Various data mining techniques to extract product relations have been examined, especially in the context of building intelligent recommender systems. Most such techniques, however, specifically examine co-occurrences of browsed or purchased products on e-commerce websites, which provide little or no useful information related to the direct relation of superiority or the factor which forms that superiority. For marketers and product managers, understanding the competitive advantages of a given product is important to consolidate their product differentiation strategies.

Online controlled experiments are widely used to improve the performance of websites by comparison of user behavior related to different variations of the given website. Although such experiments might have an important effect on the key metrics to maximize, small-scale websites have difficulty applying this methodology because they have few users. Furthermore, the candidate variations increase exponentially with the number of elements that must be optimized. A testing method that finds a high-performing variation with a few samples must be devised to address these problems. As described herein, we formalize this problem as a website optimization problem and provide a basis to apply existing search algorithms to this problem. We further organize existing testing methods and extract devices to make the experiments more effective. By combining organized algorithms and devices, we propose a rapid testing method that detects high-performing variations with few users. We evaluated our proposed method using simulation experiments. Results show that it outperforms existing methods at any website scale. Moreover, we implemented our proposed method as an optimizer program and used it on an actual small-scale website. Results show that our proposed method can achieve 57% higher performance variation than that of the generally used A/B testing method. Therefore, our proposed method can optimize a website with fewer samples. The website optimization problem has broad application possibilities that are applicable not only to websites but also to manufactured goods.

We propose Collaborative Exploratory Search (CES), which is an integration of dialog analysis and web search that involves multiparty collaboration to accomplish an exploratory information retrieval goal. Given a real-time dialog between users on a single topic; we define CES as the task of automatically detecting the topic of the dialog and retrieving task-relevant web pages to support the dialog. To recognize the task of the dialog, we apply the Author--Topic model as a topic model. Then, attribute extraction is applied to the dialog to obtain the attributes of the tasks. Finally, a specific search query is generated to identify the task-relevant information. We implement and evaluate the CES system for a commercial in-vehicle conversation. We also develop an iPad application that listens to conversations among users and continuously retrieves relevant web pages. Our experimental results reveal that the proposed method outperforms existing methods, which demonstrates the potential usefulness of collaborative exploratory search with practically usable accuracy levels.