Model User Information Research Articles

ACM UMAP 2022 report

ACM UMAP is the premier international conference for researchers and practitioners working on systems that adapt to individual users, to groups of users, and that collect, represent, and model user information. For the first time in the history of the conference, UMAP 2022 ran in a hybrid mode, with the opportunity for speakers and attendees to participate onsite or online. Besides, in this edition, we celebrated the 30th anniversary of UMAP, which started as a workshop on User Modeling in 1986. In this report we comment on the conference organization and attendees' experience.

Modeling user information needs on mobile devices

Recent advances in the development of mobile devices equipped with multiple sensors, together with the availability of millions of applications, have made these devices more pervasive in our lives than ever. The availability of the diverse set of sensors, as well as high computational power, enable information retrieval (IR) systems to sense a user's context, and personalize their results accordingly. Relevant studies show that people use their mobile devices to access information in a wide range of topics in various contextual situations, highlighting the fact that modeling user information need on mobile devices involves studying several means of information access. In this thesis, we study three significant aspects of information access on mobile devices. First, we focus on proactive approaches to modeling users for venue suggestion. We investigate three methods of user modeling, namely, content-based, collaborative, and hybrid, focusing on personalization and context-awareness. Our content-based model is based on multiple relevance scores derived from multi-modal information that we collected from multiple sources of information [1]. We further propose a two-phase collaborative ranking algorithm for leveraging users' implicit feedback while incorporating temporal and geographical information into the model [3]. We then extend our collaborative model to include multiple cross-venue similarity scores and combine it with our content-based approach to produce a hybrid recommendation [2]. Second, we introduce and investigate a new task on mobile search, that is, unified mobile search. We take the first step in defining, studying, and modeling this task by collecting two datasets [5, 4]. Our analyses show that a limited number of popular apps attract most of the search queries. We further observe notable differences between queries submitted to different apps. We show that query length and content differ among apps. We also show that 39% of search queries were done in Google Search, and it was the top choice of users in 35% of the tasks. Given that more than 71% of the defined tasks could be done with the current features of Google Search, this indicates that users prefer to search using a more specific app. Also, we conduct experiments on one of the main components of unified mobile search frameworks, which is target apps selection. To this end, we propose two neural approaches that learn high-dimensional app representations based on their respective queries. Finally, we address the conversational aspect of mobile search , where we propose an offline evaluation protocol and build a dataset for asking clarifying questions for conversational search [6]. Also, we propose a retrieval framework consisting of three main components: question retrieval, question selection, and document retrieval. The experiments and analyses indicate that asking clarifying questions should be an essential part of a conversational system, resulting in a high performance gain. In particular, experiments on the oracle model demonstrated that asking only one good clarifying question leads to over 150% relative improvement in terms of P@1 and nDCG@1. Moreover, we observed that asking clarifying questions improves the model's performance for a substantial percentage of the facets, even though a more effective retrieval model than the one we used could potentially improve the performance.

ACM UMAP 2018 - User Modeling, Adaptation and Personalization

UMAP (User Modeling, Adaptation and Personalization) is the premier international conference for researchers and practitioners working on systems that adapt to individual users, to groups of users, and that collect, represent, and model user information. UMAP is the successor to the biennial User Modeling (UM) and Adaptive Hypermedia and Adaptive Web-based Systems (AH) conferences that were merged in 2009. It is sponsored by ACM SIGCHI and SIGWEB, and organized under the auspices of User Modeling Inc. The proceedings are published by ACM and will be part of the ACM Digital Library.

ACM UMAP 2017 - User Modeling, Adaptation and Personalization

UMAP (User Modeling, Adaptation and Personalization) is the premier international conference for researchers and practitioners working on systems that adapt to individual users, to groups of users, and that collect, represent, and model user information. UMAP is the successor to the biennial User Modeling (UM) and Adaptive Hypermedia and Adaptive Web-based Systems (AH) conferences that were merged in 2009. It is sponsored by ACM SIGCHI and SIGWEB, and organized under the auspices of User Modeling Inc. The proceedings are published by ACM and will be part of the ACM Digital Library.

Fuzzy rule based profiling approach for enterprise information seeking and retrieval

With the exponential growth of information available on the Internet and various organisational intranets there is a need for profile based information seeking and retrieval (IS&R) systems. These systems should be able to support users with their context-aware information needs. This paper presents a new approach for enterprise IS&R systems using fuzzy logic to develop task, user and document profiles to model user information seeking behaviour. Relevance feedback was captured from real users engaged in IS&R tasks. The feedback was used to develop a linear regression model for predicting document relevancy based on implicit relevance indicators. Fuzzy relevance profiles were created using Term Frequency and Inverse Document Frequency (TF-IDF) analysis for the successful user queries. Fuzzy rule based summarisation was used to integrate the three profiles into a unified index reflecting the semantic weight of the query terms related to the task, user and document. The unified index was used to select the most relevant documents and experts related to the query topic. The overall performance of the system was evaluated based on standard precision and recall metrics which show significant improvements in retrieving relevant documents in response to user queries.

Improved Method for Pattern Discovery in Text Mining

The proposed technique uses two processes, pattern deploying and pattern evolving, to refine the discovered patterns in text documents. The experimental results show that the proposed model outperforms not only other pure data mining-based methods and the concept based model, but also term-based state-of-the-art models, such as BM25 and SVM-based models. PBTM firstly generates pattern based topic representations to model user's information interests with multiple topics; then PBTM selects quality patterns for estimating the relevance of documents. The proposed approach incorporates the semantic topics from topic modeling and the specificity of the representative patterns. The proposed model has been evaluated by using RCV1 and TREC topics for the task of information filtering. Comparing with the state-of-the-art models, PBTM demonstrates excellent strength on document modeling and relevance ranking. Keywords: Pattern mining, Text mining, Text classification

Assisting the searcher: utilizing software agents for Web search systems

Much previous research on improving information retrieval applications has focused on developing entirely new systems with advanced searching features. Unfortunately, most users seldom utilize these advanced features. This research explores the use of a software agent that assists the user during the search process. The agent was developed as a separate, stand‐alone component to be integrated with existing information retrieval systems. The performance of an information retrieval system with the integrated agent was subjected to an evaluation with 30 test subjects. The results indicate that agents developed using both results from previous user studies and rapidly modeling user information needs can result in an improvement in precision. Implications for information retrieval system design and directions for future research are outlined.

An architecture for developing attentive information systems

Attentive systems attend to what users do so that they can attend to what users need. Such systems track user behavior, model user interests, and anticipate user desires and actions. Because the general class of attentive systems is broad — ranging from human butlers to web sites that profile users — we have focused specifically on attentive information systems, which observe user actions with information resources, model user information states, and suggest information that might be helpful to users. In particular, we describe Simple User Interest Tracker (Suitor), an architecture for developing attentive information systems that track computer users through multiple channels — eye gaze, web browsing, application use, to determine interests and to try to satisfy information needs. By observing behavior and modeling users, Suitor can be used to find and display potentially relevant information that is both timely and non-disruptive to the user's ongoing activities.