Document Type



Doctor of Philosophy (PhD)


Computer Science

First Advisor's Name

Sundaraja Sitharama Iyengar

First Advisor's Committee Title

Committee chair

Second Advisor's Name

Shu-Ching Chen

Second Advisor's Committee Title

Committee member

Third Advisor's Name

Ning Xie

Third Advisor's Committee Title

Committee member

Fourth Advisor's Name

Leonardo Bobadilla

Fourth Advisor's Committee Title

Committee member

Fifth Advisor's Name

Debra VanderMeer

Fifth Advisor's Committee Title

Committee member


Social Network Analysis, Information Filtering, Recommendation System, Point-of-Interest Recommendation

Date of Defense



The increasing volume of information has created overwhelming challenges to extract the relevant items manually. Fortunately, the online systems, such as e-commerce (e.g., Amazon), location-based social networks (LBSNs) (e.g., Facebook) among many others have the ability to track end users' browsing and consumption experiences. Such explicit experiences (e.g., ratings) and many implicit contexts (e.g., social, spatial, temporal, and categorical) are useful in preference elicitation and recommendation. As an emerging branch of information filtering, the recommendation systems are already popular in many domains, such as movies (e.g., YouTube), music (e.g., Pandora), and Point-of-Interest (POI) (e.g., Yelp).

The POI domain has many contextual challenges (e.g., spatial (preferences to a near place), social (e.g., friend's influence), temporal (e.g., popularity at certain time), categorical (similar preferences to places with same category), locality of POI, etc.) that can be crucial for an efficient recommendation. The user reviews shared across different social networks provide granularity in users' consumption experience. From the data mining and machine learning perspective, following three research directions are identified and considered relevant to an efficient context-aware POI recommendation, (1) incorporation of major contexts into a single model and a detailed analysis of the impact of those contexts, (2) exploitation of user activity and location influence to model hierarchical preferences, and (3) exploitation of user reviews to formulate the aspect opinion relation and to generate explanation for recommendation.

This dissertation presents different machine learning and data mining-based solutions to address the above-mentioned research problems, including, (1) recommendation models inspired from contextualized ranking and matrix factorization that incorporate the major contexts and help in analysis of their importance, (2) hierarchical and matrix-factorization models that formulate users' activity and POI influences on different localities that model hierarchical preferences and generate individual and sequence recommendations, and (3) graphical models inspired from natural language processing and neural networks to generate recommendations augmented with aspect-based explanations.






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