A Multi-objective recommendation system

Özsoy, Makbule Gülçin
Recommendation systems suggest items to the user by estimating their preferences. Most of the recommendation systems are based on single criterion, such that they evaluate items based on overall rating. In order to give more accurate recommendations, a recommendation system can take advantage of considering multiple criteria. Beside combining multiple criteria from a single data source, multiple criteria from multiple data sources can be combined. Recommendation methods can also be used in various application domains involving big data such as marketing, biology, chemistry. In this thesis, four applications are studied: 1) use of multiple criteria from a single source to make recommendations, 2) use of multiple criteria from multiple sources to make recommendations, 3) use of recommendation methods to predict gene regularity networks and 4) use of recommendation methods to identify new indicators for known drugs. Firstly, we propose a new multi-objective optimization based recommendation method that combines multiple criteria, namely past preferences of users, hometown of users, friendship relation among users, check-in time information. We expanded this method by inferring home/center location of users in terms of longitude-latitude pairs, by making dynamic recommendations based on temporal preferences of users and by clustering users by their hometown and friendship relations. These methods are evaluated on a Foursquare check-in dataset. Secondly, we combine information collected from multiple different social networks to create integrated models of individuals and to make recommendations to them. To our knowledge, this is the first work aiming to use information from multiple social networks in recommendation process by modeling the users. For this purpose, we collect and anonymize two data-sets that contain information from BlogCatalog, Twitter, Flickr, Facebook, YouTube and LastFm web-sites. We implement several different types of recommendation methodologies to observe their performance while using single or multiple features from a single source or multiple sources. Thirdly, observing the common features of recommendation systems and gene regularity networks (GRNs), we use the proposed multi-objective optimization based recommendation method to predict the gene relationships; such that which genes regulates the others. Lastly, we adapt the proposed recommendation method to identify new indications for known drugs, i.e. drug repositioning.


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...
Probabilistic matrix factorization based collaborative filtering with implicit trust derived from review ratings information
Ercan, Eda; Taşkaya Temizel, Tuğba; Department of Information Systems (2010)
Recommender systems aim to suggest relevant items that are likely to be of interest to the users using a variety of information resources such as user profiles, trust information and users past predictions. However, typical recommender systems suffer from poor scalability, generating incomprehensible and not useful recommendations and data sparsity problem. In this work, we have proposed a probabilistic matrix factorization based local trust boosted recommendation system which handles data sparsity, scalabil...
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...
Extending singular value decomposition based recommendation systems with tags and ontology
Turgut, Yakup; Toroslu, İsmail Hakkı; Department of Computer Engineering (2014)
Due to increase of the volume of data related to user ratings on items, in recent years, recommendation systems became very popular, which uses this data in order to rec- ommend items to users in many different domains. Singular Value Decomposition is one of the most widely studied collaborative filtering recommendation techniques. In some applications users are also allowed to enter (sometimes free) tags in addition to their ratings on items. Adding tags in addition to regular users’ ratings on items have a...
A Content boosted hybrid recommendatıon system
Çapraz, Seval; Temizer, Selim; Department of Computer Engineering (2016)
Nowadays, most of e-commerce and social media sites use recommendation systems to help users find more relevant products easily. The key feature of recommendation is personalization which means different products are being offered for different users according to each user s interests. In literature, there are a lot of algorithms and tools which implement recommendation systems. The most common techniques for recommendation systems include Collaborative Filtering (CF) and Content-Based Filtering (CBF). To incre...
Citation Formats
M. G. Özsoy, “A Multi-objective recommendation system,” Ph.D. - Doctoral Program, Middle East Technical University, 2016.