliar users navigate in such environments. This work explores techniques for improving feature recommendation accuracy. Conversely, it also examines possibilities for processing user ratings of features to improve recommendation of both features and items. This work’s illustrative implementation is a web portal for a museum collection that lets users browse, rate and receive recommendations for both artworks and interrelated topics about them. Accuracy measurements compare proposed techniques for processing feature ratings and recommending features. Resulting techniques recommend features with relative accuracy. Analysis indicates that processing ratings of either features or items does not improve accuracy of recommending the other
A Cross-Domain Collaborative Filtering Algorithm Based on Feature Construction and Locally Weighted Linear Regression
Cross-domain collaborative filtering (CDCF) solves the sparsity problem by transferring rating knowledge from auxiliary domains. Obviously, different auxiliary domains have different importance to the target domain. However, previous works cannot evaluate effectively the significance of different auxiliary domains. To overcome this drawback, we propose a cross-domain collaborative filtering algorithm based on Feature Construction and Locally Weighted Linear Regression (FCLWLR). We first construct features in different domains and use these features to represent different auxiliary domains. Thus the weight computation across different domains can be converted as the weight computation across different features. Then we combine the features in the target domain and in the auxiliary domains together and convert the cross-domain recommendation problem into a regression problem. Finally, we employ a Locally Weighted Linear Regression (LWLR) model to solve the regression problem. As LWLR is a nonparametric regression method, it can effectively avoid underfitting or overfitting problem occurring in parametric regression methods. We conduct extensive experiments to show that the proposed FCLWLR algorithm is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary domains, as compared to many state-of-the-art single-domain or cross-domain CF methods
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