A Context-aware Recommender System for Web Service Composition

[[abstract]]This study explored the use of context-aware recommender system to facilitate web service composition. The needs for composition of existing web services to generate functionality for users are increasing. And an intelligent framework is needed to alleviate users' burden to discover, select, invoke and combine web services. In this study, we focus on using context-aware recommender system to provide users with the most appropriate web services composition. The concept of context-aware collaborative filtering is used here to learn and predict user preferences, and based on this information, to compose necessary web services to achieve user request. We provide a restaurant recommender system prototype for the restaurant search scenario to demonstrate how proposed architecture works.[[conferencetype]]國際[[conferencedate]]20120718~20120720[[booktype]]紙本[[iscallforpapers]]Y[[conferencelocation]]Piraeus-Athens, Greec

Latent Semantic Indexing (LSI) Based Distributed System and Search On Encrypted Data

Latent semantic indexing (LSI) was initially introduced to overcome the issues of synonymy and polysemy of the traditional vector space model (VSM). LSI, however, has challenges of its own, mainly scalability. Despite being introduced in 1990, there are few attempts that provide an efficient solution for LSI, most of the literature is focuses on LSI’s applications rather than improving the original algorithm. In this work we analyze the first framework to provide scalable implementation of LSI and report its performance on the distributed environment of RAAD. The possibility of adopting LSI in the field of searching over encrypted data is also investigated. The importance of that field is stemmed from the need for cloud computing as an effective computing paradigm that provides an affordable access to high computational power. Encryption is usually applied to prevent unauthorized access to the data (the host is assumed to be curious), however this limits accessibility to the data given that search over encryption is yet to catch with the latest techniques adopted by the Information Retrieval (IR) community. In this work we propose a system that uses LSI for indexing and free-query text for retrieving. The results show that the available LSI framework does scale on large datasets, however it had some limitations with respect to factors like dictionary size and memory limit. When replicating the exact settings of the baseline on RAAD, it performed relatively slower. This could be resulted by the fact that RAAD uses a distributed file system or because of network latency. The results also show that the proposed system for applying LSI on encrypted data retrieved documents in the same order as the baseline (unencrypted data)

A web service recommender system using vector space model and latent semantic indexing

International audienceThe tremendous growth in the amount of available web services (WS) impulses many researchers on proposing recommender systems to help users discover services. Most of the proposed solutions analyzed query strings and web service descriptions to generate recommendations. However, text based recommendations approaches depend mainly on user's perspective, languages and notations which easily decrease recommendation's efficiency. In this paper, we propose to take into account user's behaviors instead of text based analysis. We apply collaborative filtering technique on user's interactions. We propose and implement two algorithms based on Vector Space Model and Latent Semantic Indexing to validate our approach. We also provide evaluation methods with different datasets in order to compare and assert the efficiency of our two algorithm