Bettencourt, N.; Silva, N.; Barroso, J.: Semantically enhancing recommender systems (2016)
0.00
0.001757696 = product of:
0.003515392 = sum of:
0.003515392 = product of:
0.007030784 = sum of:
0.007030784 = weight(_text_:a in 3374) [ClassicSimilarity], result of:
0.007030784 = score(doc=3374,freq=6.0), product of:
0.053105544 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046056706 = queryNorm
0.13239266 = fieldWeight in 3374, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046875 = fieldNorm(doc=3374)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- As the amount of content and the number of users in social relationships is continually growing in the Internet, resource sharing and access policy management is difficult, time-consuming and error-prone. Cross-domain recommendation of private or protected resources managed and secured by each domain's specific access rules is impracticable due to private security policies and poor sharing mechanisms. This work focus on exploiting resource's content, user's preferences, users' social networks and semantic information to cross-relate different resources through their meta information using recommendation techniques that combine collaborative-filtering techniques with semantics annotations, by generating associations between resources. The semantic similarities established between resources are used on a hybrid recommendation engine that interprets user and resources' semantic information. The recommendation engine allows the promotion and discovery of unknown-unknown resources to users that could not even know about the existence of those resources thus providing means to solve the cross-domain recommendation of private or protected resources.
- Source
- Knowledge discovery, knowledge engineering and knowledge management: 7th International Joint Conference, IC3K 2015, Lisbon, Portugal, November 12-14, 2015, Revised Selected Papers. Eds.: A. Fred et al
- Type
- a
