text: Product reviews contain a lot of useful information about product features and customer opinions. One important product feature is the complementary entity (products) that may potentially work together with the reviewed product. Knowing complementary entities of the reviewed product is very important because customers want to buy compatible products and avoid incompatible ones. In this paper, we address the problem of Complementary Entity Recognition (CER). Since no existing method can solve this problem, we first propose a novel unsupervised method to utilize syntactic dependency paths to recognize complementary entities. Then we expand category-level domain knowledge about complementary entities using only a few general seed verbs on a large amount of unlabeled reviews. The domain knowledge helps the unsupervised method to adapt to different products and greatly improves the precision of the CER task. The advantage of the proposed method is that it does not require any labeled data for training. We conducted experiments on 7 popular products with about 1200 reviews in total to demonstrate that the proposed approach is effective.
  @cite_18: In a modern recommender system, it is important to understand how products relate to each other. For example, while a user is looking for mobile phones, it might make sense to recommend other phones, but once they buy a phone, we might instead want to recommend batteries, cases, or chargers. These two types of recommendations are referred to as substitutes and complements: substitutes are products that can be purchased instead of each other, while complements are products that can be purchased in addition to each other. Here we develop a method to infer networks of substitutable and complementary products. We formulate this as a supervised link prediction task, where we learn the semantics of substitutes and complements from data associated with products. The primary source of data we use is the text of product reviews, though our method also makes use of features such as ratings, specifications, prices, and brands. Methodologically, we build topic models that are trained to automatically discover topics from text that are successful at predicting and explaining such relationships. Experimentally, we evaluate our system on the Amazon product catalog, a large dataset consisting of 9 million products, 237 million links, and 144 million reviews.
  @cite_11: In this paper, we introduce the method tagging substitute-complement attributes on miscellaneous recommending relations, and elaborate how this step contributes to electronic merchandising. There are already decades of works in building recommender systems. Steadily outperforming previous algorithms is difficult under the conventional framework. However, in real merchandising scenarios, we find describing the weight of recommendation simply as a scalar number is hardly expressive, which hinders the further progress of recommender systems. We study a large log of user browsing data, revealing the typical substitute complement relations among items that can further extend recommender systems in enriching the presentation and improving the practical quality. Finally, we provide an experimental analysis and sketch an online prototype to show that tagging attributes can grant more intelligence to recommender systems by differentiating recommended candidates to fit respective scenarios.
summary: The proposed problem is closely related to product recommender systems that are able to separate substitues and complements @cite_18 @cite_11 . @cite_11 first propose to incorporate the concepts of substitutes and complements into recommendation systems by analyzing navigation logs. More specifically, predicting complementary relations is pioneered by @cite_18 . They utilize topic models and customer purchase information (e.g., the products in the items also viewed'' section and the items also bought'' section of a product page) to predict category-level substitutes and complements. However, we observe that purchase information generated by the unknown algorithm from Amazon.com tends to be noisy and inaccurate for complementary entities since co-purchased products may not be complementary to each other. We demonstrate that their predictions are non-complementary entities for the products that we use for experiments in Section . Also, category-level predictions are not good enough for specific pairs of products (i.e., and are not complements). Furthermore, their predictions do not provide information about incompatible entities, which are valuable buying warnings for customers. Thus, fine-grained extraction of complementary entities from reviews that express firsthand user experience is important. To the best of our knowledge, the linguistic patterns of complementary relations are not studied in computer science.