Content-based recommender system for an academic social network / Vala Ali Rohani
The rapid growth of Web 2.0 applications, such as blogs and social networks creates rich online information and provides various new sources of knowledge. The situation, however, leads to a great challenge in terms of information overload among social network users. Recommender systems (RSs) alle...
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| Format: | Thesis |
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2014
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| Online Access: | http://studentsrepo.um.edu.my/4705/ http://studentsrepo.um.edu.my/4705/1/Vala_PhDThesisFinalSubmission_1April.pdf |
| Summary: | The rapid growth of Web 2.0 applications, such as blogs and social networks creates
rich online information and provides various new sources of knowledge. The situation,
however, leads to a great challenge in terms of information overload among social
network users. Recommender systems (RSs) alleviate this problem with a technique that
suggests relevant information from the abundance of Web data by considering the user’s
previous preference. Collaborative and content-based are the recommendation
techniques typically used in existing RSs. The content-based method is employed more
widely though. Similar to the collaborative, the content-based technique suffers from
the cold-start dilemma that is caused by the incapability of RSs to make reliable
recommendations in situations when new items or new users are involved. Such issues
have an impact on prediction accuracy in existing algorithms, and hence, a better
approach is required. In this study, a new algorithm is proposed to represent an
enhanced version of content-based recommender systems by utilizing social networking
features. In its formulation, the algorithm considers the interests and preferences of
users’ friends and faculty mates in addition to users’ own preferences. The algorithm
exploits all interests and preferences in a hierarchy tree structure. Since no offline data
on Academic Social Networks (ASNs) exists and concerning the advantages of online
study benefits, a real runtime environment of ASN called MyExpert was built in order
to conduct an online study to assess the four recommender algorithms. Each
recommender system algorithm, including the enhanced version of the content-based
recommender systems using social networking (ECSN), is later incorporated into
MyExpert to propose to members of this online society the most relevant academic
items including jobs, news, scholarships and conferences. By using MyExpert, the
online study was carried out to collect real feedback from live interactions between
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users and the system. The assessment ran for 14 consecutive weeks from 7th September
to 26th December, 2012. MyExpert had 920 members from 10 universities in Malaysia
at the time of evaluation. Four metrics, namely precision, recall, fallout, and F1 were
employed to measure the prediction accuracy of each algorithm. Although the
experiment conducted presented some threats, the results indicated that the ECSN
algorithm not only improves the prediction accuracy of recommendations but also
resolves the cold start problem in the existing recommender systems algorithms. |
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