In this research, we combine relational learning with multi-domain to develop a formal framework for a recommendation system. The design of our framework aims at: (i) constructing general rules for recommendations, (ii) providing suggested items with clear and understandable explanations, (iii) delivering a broad range of recommendations including novel and unexpected items. We use relational learning to find all possible relations, including novel relations, and to form the general rules for recommendations. Each rule is represented in relational logic, a formal language, associating with probability. The rules are used to suggest the items, in any domain, to the user whose preferences or other properties satisfy the conditions of the rule. The information described by the rule serves as an explanation for the suggested item. It states clearly why the items are chosen for the users. The explanation is in if-then logical format which is unambiguous, less redundant and more concise compared to a natural language used in other explanation recommendation systems. The explanation itself can help persuade the user to try out the suggested items, and the associated probability can drive the user to make a decision easier and faster with more confidence. Incorporating information or knowledge from multiple domains allows us to broaden our search space and provides us with more opportunities to discover items which are previously unseen or surprised to a user resulting in a wide range of recommendations. The experiment results show that our proposed algorithm is very promising. Although the quality of recommendations provided by our framework is moderate, our framework does produce interesting recommendations not found in the primitive single-domain based system and with simple and understandable explanations.

中文翻译：

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使用多个领域的关系学习的可解释和不可预见的建议

在这项研究中，我们将关系学习与多领域相结合，以开发推荐系统的正式框架。我们框架的设计旨在：（i）构建建议的一般规则，（ii）为建议的项目提供清晰且易于理解的解释，（iii）提出广泛的建议，包括新颖和意外的项目。我们使用关系学习来找到所有可能的关系，包括新颖的关系，并形成建议的一般规则。每个规则都以关系逻辑（一种与概率相关的形式语言）表示。规则用于在任何域中向偏好或其他属性满足规则条件的用户建议项目。规则描述的信息用作建议项目的解释。它清楚地说明了为什么要为用户选择商品。解释采用if-then逻辑格式，与其他解释推荐系统中使用的自然语言相比，该逻辑格式明确，冗余少且更简洁。解释本身可以帮助说服用户尝试建议的项目，并且相关的概率可以促使用户更自信，更轻松地做出决策。整合来自多个领域的信息或知识可以使我们拓宽搜索空间，并为我们提供更多的机会来发现用户以前看不见或感到惊讶的商品，从而获得广泛的推荐。实验结果表明，该算法具有很好的应用前景。尽管我们框架提供的建议质量中等，