Neural Session Recommendation

In this thesis we aim to research and design different neural models for session recommendation. We investigate the fundamental neural models for session recommendation, namely BERT4Rec, SASRec and GRU4Rec and subsequently use our findings to design a simpler but performant neural model. <br/><br/>Firstly, we address methodological errors made...

Minimizing the Long-tail Problem in Collaborative Filtering Based Recommender Systems Using Clustering

Recommender systems are an essential part of online businesses in today's day and age. They provide users with meaningful recommendations for items and products. A frequently occurring problem in recommender systems is known as the long-tail problem. It refers to a situation in which a majority of the items in the data set have limited ratings...

Recommender Systems with Evolutionary Algorithms: Many­-Objective Optimization for Large­-Scale Music Recommendation: A demonstration showing the reliability of serving users recommendations with trade-­off for large music collections, by leveraging diverse Recommender Systems and Evolutionary Algorithms

Using Recommender Systems with Evolutionary Algorithms is an extremely niche domain. It holds the key to enabling new user interaction designs, where users can effectively configure their experience with a Recommender System. This thesis answers important questions about the scientific aspects of its application to large-scale data through a...

Comparative Analysis of Techniques for Data Minimization for Recommender System algorithms

Recommender systems (RS) often use a large amount of data for a marginal gain in performance. This thesis investigates the data minimization in Recommender Systems, which is not well studied in the literature. This thesis extends the data minimization principles advocated in GDPR and studies its effects on recommender systems. Minimizing data...

Evaluating Recommendation Algorithms Based on U-I Matrix Property Analysis

Recent years, recommender systems are more and more important for solving information overload problem. They sort through massive data to provide users with personalized content and services. Most researchers focus on designing new algorithms to increase the performance of recommender systems. However, some open challenges stand: Why the...

Expertise Identification in Enterprise Social Media

The increasing adoption of Enterprise Social Media (ESM) systems within enterprises is driven by the need for the explicit facilitation of sharing expertise. Expertise Identification (EI) functionality can satisfy this need. The social-media-like content and Collaborative Filtering (CF) annotation data available in ESM, however, pose unique...