Text

theses

Advances in mobile technologies have allowed us to collect and process massive amounts of mobile data across many different mobile applications. If properly analyzed, this data can be a source of rich intelligence for providing real-time decision making in various mobile applications and for the provision of mobile recommendations. Indeed, mobile recommendations constitute an especially important class of recommendations because mobile users often find themselves in unfamiliar environments and are often overwhelmed with the "new terrain" abundance of unfamiliar information and uncertain choices. Therefore, it is especially useful to equip them with the tools and methods that will guide them through all these uncertainties by providing useful recommendations while they are "on the move." In this dissertation, we aim to address the unique challenges of recommendations in mobile and pervasive business environments from both theoretical and practical perspectives. Specifically, we first develop an energy-efficient mobile recommender system which is to recommend a sequence of potential pick-up points for taxi drivers by handling the complex data characteristics of real-world location traces. The developed mobile recommender system can provide effective mobile sequential recommendation and the knowledge extracted from location traces can be used for coaching drivers and lead to the efficient use of energy. The experimentations on real-world spatio-temporal data demonstrate the efficiency and effectiveness of our methods. Moreover, we introduce a focused study of cost-aware collaborative filtering that is able to address the cost constraint for travel tour recommendation. Specifically, we present two ways to represent user's latent cost preference and different cost-aware collaborative filtering models for travel tour recommendations. We demonstrate that the cost-aware recommendation models can consistently and significantly outperform several existing latent factor models. In addition, we introduce a Tourist-Area-Season Topic (TAST) model. This TAST model can represent travel packages and tourists by different topic distributions, where the topic extraction is conditioned on both the tourists and the intrinsic features (i.e. locations, travel seasons) of the landscapes. Then, based on this topic model representation, we present a cocktail approach to generate the lists for personalized travel package recommendation. When applied to real-world travel tour data, the TAST model can lead to better performances of recommendation. Finally, we introduce the collective training to boost collaborative filtering models. The basic idea is that we compliment the training data for a particular collaborative filtering model with the predictions of other models. And we develop an iterative process to mutually boost each collaborative filtering model iteratively.

ETD_4724

Ph.D.

Includes bibliographical references

Includes vita

by Yong Ge

http://hdl.rutgers.edu/1782.1/rucore10002600001.ETD.000068669

doi:10.7282/T3JM287G

ETD doctoral

The author owns the copyright to this work.