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首页> 外文会议>13th IEEE International Conference on Commerce and Enterprise Computing >Will I Like It? Providing Product Overviews Based on Opinion Excerpts
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Will I Like It? Providing Product Overviews Based on Opinion Excerpts

机译：我会喜欢吗？根据意见摘录提供产品概述

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摘要

With the growing popularity and acceptance of e-commerce platforms, users face an ever increasing burden in actually choosing the right product from the plethora of online offers. Thus, techniques for personalization and shopping assistance are in high demand by users, as well as by shopping platforms themselves. For a pleasant and successful shopping experience, users should be empowered to easily decide on which products to buy with high confidence. However, especially for entertainment goods like e.g. movies, books, or music, this task is very challenging. Unfortunately, to days approaches for dealing with this challenge (like e.g. recommender systems) suffer severe drawbacks: recommender systems are completely opaque, i.e. the recommendation is hard to justify semantically. User reviews could help users to form an opinion of recom-mended items, but with several thousand reviews available for e.g. a given popular movie, it is very challenging for users to find representative reviews. In this paper, we propose a novel technique for automatically analyzing user reviews using advanced opinion mining techniques. The results of this analysis are then used to group reviews by their semantics, i.e. by their contained opinions and point-of-views. Furthermore, the relevant paragraphs with respect to each opinion is extracted and presented to the user. These extracts can easily be digested by users to allow them a quick and diverse forming of opinion, and thus increasing their confidence in their decision, and their overall customer satisfaction.
机译：随着电子商务平台的日益普及和接受，用户在从大量在线商品中选择合适的产品时面临越来越大的负担。因此，用户以及购物平台本身对个性化和购物辅助的技术都有很高的要求。为了获得愉快和成功的购物体验，应该授权用户轻松自信地决定要购买的产品。但是，特别是对于娱乐商品，例如电影，书籍或音乐，这项任务非常艰巨。不幸的是，直到今天，处理这一挑战的方法（例如推荐系统）都存在严重的缺点：推荐系统是完全不透明的，即推荐很难从语义上证明其合理性。用户评论可以帮助用户形成对推荐商品的意见，但是有数千条评论可用于例如对于给定的流行电影，用户要找到具有代表性的评论非常具有挑战性。在本文中，我们提出了一种使用高级意见挖掘技术自动分析用户评论的新颖技术。然后将这种分析的结果用于按语义将评论进行分组，即按其包含的观点和观点进行分组。此外，针对每个意见的相关段落被提取并呈现给用户。用户可以轻松地消化这些摘录，以使他们能够迅速而多样化地形成意见，从而增强了他们对决策的信心以及他们的整体客户满意度。

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