SEO Case Study: How I Increased SEO Traffic & Ranking by 50-60% in 6 Months
Context-aware Mobile Recommendation Services for Conference Participants
1. www.b-it-center.de
Dejan Kovachev, Manh Cuong Pham, Yiwei Cao — Information Systems & Databases, RWTH Aachen University, Aachen, Germany — {kovachev|pham|cao }@dbis.rwth-aachen.de
Context-aware Mobile Recommendation
Services for Conference Participants
Academic events: which talk to attend, who is my potential collaborator?
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Auditorium: Keynote Room 342: Workshop
Room 204: Paper session Hall: Poster session Room 048: Round table
Our approach: collaborative recommendation based on your research community and current location
AERCS SNA services(http://bosch.informatik.rwth- Location Algorithm: collaborative filtering with contextual
aachen.de:5080/AERCS/) information information
1. Identify user‘s community: similarity measure
sim(u , v) =α ∗ scoreauthor (u, v) + (1 − α ) ∗ scorecitation (u, v)
using jaccard measure on coauthorship and citation nets.
2. Recommend top k similar researchers who are in
Bibliographical SNA on co- the same room.
information (DBLP authorship and Smart phone 3. Talk recommendation:
and CiteSeerX) citation networks location sensing 3.1 Rank sections
rank (e, u , t ) =β t ∗ ∑ sim(u , v) R(v, e)
v∈K
where 1, t < eend
βt =
Collaborative filtering 0, otherwise
R(v,e): attendance of v at section e, K: top similar users,
Web services eend : ending time of section e
3.2 Recommend top n rank section
Download and install the application at:
http://dbis.rwth-aachen.de/~kovachev/camrs/
http://goo.gl/XQWLL
Android app
CAMRS Mobile