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首页> 外文期刊>Journal of systems and software >Exploiting Synergies Between Semantic Reasoning And Personalization Strategies In Intelligent Recommender Systems: A Case Study
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Exploiting Synergies Between Semantic Reasoning And Personalization Strategies In Intelligent Recommender Systems: A Case Study

机译：利用智能推荐系统中语义推理与个性化策略之间的协同作用

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Current recommender systems attempt to identify appealing items for a user by applying syntactic matching techniques, which suffer from significant limitations that reduce the quality of the offered suggestions. To overcome this drawback, we have developed a domain-independent personalization strategy that borrows reasoning techniques from the Semantic Web, elaborating recommendations based on the semantic relationships inferred between the user's preferences and the available items. Our reasoning-based approach improves the quality of the suggestions offered by the current personalization approaches, and greatly reduces their most severe limitations. To validate these claims, we have carried out a case study in the Digital TV field, in which our strategy selects TV programs interesting for the viewers from among the myriad of contents available in the digital streams. Our experimental evaluation compares the traditional approaches with our proposal in terms of both the number of TV programs suggested, and the users' perception of the recommendations. Finally, we discuss concerns related to computational feasibility and scalability of our approach.
机译：当前的推荐器系统试图通过应用语法匹配技术来为用户识别吸引人的项目，该语法匹配技术遭受显着的局限，降低了所提供建议的质量。为克服此缺点，我们开发了一种独立于域的个性化策略，该策略借鉴了语义Web的推理技术，并根据在用户的偏好和可用项目之间推断出的语义关系来详细说明建议。我们基于推理的方法提高了当前个性化方法所提供建议的质量，并大大减少了它们的最严格限制。为了验证这些说法，我们在数字电视领域进行了一项案例研究，其中，我们的策略是从数字流中可用的众多内容中选择对观众来说有趣的电视节目。我们的实验评估在建议的电视节目数量和用户对建议的理解方面都将传统方法与我们的建议进行了比较。最后，我们讨论与我们方法的计算可行性和可扩展性有关的问题。

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