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In this paper we focus on personalized recommendation algorithm for coupon deals, which are very different from deals of other retailers. We first analyzed some sample deals from Groupon and found that deals under category dining, Wellness and activities have a high probability of having the same keywords in the deal
Keywords of academic papers are jargons shared within a research domain as well as a summary of the contents. However, the increasing popularity of the interdisciplinary research in academia in recent years opened a possibility that the choice of keywords would be no longer confined by the traditional domain
mismatch problem and match irrelevance problem and fail to generate highly related results. To overcome these problems, we propose a novel approach to recommend articles to the researchers. In our approach we integrate three types of similarity measures: keyword similarity, journal similarity, and author similarity to measure
keyword. This graph is built using a carefully selected one-parameter set of keywords. By varying this parameter - the level of meaningfulness - we transition the document-representing graph from a trivial path graph into a large random graph. During such a conversion, as the parameter is varied over its range, the graph
In traditional collaborative filtering recommendation, the matrix sparsity and cold start restricted the accuracy of system. In this paper, we develop a way to enhance the recommendation effectiveness by merging neighborhood relationship and users keyword of social network information into collaborative filtering. We
Social recommender systems leverage collaborative filtering (CF) to serve users with content that is of potential interesting to active users. A wide spectrum of CF schemes has been proposed. However, most of them cannot deal with the cold-start problem that denotes a situation that social media sites fail to draw recommendation for new items, users or both. In addition, they regard that all ratings...
The exploitation of social networks and collaborative systems is a phenomenon that is gradually integrated with the practice of information retrieval on the Internet. These systems of Web 2.0, allowing users to collaborate via the free content indexing using keywords or tags; creating structures represented as
feedback and system log, then set up the social networks. According to the input keywords and types of recommender, more recommendation information can be generated. This model has been implemented as a recommendation module in an academic search system Gloss, deployed at the WSI Laboratory of Graduate University of Chinese
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