Aviram Dayan

Introducing Preferences over NFPs into Service Selection in SOA

When implementing a business or software activity in SOA, a match is sought between the required functionality and that provided by a web service. In selecting services to perform a certain business functionality, often only hard constraints are considered. However, client requirements over QoS or other NFP types are often soft and allow tradeoffs. We use a graphical language for specifying hard constraints, preferences and tradeoffs over NFPs as well as service level objectives (SLO). In particular, we use the TCP and UCP network formalisms to allow for a simple yet very flexible specification of hard constraints, preferences, and tradeoffs over these properties. Algorithms for selecting web services according to the hard constraints, as well as for optimizing the selected web service configuration, according to the specification, were developed.

Utilizing transfer learning for in-domain collaborative filtering

In recent years, transfer learning has been used successfully to improve the predictive performance of collaborative filtering (CF) for sparse data by transferring patterns across domains. In this work, we advance transfer learning (TL) in recommendation systems (RSs), facilitating improvement within a domain rather than across domains. Specifically, we utilize TL for in-domain usage. This reduces the need to obtain information from additional domains, while achieving stronger single domain results than other state-of-the-art CF methods. We present two new algorithms; the first utilizes different event data within the same domain and boosts recommendations of the target event (e.g., the buy event), and the second algorithm transfers patterns from dense subspaces of the dataset to sparse subspaces. Experiments on real-life and publically available datasets reveal that the proposed methods outperform existing state-of-the-art CF methods.

Recommenders benchmark framework

In this demo we present a recommender benchmark framework that serves as an infrastructure for comparing and examining the performance and feasibility of different recommender algorithms on various datasets with a variety of measures. The extendable infrastructure aims to provide easy plugging of novel recommendation-algorithms, datasets and compare their performance using visual tools and metrics with other algorithms in the benchmark. It also aims at generating a WEKA-type workbench [1] for the recommender systems field to enable usage and application of common recommender systems (RS) algorithms for research and practice. The demo movie is available at: http://www.youtube.com/watch?v=fsDITf6s0WY