BibTeX
@MISC{Shabib15ii,
author = {Nafiseh Shabib},
title = {ii},
year = {2015}
}
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Abstract
iii To my family iv Recommendation systems are extensively used to provide a constantly increasing variety of services. Alongside single-user recommendation systems, group recommendation systems have emerged as a method of identifying the items that a set of users will most appreciate collectively. In this thesis, we describe developments in the area of group recommendation techniques and how such techniques can be applied to address current challenges in the field of group recom-mendations. First, we propose a conceptual data model to support group recommendation that can be deployed in addition to the suggested approaches. Second, we propose a contextual group recommendation model that addresses the problem of contextual recommendation for groups and exploits a hierarchical context model to extend a typical recommendation model to a gen-eral context-aware model that addresses the information needs of a group. We also develop a context-aware recommendation system for concerts as a prototype for an exploratory analysis of the suggested model. Third, we propose a new dimension in the computation of group recom-mendations, namely, the exploitation of social ties (affinities) between group members, and its evolution over time; moreover, we present an efficient algorithm that produces temporal-affinity-aware recommendations for ad hoc groups. Finally, we propose an approach that addresses the sparsity problem in group recommendation, and we present our method, which employs a memory-based technique to resolve the data sparsity problem in the group recommendation set-ting. All proposed methods have been evaluated through extensive experiments on public datasets or on real users who have participated in our experiments. Where possible, comparisons with related techniques have been performed to reinforce the validity of the presented approaches. Based on state-of-the-art metrics, the proposed methods have produced promising results for use in the field of group recommendation systems.
Keyphrases
group recommendation system group recommendation group recom-mendations sparsity problem conceptual data model exploratory analysis family iv recommendation system related technique ad hoc group contextual recommendation typical recommendation model efficient algorithm real user public datasets group recommendation set-ting memory-based technique contextual group recommendation model extensive experiment suggested approach group recommendation technique presented approach hierarchical context model suggested model group member new dimension data sparsity problem information need temporal-affinity-aware recommendation social tie current challenge state-of-the-art metric context-aware recommendation system single-user recommendation system gen-eral context-aware model promising result
