AAAI Publications, Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence

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Leveraging Multiple Networks for Author Personalization
Rohit Parimi, Doina Caragea

Last modified: 2015-04-01

Abstract


Recommender systems provide personalized item suggestions by identifying patterns in past user-item preferences. Most existing approaches for recommender systems work on a single domain, i.e., use user preferences from one domain and recommend items from the same domain. Recently, some recommendation models have been proposed to use user preferences from multiple related item source domains to improve recommendation accuracy for a target item domain, an area of research known as cross-domain recommender systems. One typical assumption in these systems is that users, items, and user preferences for items are similar across domains. In this paper, we introduce a new cross-domain recommendation problem which does not meet this typical assumption. For example, for some scientometric datasets, when the objective is to recommend co-authors, conferences, and references, respectively, to authors, although the users are similar across domains, the items and user-item preferences are different. To address this problem, we propose two approaches to aggregate knowledge from multiple domains. Our approaches allow us to control the knowledge transferred between domains. Experimental results on a DBLP subset show that the proposed cross-domain approaches are helpful in improving recommendation accuracy as compared to single domain approaches.

Keywords


recommender systems; cross-domain recommender sytems; neighborhood-based approaches

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