Yonghua Yang

Xusheng Luo, Luxin Liu, Yonghua Yang et al.

One of the ultimate goals of e-commerce platforms is to satisfy various shopping needs for their customers. Much efforts are devoted to creating taxonomies or ontologies in e-commerce towards this goal. However, user needs in e-commerce are still not well defined, and none of the existing ontologies has the enough depth and breadth for universal user needs understanding. The semantic gap in-between prevents shopping experience from being more intelligent. In this paper, we propose to construct a large-scale e-commerce cognitive concept net named "AliCoCo", which is practiced in Alibaba, the largest Chinese e-commerce platform in the world. We formally define user needs in e-commerce, then conceptualize them as nodes in the net. We present details on how AliCoCo is constructed semi-automatically and its successful, ongoing and potential applications in e-commerce.

Xusheng Luo, Yonghua Yang, Kenny Q. Zhu et al.

Understanding latent user needs beneath shopping behaviors is critical to e-commercial applications. Without a proper definition of user needs in e-commerce, most industry solutions are not driven directly by user needs at current stage, which prevents them from further improving user satisfaction. Representing implicit user needs explicitly as nodes like "outdoor barbecue" or "keep warm for kids" in a knowledge graph, provides new imagination for various e- commerce applications. Backed by such an e-commerce knowledge graph, we propose a supervised learning algorithm to conceptualize user needs from their transaction history as "concept" nodes in the graph and infer those concepts for each user through a deep attentive model. Offline experiments demonstrate the effectiveness and stability of our model, and online industry strength tests show substantial advantages of such user needs understanding.

Li Chen, Ningxia Wang, Yonghua Yang et al.

Though it has been recognized that recommending serendipitous (i.e., surprising and relevant) items can be helpful for increasing users' satisfaction and behavioral intention, how to measure serendipity in the offline environment is still an open issue. In recent years, a number of metrics have been proposed, but most of them were based on researchers' assumptions due to the serendipity's subjective nature. In order to validate these metrics' actual performance, we collected over 10,000 users' real feedback data and compared with the metrics' results. It turns out the user profile based metrics, especially content-based ones, perform better than those based on item popularity, in terms of estimating the unexpectedness facet of recommendations. Moreover, the full metrics, which involve the unexpectedness component, relevance, timeliness, and user curiosity, can more accurately indicate the recommendation's serendipity degree, relative to those that just involve some of them. The application of these metrics to several recommender algorithms further consolidates their practical usage, because the comparison results are consistent with those from user evaluation. Thus, this work is constructive for filling the gap between offline measurement and user study on recommendation serendipity.