Behavior Driven Topic Transition for Search Task Identification
| Autor: | Hongyuan Zha, Yi Chang, Anlei Dong, Yunlong He, Hongbo Deng, Liangda Li |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Incremental heuristic search
Web search query Computer science business.industry Search analytics Semantic search 02 engineering and technology Phrase search Machine learning computer.software_genre Search engine Web query classification 020204 information systems 0202 electrical engineering electronic engineering information engineering Beam search 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | WWW |
| DOI: | 10.1145/2872427.2883047 |
| Popis: | Search tasks in users' query sequences are dynamic and interconnected. The formulation of search tasks can be influenced by multiple latent factors such as user characteristics, product features and search interactions, which makes search task identification a challenging problem. In this paper, we propose an unsupervised approach to identify search tasks via topic membership along with topic transition probabilities, thus it becomes possible to interpret how user's search intent emerges and evolves over time. Moreover, a novel hidden semi-Markov model is introduced to model topic transitions by considering not only the semantic information of queries but also the latent search factors originated from user search behaviors. A variational inference algorithm is developed to identify remarkable search behavior patterns, typical topic transition tracks, and the topic membership of each query from query logs. The learned topic transition tracks and the inferred topic memberships enable us to identify both small search tasks, where a user searches the same topic, and big search tasks, where a user searches a series of related topics. We extensively evaluate the proposed approach and compare with several state-of-the-art search task identification methods on both synthetic and real-world query log data, and experimental results illustrate the effectiveness of our proposed model. |
| Databáze: | OpenAIRE |
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