Alleviating Information Cocoons and Fatigue with Serendipity: Effect of Relevant Diversification and its Timing

With the rapid development of online media, in which personalized recommendations are provided, users are gaining increasingly narrow access to information, trapping them in so-called “information cocoons.” At the same time, the increase in homogenized content has brought boredom and fatigue, which are not conducive to the long-term interests of a platform. Grounded in the entertainment consumption context, as represented by the Tik Tok short video platform, this study focuses on the information cocoon reinforcement and browsing fatigue phenomena caused by the lack of proper diversification. Then, to mitigate these issues, this paper proposes relevant diversified content and diversification timing countermeasures to optimize the “what” and “when” technical designs. We explore the role of perceived serendipity as a key path toward user diversity acceptance and browsing duration, thus alleviating the phenomenon of information cocoons and browsing fatigue and facilitating the common development of platforms and users

Interactive Intent Modeling for Exploratory Search

Exploratory search requires the system to assist the user in comprehending the information space and expressing evolving search intents for iterative exploration and retrieval of information. We introduce interactive intent modeling, a technique that models a user’s evolving search intents and visualizes them as keywords for interaction. The user can provide feedback on the keywords, from which the system learns and visualizes an improved intent estimate and retrieves information. We report experiments comparing variants of a system implementing interactive intent modeling to a control system. Data comprising search logs, interaction logs, essay answers, and questionnaires indicate significant improvements in task performance, information retrieval performance over the session, information comprehension performance, and user experience. The improvements in retrieval effectiveness can be attributed to the intent modeling and the effect on users’ task performance, breadth of information comprehension, and user experience are shown to be dependent on a richer visualization. Our results demonstrate the utility of combining interactive modeling of search intentions with interactive visualization of the models that can benefit both directing the exploratory search process and making sense of the information space. Our findings can help design personalized systems that support exploratory information seeking and discovery of novel information.Peer reviewe

Resource Discovery Tools: Supporting Serendipity

Serendipity, the accidental discovery of something useful, plays an important role in discovery and the acquisition of new knowledge. The process and role of serendipity varies across disciplines. As library collections have become increasingly digital faculty lament the loss of serendipity of browsing library stacks. Resource discovery tools may have features that support serendipity as part of information seeking. A comparison of four commercial Web-scale discovery tools, Online Computer Library Center (OCLC) WorldCat® Local1, Serials Solution2® Summon3™, ExLibris4® Primo Central5™, and EBSCO Discovery Services (EDS)6™, links product features to characteristics that support serendipitous discovery. However, having such features is only part of the equation. Educators need to include serendipity in discussions about the research process. Future research opportunities include determining whether serendipity can be encouraged, evaluating its occurrence in the web scale environment, and studying serendipity in relation to research instruction

Effective Retrieval of Resources in Folksonomies Using a New Tag Similarity Measure

Social (or folksonomic) tagging has become a very popular way to describe content within Web 2.0 websites. However, as tags are informally defined, continually changing, and ungoverned, it has often been criticised for lowering, rather than increasing, the efficiency of searching. To address this issue, a variety of approaches have been proposed that recommend users what tags to use, both when labeling and when looking for resources. These techniques work well in dense folksonomies, but they fail to do so when tag usage exhibits a power law distribution, as it often happens in real-life folksonomies. To tackle this issue, we propose an approach that induces the creation of a dense folksonomy, in a fully automatic and transparent way: when users label resources, an innovative tag similarity metric is deployed, so to enrich the chosen tag set with related tags already present in the folksonomy. The proposed metric, which represents the core of our approach, is based on the mutual reinforcement principle. Our experimental evaluation proves that the accuracy and coverage of searches guaranteed by our metric are higher than those achieved by applying classical metrics.Comment: 6 pages, 2 figures, CIKM 2011: 20th ACM Conference on Information and Knowledge Managemen

Intent-Aware Contextual Recommendation System

Recommender systems take inputs from user history, use an internal ranking algorithm to generate results and possibly optimize this ranking based on feedback. However, often the recommender system is unaware of the actual intent of the user and simply provides recommendations dynamically without properly understanding the thought process of the user. An intelligent recommender system is not only useful for the user but also for businesses which want to learn the tendencies of their users. Finding out tendencies or intents of a user is a difficult problem to solve. Keeping this in mind, we sought out to create an intelligent system which will keep track of the user's activity on a web-application as well as determine the intent of the user in each session. We devised a way to encode the user's activity through the sessions. Then, we have represented the information seen by the user in a high dimensional format which is reduced to lower dimensions using tensor factorization techniques. The aspect of intent awareness (or scoring) is dealt with at this stage. Finally, combining the user activity data with the contextual information gives the recommendation score. The final recommendations are then ranked using filtering and collaborative recommendation techniques to show the top-k recommendations to the user. A provision for feedback is also envisioned in the current system which informs the model to update the various weights in the recommender system. Our overall model aims to combine both frequency-based and context-based recommendation systems and quantify the intent of a user to provide better recommendations. We ran experiments on real-world timestamped user activity data, in the setting of recommending reports to the users of a business analytics tool and the results are better than the baselines. We also tuned certain aspects of our model to arrive at optimized results.Comment: Presented at the 5th International Workshop on Data Science and Big Data Analytics (DSBDA), 17th IEEE International Conference on Data Mining (ICDM) 2017; 8 pages; 4 figures; Due to the limitation "The abstract field cannot be longer than 1,920 characters," the abstract appearing here is slightly shorter than the one in the PDF fil