Personalized diversification of complementary recommendations with user preference in online grocery.

Complementary recommendations play an important role in surfacing the relevant items to the customers. In the cross-selling scenario, some customers might present more exploratory shopping behaviors and prefer more diverse complements, while other customers show less exploratory (or more conventional) shopping behaviors and want to have a deep dive of less diverse types of complements. The existence of two distinct shopping behaviors reflects users' different shopping intents and requires complementary recommendations to be adaptable based on the user's shopping intent. Although many studies focus on improving the recommendations through post-processing techniques, such as user-item-level personalized ranking and diversification of recommendations, they fail to address such a requirement. First, many user-item-level personalization methods cannot explicitly model the preference of users in two types of shopping behaviors and their intent on the corresponding complementary recommendations. Second, most of the diversification methods increase the heterogeneity of the recommendations. However, users' intent on conventional complementary shopping requires more homogeneity of the recommendations, which is not explicitly modeled. The present study tries attempts to solve these problems by the personalized diversification strategies for complementary recommendations. To address the requirement of modeling heterogenized and homogenized complementary recommendations, we propose two diversification strategies, heterogenization and homogenization, to re-rank complementary recommendations based on the determinantal point process (DPP). We use transaction history to estimate users' intent on more exploratory or more conventional complementary shopping. With the estimated user intent scores and two diversification strategies, we propose an algorithm to personalize the diversification strategies dynamically. We demonstrate the effectiveness of our re-ranking algorithm on the publicly available Instacart dataset.

Understanding user intents in online health forums.

Online health forums provide a convenient way for patients to obtain medical information and connect with physicians and peers outside of clinical settings. However, large quantities of unstructured and diversified content generated on these forums make it difficult for users to digest and extract useful information. Understanding user intents would enable forums to find and recommend relevant information to users by filtering out threads that do not match particular intents. In this paper, we derive a taxonomy of intents to capture user information needs in online health forums and propose novel pattern-based features for use with a multiclass support vector machine (SVM) classifier to classify original thread posts according to their underlying intents. Since no dataset existed for this task, we employ three annotators to manually label a dataset of 1192 HealthBoards posts spanning four forum topics. Experimental results show that a SVM using pattern-based features is highly capable of identifying user intents in forum posts, reaching a maximum precision of 75%, and that a SVM-based hierarchical classifier using both pattern and word features outperforms its SVM counterpart that uses only word features. Furthermore, comparable classification performance can be achieved by training and testing on posts from different forum topics.