Collaborative and content based filtering personalized recommender system for book / Hossein Arabi
Personalized recommendation systems provide end users with suggestions about information items, social elements, products or services that are likely to be of their interest based on users' details such as demographics, location, time, and emotion. Incorporating contextual information in rec...
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| Main Author: | |
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| Format: | Thesis |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://studentsrepo.um.edu.my/8978/1/Hossein.pdf http://studentsrepo.um.edu.my/8978/6/hossein.pdf http://studentsrepo.um.edu.my/8978/ |
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| Summary: | Personalized recommendation systems provide end users with suggestions about
information items, social elements, products or services that are likely to be of their
interest based on users' details such as demographics, location, time, and emotion.
Incorporating contextual information in recommendation system is an effective
approach to create more accurate and personalized recommendations. Therefore, in this
study, a Personalized Hybrid Book Recommender is proposed, which integrates several
users’ characteristics, namely their personality traits, demographic details and current
location, together with review sentiments and purchase reason, to improve their book
recommendations. The system is able to determine user’s personality traits by utilizing
the Ten Item Personality Inventory. The proposed recommender system would be
evaluated using two metrics, that are, Standardized Root Mean Square Residual and
Root Mean Square Error of Approximation. The proposed technique was evaluated by
comparing it against baseline models and existing personalized recommendation
systems. This study is able to show effectiveness of integrating user’s contextual data
(personality trait, demographic data and location) with product’s features (review and
purchase reason). |
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