ExSMuV: [Ex]ploration software for [S]ummarized [Mu]ltimedia [V]ertical search results

The proliferation of online multimedia content has transformed user information-seeking behavior from lookup to exploratory search. Existing web search engines present search results in disjoint, linearly ranked search result lists called verticals to bridge the information-exploration gap. However, search results presented by vertical search engines require extensive cognitive effort, hindering users’ ability to explore relevant content across verticals. We propose ExSMuV: [Ex]ploration Software for [S]ummarized [Mu]ltimedia [V]ertical Search Results, a framework that aggregates search results across verticals into coherent multimedia documents based on the most prominent topics, using a customized frequent-term scoring algorithm. Based on the identified important topics, a cosine similarity measure is used to aggregate the top-k similar results across verticals into a multimedia document. These documents combine conceptually similar web, image, and video search results into a comprehensive, unified Search User Interface (SUI) to reduce user navigation effort and improve exploration of relevant search results. We conducted a cognitive user study (N=23) comparing ExSMuV with a Bing vertical search baseline. The proposed framework enabled participants to perform exploratory search tasks with +37 % processing speed, +34 % selective attention, and +41 % better working memory compared to the baseline with statistically significant results (p ≤ 0.01).