Recommender systems have to serve in online environments which can be highly non-stationary.1. Traditional recommender algorithmsmay periodically rebuild their models, but they cannot adjust to quick changes in trends caused by timely information. In our experiments, we observe that even a simple, but online trained recommender model can perform significantly better than its batch version. We i...

Recommender systems are the systems that try to make recommendations to each user based on performance, personal tastes, user behaviors, and the context that match their personal preferences and help them in the decision-making process. One of the most important subjects regarding these systems is to increase the system accuracy which means how much the recommendations are close to the user int...

Traditionally, recommender systems have been “hand-built”, implemented as custom applications hard-wired to a particular recommendation task. Recently, the database community has begun exploring alternative DBMS-based recommender system architectures, whereby a database both stores the recommender system data (e.g., ratings data and the derived recommender models) and generates recommendations ...

If we are keen to boost the process of language learning, we need to study every aspect and component of our course. To this end, we carry out an investigation in which every detail of the course is put under microscope. Assessment of a course is an attempt in which different type of information is gathered systematically in order to study the working of a language instruction program. Certainl...

In this thesis we describe an approach to the recommender system problem based on the Probabilistic Relational Model framework. Traditionally, recommender systems have fallen into two broad categories: content-based and collaborative-filteringbased recommender systems, each of which has a distinct set of strengths and weaknesses. We present a sound statistical framework for integrating both of ...

Most existing reciprocal recommender systems use either profile similarity or interaction similarity to recommend new matches, assuming that user preferences are static and ignoring temporal aspects of user behaviour. This paper takes a different approach, and addresses the issue of representing user preferences as dynamic. We introduce a new representation for changes in user preferences and u...

In this work, we will provide a brief review of different recommender systems’ algorithms, which have been proposed in the recent literature. First, we will present the basic recommender systems’ challenges and problems. Then, we will give an overview of association rules, memorybased, model-based and hybrid recommendation algorithms. Finally, evaluation metrics to measure the performance of th...