Recommendation algorithms in distributed systems

OData support
Supervisor:
Dr. Wiener Gábor
Department of Computer Science and Information Theory

In this thesis, we give methods for time-aware recommendation, based on our paper [Pálovics, Benczúr, Kocsis, Kiss, Frigó, RecSys 2014] and additional experiments with more data sets and distributed implementations.

Our results have the potential of exploiting immediate temporal influences between users in a social media service.

We provide new models and evaluation

measures for real time recommendations with very strong temporal aspects by

introducing an online DCG evaluation metric.

Our experiments are carried over the two-year "scrobble" history of 70,000 Last.fm users and the public MovieLens data.

We consider the distributed implementation of our methods in large fully distributed P2P systems, in a

robust and scalable manner. We assume that the matrix to be

approximated is stored in a large network where each node

knows one row of the matrix (personal attributes, documents,

media ratings, etc.). In our P2P model, we do not allow this

personal information to leave the node, yet we want the nodes to

collaboratively train a recommender system. Methods applied in large scale

distributed systems such as synchronized parallel gradient search

or distributed iterative methods are not preferable in our system

model due to their requirements of synchronized rounds or their

inherent issues with load balancing. Our approach overcomes

these limitations with the help of a distributed stochastic gradient

search in which the personal part of the decomposition remains

local, and the global part (e.g., movie features) converges at all

nodes to the correct value.

Our main contribution is the online and distributed implementation of the factor models in the recommender frameworks. Using our implementation, we investigated the performance of online factorization algorithms and compared them with traditionally trained models.

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