Precision difference management using a common sub-vector to extended the extended VSM method Mars

3 Introduction . 4 Related work . 4 Implementation . 5 The Knowledge base . 5 Indexing . 6 Recommendation . 7 Comparison . 7 VSM . 7 Expanded vectors . 9 Relevance . 9 Conclusion and future work . 11 Acknowledgements . 12 References . 12 ICCS 2013 Werner Cruz 3 LE2I – UMR CNRS 6306 – Université de Bourgogne ABSTRACT Contractors, commercial and business decision-makers need economical information to drive their decisions. The production and distribution of a press review about French regional economic actors represents a prospecting tool on partners and competitors for the businessman. Our goal is to propose a customized review for each user, thus reducing the overload of useless information. Some systems for recommending news items already exist. The usefulness of external knowledge to improve the process has already been explained in information retrieval. The system’s knowledge base includes the domain knowledge used during the recommendation process. Our recommender system architecture is standard, but during the indexing task, the representations of content of each article and interests of users’ profiles created are based on this domain knowledge. Articles and Profiles are semantically defined in the Knowledge base via concepts, instances and relations. This paper deals with the relevance measure, a critical sub-task in recommendation systems and relationships between relevance and similarity concepts. The Vector Space Model is a wellknown model used for relevance ranking. The problematic exposed here is the utilization of the standard VSM method with our indexing method. ICCS 2013 Werner Cruz 4 LE2I – UMR CNRS 6306 – Université de Bourgogne INTRODUCTION The decision-making process in the economic field requires the centralization and intake of a large amount of information. The aim is to keep abreast with current market trends. Thus, contractors, business men and sales persons need to continuously be aware of the market conditions. This means to be up-to-date regarding ongoing information and projects undergoing development. With the help of economic monitoring, prospects can be easily identified, so as to establish new contracts. Our tool is specialized in the production and distribution of press reviews about French regional economic actors. The reviews sent are the same for each user, but personalized according to a geo-graphic area. All articles sent do not necessarily correspond to a person’s needs, and can be a waste of time. To reduce the overload of useless information, we are moving towards a customized review for each user. Therefore, an opinion survey on magazine readers that covers a broad array of subjects, including news services, was undertaken. Criteria for a relevant customization of the review were identified as a result of this survey as well as expert domain knowledge. These criteria are economic themes (i.e. main economic events), economic sectors, major transverse projects, temporal and localization data about each element underlined. Therefore, the complete production process of the review was redesigned to produce and to automatically distribute a customized review for each user. Another drawback in the existing process is the produced information storage. News articles are stored as PDF file reviews (i.e. the same format that is sent to customers, natural language), but this unstructured format is hard to handle and reuse. The aim of the architecture is to manage all news produced, and provide the most relevant article for each customer, using our domain knowledge. The overload of news information is a particular case of information overload, which is a well-known problem, studied by Information Retrieval and Recommender Systems research fields. News recommender systems already exist [14] [9] [13] [21] SCENE [12] NewsJunkie [8] Athena [10] GroupLens [18] News Dude [3] et YourNews [4]. Some of these systems use domain knowledge to improve the recommendation task [10] [14]. To achieve this goal, a content-based recommender system is being developed. A recommender system is necessary for the item ranking. And content-base is required to analyze the content of each article to structure and preserve information content. The results of the analysis enable to link the domain knowledge to the articles. Because, the domain knowledge can be reused to improve the recommendation task [10] [14]. This paper is organized as follows: Section 2 presents a brief review of related work. In section 3, we outline the proposed solution of a recommender system. Section 4 presents the similarity measure task, and our implementation of VMS. Section 5 is the conclusion and future work.