User Intent in Multimedia Search

Author:

Kofler Christoph1,Larson Martha2,Hanjalic Alan2

Affiliation:

1. Bloomberg L.P., New York, NY

2. Delft University of Technology, Delft, The Netherlands

Abstract

Today's multimedia search engines are expected to respond to queries reflecting a wide variety of information needs from users with different goals. The topical dimension (“what” the user is searching for) of these information needs is well studied; however, the intent dimension (“why” the user is searching) has received relatively less attention. Specifically, intent is the “immediate reason, purpose, or goal” that motivates a user to query a search engine. We present a thorough survey of multimedia information retrieval research directed at the problem of enabling search engines to respond to user intent. The survey begins by defining intent, including a differentiation from related, often-confused concepts. It then presents the key conceptual models of search intent. The core is an overview of intent-aware approaches that operate at each stage of the multimedia search engine pipeline (i.e., indexing, query processing, ranking). We discuss intent in conventional text-based search wherever it provides insight into multimedia search intent or intent-aware approaches. Finally, we identify and discuss the most important future challenges for intent-aware multimedia search engines. Facing these challenges will allow multimedia information retrieval to recognize and respond to user intent and, as a result, fully satisfy the information needs of users.

Funder

Dutch national program COMMIT/

Google Europe Doctoral Fellowship in Video Search

Google Fellowship

Publisher

Association for Computing Machinery (ACM)

Subject

General Computer Science,Theoretical Computer Science

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1. The Discontent with Intent Estimation In-the-Wild: The Case for Unrealized Intentions;Extended Abstracts of the CHI Conference on Human Factors in Computing Systems;2024-05-11

2. End-to-end pseudo relevance feedback based vertical web search queries recommendation;Multimedia Tools and Applications;2024-02-21

3. Analyzing Social Exchange Motives With Theory-Driven Data and Machine Learning;IEEE Access;2024

4. A Machine Learning Approach to User Profiling for Data Annotation of Online Behavior;Computers, Materials & Continua;2024

5. An Intent Taxonomy of Legal Case Retrieval;ACM Transactions on Information Systems;2023-12-11

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