1,929 research outputs found
Towards a Collaborative Filtering Framework for Recommendation in Museums: From Preference Elicitation to Group's Visits
AbstractRecommendation systems based on collaborative filtering methods can be exploited in the context of providing personalized artworks tours within a museum. However, in order to be effectively used, several problems have to be addressed: user preferences are not expressed as rating, items to be suggested are located in a physical space, and users may be in a group. In this work, we present a general framework that, by using the Matrix Factorization (MF) approach and a graph representation of a museum, addresses the problem of generating and then recommending an artworks sequence for a group of visitors within a museum. To reach a high-quality initial personalization, the recommendation system uses a simple, but efficient, elicitation method that is inspired by the MF approach. Moreover, the proposed approach considers the individual or the aggregated artworks' ratings to build up a solution that takes into account the physical location of the artworks
Time-aware topic recommendation based on micro-blogs
Topic recommendation can help users deal with the information overload issue in micro-blogging communities. This paper proposes to use the implicit information network formed by the multiple relationships among users, topics and micro-blogs, and the temporal information of micro-blogs to find semantically and temporally relevant topics of each topic, and to profile users' time-drifting topic interests. The Content based, Nearest Neighborhood based and Matrix Factorization models are used to make personalized recommendations. The effectiveness of the proposed approaches is demonstrated in the experiments conducted on a real world dataset that collected from Twitter.com
Exploring Deep Space: Learning Personalized Ranking in a Semantic Space
Recommender systems leverage both content and user interactions to generate
recommendations that fit users' preferences. The recent surge of interest in
deep learning presents new opportunities for exploiting these two sources of
information. To recommend items we propose to first learn a user-independent
high-dimensional semantic space in which items are positioned according to
their substitutability, and then learn a user-specific transformation function
to transform this space into a ranking according to the user's past
preferences. An advantage of the proposed architecture is that it can be used
to effectively recommend items using either content that describes the items or
user-item ratings. We show that this approach significantly outperforms
state-of-the-art recommender systems on the MovieLens 1M dataset.Comment: 6 pages, RecSys 2016 RSDL worksho
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