AbstractContext in the form of meta-data has been accredited
as an important component in cross-domain collaborative
filtering (CDCF). In this research paper CDCF
concept is used to exploit event information (context)
from two UI matrices to allow the recommendation performance
of one domain (Facebook- User-Event Matrix)
to benefit from the information from another domain
(Bookmyshow- Event-Tag Matrix). The model based collaborative
filtering approach Tensor Factorization(TF) has
been used to integrate Facebook provided User-Event context
information with Bookmyshow Event-Tag context information
to recommend events. In contrast to the standard
collaborative tag recommendation, our CDCF approach
uses one User-Event matrix of Facebook that takes
another Bookmyshow Event-Tag matrix as additional informant.
The proposed cross-domain based Event Recommendation
approach is divided into three modules- i) data
collection which extracts the unstructured dataset from
the two domains Bookmyshow and social networking site
Facebook using API’s; ii) data mapping module which is
basically used to integrate the common knowledge/ data
that can be shared between considered different domains
(Facebook & Bookmyshow). This module integrates and
reduces the data into structured events’ instances. As the
dataset was collected from two different sites, an intersection
of both was taken out. Therefore this module is carefully
designed according to reliability of information that
is common between two domains; iii) 3 order tensor factorization
and Latent Dirichlet Allocation (LDA) used for
most preferable recommendation by less pertinent result reduction. The proposed 3 order tensor factorization is designed
for maximizing the mutual benefit from both the
considered domains (organizer and user). Therefore providing
three recommendations: For organizers: 1) system
recommends places to conduct specific event according to
maximum of attendees of a particular type of event at a
specific location; 2) recommending target audience to organizer:
those who are interested to attend event on the
basis of past data for promotion purposes. For users: 3) recommending
events to users of their interest on the basis of
past record. Our result shows significant improvement in
reduction of less relevant data and result effectiveness is
measured through recall and precision. Reduction of less
relevant recommendation is 64%, 72% and 63% for place
recommendation to organizer, target audience recommendation
to organizer and event recommendation to user
respectively. The proposed tensor factorization approach
achieved 68% precision, 15.5% recall in recommending attendees
to organizer and 62% precision, 13.4% recall for
event recommendation to user.
Temporal Collaborative Filtering with Bayesian Probabilistic Tensor Factorization
