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Monday, 4 June 2018

Mining Competitors from Large Unstructured Datasets

In any competitive business, success is based on the ability to make an item more appealing to customers than the competition. A number of questions arise in the context of this task: how do we formalize and quantify the competitiveness between two items? Who are the main competitors of a given item? What are the features of an item that most affect its competitiveness? Despite the impact and relevance of this problem to many domains, only a limited amount of work has been devoted toward an effective solution. In this paper, we present a formal definition of the competitiveness between two items, based on the market segments that they can both cover. Our evaluation of competitiveness utilizes customer reviews, an abundant source of information that is available in a wide range of domains. We present efficient methods for evaluating competitiveness in large review datasets and address the natural problem of finding the top-k competitors of a given item. Finally, we evaluate the quality of our results and the scalability of our approach using multiple datasets from different domains.
  • The management literature is rich with works that focus on how managers can manually identify competitors. Some of these works model competitor identification as a mental categorization process in which managers develop mental representations of competitors and use them to classify candidate firms. Other manual categorization methods are based on market- and resource-based similarities between a firm and candidate competitors.
  • Zheng et al. identify key competitive measures (e.g. market share, share of wallet) and showed how a firm can infer the values of these measures for its competitors by mining (i) its own detailed customer transaction data and (ii) aggregate data for each competitor.
  • The frequency of textual comparative evidence can vary greatly across domains. For example, when comparing brand names at the firm level (e.g. “Google vs Yahoo” or “Sony vs Panasonic”), it is indeed likely that comparative patterns can be found by simply querying the web. However, it is easy to identify mainstream domains where such evidence is extremely scarce, such as shoes, jewelery, hotels, restaurants, and furniture.
  • Existing approach is not appropriate for evaluating the competitiveness between any two items or firms in a given market. Instead, the authors assume that the set of competitors is given and, thus, their goal is to compute the value of the chosen measures for each competitor. In addition, the dependency on transactional data is a limitation we do not have.
  • The applicability of such approaches is greatly limited
  • We propose a new formalization of the competitiveness between two items, based on the market segments that they can both cover.
  • We describe a method for computing all the segments in a given market based on mining large review datasets. This method allows us to operationalize our definition of competitiveness and address the problem of finding the top-k competitors of an item in any given market. As we show in our work, this problem presents significant computational challenges, especially in the presence of large datasets with hundreds or thousands of items, such as those that are often found in mainstream domains. We address these challenges via a highly scalable framework for top-k computation, including an efficient evaluation algorithm and an appropriate index.
  • To the best of our knowledge, our work is the first to address the evaluation of competitiveness via the analysis of large unstructured datasets, without the need for direct comparative evidence.
  • A formal definition of the competitiveness between two items, based on their appeal to the various customer segments in their market. Our approach overcomes the reliance of previous work on scarce comparative evidence mined from text.
  • A formal methodology for the identification of the different types of customers in a given market, as well as for the estimation of the percentage of customers that belong to each type.
  • A highly scalable framework for finding the top-k competitors of a given item in very large datasets.
  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB
  • Operating system : Windows 7.
  • Coding Language : JAVA/J2EE
  • Tool : Eclipse Luna
  • Database : MYSQL
George Valkanas, Theodoros Lappas, and Dimitrios Gunopulos, “Mining Competitors from Large Unstructured Datasets”, IEEE Transactions on Knowledge and Data Engineering, 2017.

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