Improving search personalisation with dynamic group formation

Recent research has shown that the performance of search engines can be improved by enriching a user's personal profile with information about other users with shared interests. In the existing approaches, groups of similar users are often statically determined, e.g., based on the common documents that users clicked. However, these static grouping methods are query-independent and neglect the fact that users in a group may have different interests with respect to different topics. In this paper, we argue that common interest groups should be dynamically constructed in response to the user's input query. We propose a personalisation framework in which a user profile is enriched using information from other users dynamically grouped with respect to an input query. The experimental results on query logs from a major commercial web search engine demonstrate that our framework improves the performance of the web search engine and also achieves better performance than the static grouping method

Unified Implicit and Explicit Feedback for Multi-Application User Interest Modeling

A user often interacts with multiple applications while working on a task. User models can be developed individually at each of the individual applications, but there is no easy way to come up with a more complete user model based on the distributed activity of the user. To address this issue, this research studies the importance of combining various implicit and explicit relevance feedback indicators in a multi-application environment. It allows different applications used for different purposes by the user to contribute user activity and its context to mutually support users with unified relevance feedback. Using the data collected by the web browser, Microsoft Word and Microsoft PowerPoint, Adobe Acrobat Writer and VKB, combinations of implicit relevance feedback with semi-explicit relevance feedback were analyzed and compared with explicit user ratings. Our past research show that multi-application interest models based on implicit feedback theoretically out performed single application interest models based on implicit feedback. Also in practice, a multi-application interest model based on semi-explicit feedback increased user attention to high-value documents. In the current dissertation study, we have incorporated topic modeling to represent interest in user models for textual content and compared similarity measures for improved recall and precision based on the text content. We also learned the relative value of features from content consumption applications and content production applications. Our experimental results show that incorporating implicit feedback in page-level user interest estimation resulted in significant improvements over the baseline models. Furthermore, incorporating semi-explicit content (e.g. annotated text) with the authored text is effective in identifying segment-level relevant content. We have evaluated the effectiveness of the recommendation support from both semi-explicit model (authored/annotated text) and unified model (implicit + semi-explicit) and have found that they are successful in allowing users to locate the content easily because the relevant details are selectively highlighted and recommended documents and passages within documents based on the user’s indicated interest. Our recommendations based on the semi-explicit feedback were viewed the same as those from unified feedback and recommendations based on semi-explicit feedback outperformed those from unified feedback in terms of matching post-task document assessments

Designing a Wise Home: Leveraging Lightweight Dialogue, Proactive Coaching, Guided Experimentation and Mutual-learning to Support Mixed-initiative Homes --Comfort-aware Thermostats as a Case

Science fiction writers have been dreaming of homes that can understand our preferences, assist our daily chores and teach us to be healthier, more sustainable and more knowledgeable. While we are still far from achieving this dream, the recent development of mobile devices, wearable interfaces, smart home appliances, and machine learning offer unprecedented opportunities for homes to better understand our goals, preferences and contexts, as well as to facilitate our everyday tasks and decision making. These recent advancement open a new possibility to create homes beyond simple automation: they enable creation of homes to coach us to achieve our better selves. That is, homes that are not just smart, but wise as well. However, to develop a wise home, there are still two key questions: How can a wise home coach its occupants while considering their different goals and needs? How can a home integrates emerging sensors, devices and interfaces to better understand their goals, preferences and contexts in order to support coaching? To answer these two questions, in this dissertation I use residential heating and cooling control as a lens to advance the development of wiser homes. Based on the three studies conducted, this thesis provides three contributions. First, I show that we can integrate a diverse class of emerging devices, including mobile phones, smartwatches, in-home sensors and home appliances to capture important user contexts, such as individual preferences for thermal comfort. The integration of these emerging devices enables a home to better coach its occupants and potentially better support automation. Secondly, I show that mixed-initiative interaction is an effective approach in the design of a wise home, and further propose four design strategies to support mixed-initiative homes, namely, lightweight dialogue, proactive coaching, guided experimentation and mutual-learning. Finally, I demonstrate a novel system that integrates the above-mentioned strategies to support the development of wise homes, facilitating home occupants to identify actions to achieve a better balance between their comfort and savings goals.PHDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153457/1/chuanche_1.pd