Intent-Aware Video Search Result Optimization
|Title||Intent-Aware Video Search Result Optimization|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Kofler, C, Larson, MA, Hanjalic, A|
|Journal||Multimedia, IEEE Transactions on|
|Keywords||crowdsourcing platform, Feature Extraction, Google, information needs, intent distribution pattern, intent-aware video search result optimization, intent-sensitive lightweight visual feature extraction, Materials, Measurement, multimodal approach, optimisation, Optimization, Result optimization, results list refinement, search engines, system-oriented evaluation, user information need, user intent, user intent capture, video retrieval, video search, video search engines, visual reranking, Visualization|
Video search engines are relatively successful at returning search results that users find to be on topic. These results do not, however, completely satisfy the user's information need unless they also fulfill the user's intent, i.e., the immediate goal a user seeks to accomplish with video search. Satisfying a user's information need to its full extent poses a particular challenge to video search engines because user intent is often not explicitly reflected in the query. In this paper, we propose a multimodal approach that addresses this challenge by refining the results lists returned by a mainstream video search engine in order to optimally capture user intent. Our approach is based on the insight that the results lists returned by video search engines do contain videos that satisfy user's intent, but that videos with the highest potential for satisfaction are often buried within or scattered over the results list. The proposed approach consists of three steps. In the first step, it analyzes the initial results list to determine the intent distribution pattern. On the basis of this pattern, in the second step, it refines the video search results list such that the top of the list better reveals intent. The third step further improves this refinement by visual reranking, exploiting intent-sensitive lightweight visual features extracted from thumbnails. Extensive evaluation of the approach includes a user study carried out on a crowdsourcing platform and a system-oriented evaluation. Evaluation results demonstrate that our approach leads to a substantial improvement of the information need satisfaction at users.