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  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?




                                                                   ?                 ?
                                                                       ?     ?      ?

            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

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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? ? ? ? ? ? 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