A recommendation framework using ontological user

Download
2011
Yaman, Çağla
In this thesis, a content recommendation system has been developed. The system makes recommendations based on the preferences of the users on some aspects of the content and also preferences of similar users. The preferences of a user are extracted from the choices of that user made in the past. Similarities between users are defined by the similarities of their preferences. Such a system requires both qualified content and user information. The proposed system uses semantic user and content profiles to more effectively define the relationships between the two and make better inferences. An ontology is defined using the existing domain ontologies and the semi-structured data on the web. The system is implemented mainly for the movie domain in which well-defined ontologies and user information are easier to access.

Suggestions

Using learning to rank for a top-n recommendation system in TV domain
Acar, Bedia; Çiçekli, Fehime Nihan; Department of Computer Engineering (2016)
In this thesis, a top-N recommendation system in TV domain is proposed using learning to rank. The design, development and evaluation of the proposed recommender system are described in detail. Instead of calculating rating score of items like in conventional recommender systems, the ranked recommendation item list is presented to TV users. Moreover, path-based features which are used to build ranking model is explained in detail. These features provide collaborative filtering, content-based filtering and c...
An ontology-based hybrid recommendation system using semantic similarity measure and feature weighting
Ceylan, Uğur; Birtürk, Ayşe Nur; Department of Computer Engineering (2011)
The task of the recommendation systems is to recommend items that are relevant to the preferences of users. Two main approaches in recommendation systems are collaborative filtering and content-based filtering. Collaborative filtering systems have some major problems such as sparsity, scalability, new item and new user problems. In this thesis, a hybrid recommendation system that is based on content-boosted collaborative filtering approach is proposed in order to overcome sparsity and new item problems of c...
A Hypergraph based framework for representing aggregated user profiles, employing it for a recommender system and personalized search through a hypernetwork method
Tarakçı, Hilal; Manguoğlu, Murat; Çiçekli, Fehime Nihan; Department of Computer Engineering (2017)
In this thesis, we present a hypergraph based user modeling framework to aggregate partial profiles of the individual and obtain a complete, semantically enriched, multi-domain user model. We also show that the constructed user model can be used to support different personalization services including recommendation. We evaluated the user model against datasets consisting of user's social accounts including Facebook, Twitter, LinkedIn and Stack Overflow. The evaluation results confirmed that the proposed use...
A Content-boosted matrix factorization technique via user-item subgroups
Aslan Oğuz, Evin; Çiçekli, Fehime Nihan; Department of Computer Engineering (2014)
This thesis mainly focuses on improving the recommendation accuracy of collaborative filtering (CF) algorithm via merging two successful approaches. Since CF algorithmsgrouplike-mindedusers,atechniquecalledMulticlassCo-Clustering(MCoC) is used in order to group like-minded users more effectively. Since, CF approaches lack incorporating content information, a content-boosted CF approach that embeds content information into recommendation process is used. In the MCoC, a user or an item can belong to zero, one ...
A Graph-based core model and a hybrid recommender system for TV users
Taşcı, Arda; Çiçekli, Fehime Nihan; Department of Computer Engineering (2015)
This thesis proposes a core model to represent user profiles in a graph-based environment which can be the base of different recommender system approaches as well as other cutting edge applications for TV domain. The proposed graph-based core model is explained in detail with node types, properties and edge weight metrics. The capabilities of this core model are described in detail. Moreover, in this thesis, a hybrid recommender system based on this core model is presented with its design, development and e...
Citation Formats
Ç. Yaman, “A recommendation framework using ontological user,” M.S. - Master of Science, Middle East Technical University, 2011.