Item Infomation
Title: | Generic knowledge-based analysis of social media for recommendations |
Authors: | Graaff, Victor de |
Issue Date: | 2015 |
Citation: | CBRecSys 2015 : New trends on content-based recommender systems : proceedings of the 2nd workshop on new trends on content-based recommender systems co-located with 9th ACM conference on recommender systems (RecSys 2015)Volume 1448, 8 p.1613-0073 |
Abstract: | Recommender systems have been around for decades to help people find the best matching item in a pre-defined item set. Knowledge-based recommender systems are used to match users based on information that links the two, but they often focus on a single, specific application, such as movies to watch or music to listen to. In this paper, we present our Interest-Based Recommender System (IBRS). This knowledge-based recommender system provides recommendations that are generic in three dimensions: IBRS is (1) domain-independent, (2) language-independent, and (3) independent of the used social medium. To match user interests with items, the first are derived from the user’s social media profile, enriched with a deeper semantic embedding obtained from the generic knowledge base DBpedia. These interests are used to extract personalized recommendations from a tagged item set from any domain, in any language. We also present the results of a validation of IBRS by a test user group of 44 people using two item sets from separate domains: greeting cards and holiday homes. |
URI: | http://tailieuso.tlu.edu.vn/handle/DHTL/4680 |
Source: | http://ceur-ws.org/Vol-1448/paper5.pdf |
Appears in Collections: | Tài liệu mở |
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