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With the expansion of private and non-private information shops and the emergence of off-the-shelf data-mining know-how, suggestion platforms have emerged that in particular handle the original demanding situations of navigating and analyzing software program engineering data.

This e-book collects, buildings and formalizes wisdom on suggestion platforms in software program engineering. It adopts a realistic strategy with an particular specialize in method layout, implementation, and evaluate. The booklet is split into 3 components: “Part I – concepts” introduces fundamentals for construction recommenders in software program engineering, together with strategies for accumulating and processing software program engineering information, but in addition for offering techniques to clients as a part of their workflow. “Part II – overview” summarizes tools and experimental designs for comparing options in software program engineering. “Part III – functions” describes wishes, concerns and answer strategies concerned with complete suggestion platforms for particular software program engineering projects, targeting the engineering insights required to make powerful concepts. The e-book is complemented via the web site rsse.org/book, including loose supplemental fabrics for readers of this publication and a person drawn to advice platforms in software program engineering, together with lecture slides, info units, resource code, and an summary of individuals, teams, papers and instruments in regards to suggestion structures in software program engineering.

The publication is especially well-suited for graduate scholars and researchers development new suggestion platforms for software program engineering functions or in different high-tech fields. it will possibly additionally function the root for graduate classes on advice platforms, utilized info mining or software program engineering. software program engineering practitioners constructing suggestion platforms or related functions with predictive performance also will enjoy the huge spectrum of subject matters covered.

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The motivation for hybrid recommendations is the opportunity to achieve a better accuracy [6]. There are different approaches to evaluate the accuracy of recommendation algorithms. These approaches (see also Avazpour et al. [2] and Tosun Mısırlı et al. [58] in Chaps. 10 and 13, respectively) can be categorized into predictive accuracy metrics such as the mean absolute error (MAE), classification accuracy metrics such as precision and recall, and rank accuracy metrics such as Kendall’s Tau. For a discussion of accuracy metrics we refer the reader also to Gunawardana and Shani [28] and Jannach et al.

ACM 35(12), 61–70 (1992). 138867 27. : ReComment: Towards critiquing-based recommendation with speech interaction. In: Proceedings of the ACM Conference on Recommender Systems pp. 157–164 (2013) 28. : A survey of accuracy evaluation metrics of recommendation tasks. J. Mach. Learn. Res. 10, 2935–2962 (2009) 29. : Can automated group recommender systems help consumers make better choices? J. Market. 76(5), 89–109 (2012) 30. : An algorithmic framework for performing collaborative filtering. In: Proceedings of the ACM SIGIR International Conference on Research and Development in Information Retrieval, pp.

Felfernig et al. Fig. 5 Example of a critiquing scenario. The entry item l7 is shown to the user. 1 Critiquing-Based Recommendation There are two basic approaches to support item identification in the context of knowledge-based recommendation. First, search-based approaches require the explicit specification of search criteria and the recommendation algorithm is in charge of identifying a set of corresponding recommendations [16,57] (see also Sect. 4). If no solution can be found for a given set of requirements, the recommendation engine determines diagnoses that indicate potential changes such that a solution (recommendation) can be identified.

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