Raimo Launonen

A new similarity measure using Bhattacharyya coefficient for collaborative filtering in sparse data

Collaborative filtering (CF) is the most successful approach for personalized product or service recommendations. Neighborhood based collaborative filtering is an important class of CF, which is simple, intuitive and efficient product recommender system widely used in commercial domain. Typically, neighborhood-based CF uses a similarity measure for finding similar users to an active user or similar products on which she rated. Traditional similarity measures utilize ratings of only co-rated items while computing similarity between a pair of users. Therefore, these measures are not suitable in a sparse data. In this paper, we propose a similarity measure for neighborhood based CF, which uses all ratings made by a pair of users. Proposed measure finds importance of each pair of rated items by exploiting Bhattacharyya similarity. To show effectiveness of the measure, we compared performances of neighborhood based CFs using state-of-the-art similarity measures with the proposed measured based CF. Recommendation results on a set of real data show that proposed measure based CF outperforms existing measures based CFs in various evaluation metrics.

Multimodal astronaut virtual training prototype

A few dedicated training simulator applications exist that mix realistic interaction devices-like real cockpits in flight simulators-with virtual environment (VE) components. Dedicated virtual reality (VR) systems have been utilized also in astronaut training. However there are no detailed descriptions of projection wall VR systems and related interaction techniques for astronaut assembly training in zero gravity conditions. Back projection technology tends to have certain advantages over head mounted displays including less simulation sickness and less restricted user movement. A prototype was built to evaluate the usefulness of projection technology VEs and interaction techniques for astronaut training. This was achieved by first constructing a PC cluster-based general purpose VE software and hardware platform. This platform was used to implement a testing prototype for astronaut assembly sequence training. An interaction tool battery was designed for the purposes of viewpoint control and object handling. A selected training task was implemented as a case study for further analysis based on laptop usage in the Fluid Science Laboratory (FSL) inside the Columbus module in the International Space Station (ISS). User tests were conducted on the usability of the prototype for the intended training purpose. The results seem to indicate that projection technology-based VE systems and suitably selected interaction techniques can be successfully utilized in zero gravity training operations.

Exploiting Bhattacharyya Similarity Measure to Diminish User Cold-Start Problem in Sparse Data

Collaborative Filtering (CF) is one of the most successful approaches for personalized product recommendations. Neighborhood based collaborative filtering is an important class of CF, which is simple and efficient product recommender system widely used in commercial domain. However, neighborhood based CF suffers from user cold-start problem. This problem becomes severe when neighborhood based CF is used in sparse rating data. In this paper, we propose an effective approach for similarity measure to address user cold-start problem in sparse rating dataset. Our proposed approach can find neighbors in the absence of co-rated items unlike existing measures. To show the effectiveness of this measure under cold-start scenario, we experimented with real rating datasets. Experimental results show that our approach based CF outperforms state-of-the art measures based CFs for cold-start problem.

Distance based Incremental Clustering for Mining Clusters of Arbitrary Shapes

Clustering has been recognized as one of the important tasks in data mining. One important class of clustering is distance based method. To reduce the computational and storage burden of the classical clustering methods, many distance based hybrid clustering methods have been proposed. However, these methods are not suitable for cluster analysis in dynamic environment where underlying data distribution and subsequently clustering structures change over time. In this paper, we propose a distance based incremental clustering method, which can find arbitrary shaped clusters in fast changing dynamic scenarios. Our proposed method is based on recently proposed al-SL method, which can successfully be applied to large static datasets. In the incremental version of the al-SL (termed as IncrementalSL), we exploit important characteristics of al-SL method to handle frequent updates of patterns to the given dataset. The IncrementalSL method can produce exactly same clustering results as produced by the al-SL method. To show the effectiveness of the IncrementalSL in dynamically changing database, we experimented with one synthetic and one real world datasets.